Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation

ABSTRACT

The inherently slow acquisition speed of MRI makes abdominal imaging highly sensitive to respiratory motion artifacts. Since the early days of MRI, the development of respiratory motion compensation techniques has been an active research topic, and this field has seen substantial progress. Despite these advances, the majority of these techniques are not used in daily clinical practice, and motion management methods used in clinical abdominal MRI today have changed little over the past decades. This observation points to a significant gap between technical innovation and clinical translation in this area. This review is motivated by this question: why have so many motion management techniques not been adopted into routine clinical workflows? Unlike conventional survey‐style reviews that focus on summarizing emerging methods, this article takes a different, and perhaps opposite, perspective to investigate why those technologically sophisticated innovations are misaligned with practical clinical needs. Specifically, we discuss the barriers behind the gap between research advances and clinical practice, clarify the clinical requirements for effective respiratory motion management in abdominal MRI, and highlight research directions with stronger relevance to routine workflows. The review begins with an overview of the clinical impact of respiratory motion in abdominal MRI, followed by a discussion of standard abdominal MRI sequences and their motion sensitivity. We then summarize current clinical strategies and advanced approaches, along with the barriers that hinder their clinical adoption. The article concludes with future directions and broader lessons learned from this translational gap, with the goal of guiding future developments toward improved clinical integration.

1. Introduction

The sequential nature of data acquisition in MRI leads to relatively slow imaging speed, which in turn increases the sensitivity to motion artifacts. Respiratory motion therefore becomes a major source of image degradation in body MRI, particularly for abdominal organs [1, 2, 3]. To mitigate motion artifacts, routine abdominal MRI in current clinical practice typically requires a series of breath‐holds, with each lasting ∼10–15 s.

A practical challenge of breath‐hold imaging is that many patients, especially pediatric, elderly, or sick patients, have limited capability to hold even short breath‐holds. When breath‐holds fail, technologists may need to repeat acquisitions until diagnostic image quality is achieved, or switch to alternative motion mitigation strategies that often require additional scan time. In applications such as dynamic contrast‐enhanced MRI (DCE‐MRI) where contrast injection is usually performed only once, failed breath‐holding during a rapid enhancement phase (e.g., the arterial phase) could result in motion‐corrupted images that cannot be reacquired. In such cases, radiologists may have to interpret images with motion artifacts.

These limitations have motivated substantial efforts since the early days of MRI to develop approaches that allow patients to breathe normally in abdominal MRI [1, 2, 3]. For example, a PubMed search using the keyword “free‐breathing MRI” shows a rapid growth in related publications over the past decades, which reflects continued interest in this area. Many studies have indeed demonstrated promising solutions in controlled settings and proof‐of‐concept experiments. However, despite decades of technical innovation, routine abdominal MRI protocols have seen little change in motion management over the past two decades, and breath‐holding remains the dominant strategy for avoiding respiratory motion artifacts. This creates a “valley of death” that reflects a significant gap between the technical advances in respiratory motion management and their actual clinical impact.

This review is motivated by a central question: why have so many motion management techniques not been adopted into routine clinical workflows? Unlike conventional survey‐style reviews that primarily summarize emerging techniques, this article takes a different, and perhaps opposite, perspective to investigate why many technologically sophisticated innovations cannot be used in daily clinical practice and are misaligned with practical needs. Specifically, we discuss the barriers behind the gap between research advances and routine clinical practice, clarify the clinical requirements for effective respiratory motion management in abdominal MRI, and highlight research directions with stronger clinical relevance in routine workflows.

This article begins with an overview of the clinical impact of respiratory motion in abdominal MRI, followed by a description of standard MRI sequences used in routine abdominal imaging and an analysis of their motion sensitivity. These sections first outline the practical problems that motion management techniques should address. We then summarize the motion management approaches used in current clinical practice and describe advanced strategies with translational potential, along with barriers that currently hinder their clinical adoption. The review concludes with a discussion of future directions in this area and broader lessons learned from the translational gap in respiratory motion management, with the goal of guiding future developments toward improved clinical integration.

2. Challenges of Respiratory Motion in the Abdomen

Respiratory motion in the abdomen mainly arises from diaphragm displacement during breathing [4, 5, 6]. During inspiration, diaphragm contraction moves different abdominal organs downward by several centimeters, and these organs move upward during expiration. Importantly, respiratory‐induced displacement is not spatially uniform, and different organs can deform to different extents during a respiratory cycle [6]. Respiratory motion occurs along both the superior–inferior (head‐to‐foot) and anterior–posterior directions, and displacement is typically much larger in the superior–inferior direction.

Another important feature of respiratory motion is the substantial variability in breathing patterns, both between patients and across respiratory cycles within the same patient, and baseline drift often occurs during MRI exams. These factors together make respiratory motion often irregular and non‐periodic, with spatial variation even within the same organ [6]. As a result, rigid motion models are often insufficient to account for the full complexity of abdominal motion, and this increases the difficulty of motion management in abdominal MRI and limits the robustness and generalizability of many motion compensation techniques.

3. Standard Abdominal MRI Sequences

To understand the challenges of respiratory motion management, it is important to first review the sequences routinely used in abdominal MRI, as each of them is affected by respiratory motion in different ways and no single motion management technique can serve as a universal solution. While clinical protocols vary, most routine abdominal MRI sequences can be grouped into three major categories: (1) T2‐weighted imaging using 2D or 3D fast spin‐echo (FSE), (2) T1‐weighted imaging using single‐echo or multi‐echo gradient‐echo (GRE), and (3) 2D echo planar imaging (EPI). This section summarizes these standard clinical sequences followed by a sequence‐specific discussion of their motion sensitivity.

3.1. T2‐Weighted Imaging

Three major types of T2‐weighted sequence are commonly used in clinical abdominal MRI. The first one is 2D single‐shot FSE (SSFSE), which acquires each slice within a single long echo train. SSFSE is typically implemented with partial Fourier acquisition to shorten the effective echo train length, reduce scan time, and mitigate T2‐related spatial blurring [7]. SSFSE is widely used for rapid assessment of abdominal structures and for applications requiring strong T2 weighting, such as pancreatic imaging and fluid‐rich tissues. Because each slice is acquired in a single echo train (∼500–800 ms), SSFSE is relatively tolerant of intraframe respiratory motion and can be performed during free‐breathing or breath‐holding depending on clinical needs. Breath‐holding is often preferred, as it gives consistent organ boundaries across slices (Figure 1), which is particularly important for imaging small structures such as the pancreas. Breath‐hold SSFSE imaging is commonly divided into multiple breath‐holds (concatenations) to cover the full imaging volume, with each concatenation acquiring a subset of slices [8]. However, because different slice groups correspond to different respiratory states due to variations in breath‐hold position, mild slice‐to‐slice inconsistency and occasionally overlap or gaps in anatomical coverage can still occur (see Figure 1). Despite its motion robustness, SSFSE is limited by spatial blurring due to long echo trains, which in turn limits its achievable spatial resolution.

FIGURE 1.

Comparison of 2D SSFSE acquisitions during breath‐hold and free‐breathing. Breath‐hold acquisition shows relatively consistent slice positions, while free‐breathing acquisition results in respiratory‐induced inconsistency across slices as indicated by the green dashed line.

The second type is multi‐shot 2D FSE, which acquires each slice over multiple echo trains (also referred to as shots). Compared to SSFSE, multi‐shot 2D FSE enables higher spatial resolution and reduced T2 blurring at the cost of longer scan time. Because the final image is generated by combining data from multiple shots, a consistent respiratory state across shots is important. Therefore, multi‐shot 2D FSE typically requires reliable breath‐holding or other effective respiratory management strategies (see Section 4). As with SSFSE, data acquisition in multi‐shot 2D FSE can be divided into multiple concatenations to shorten each breath‐hold, which may result in mild slice‐to‐slice inconsistency and occasionally overlap or gaps in anatomical coverage due to variations in breath‐hold position [8, 9].

The third type is 3D FSE, which is most commonly used for MR cholangiopancreatography (MRCP) in clinical practice [10, 11]. This sequence allows extended echo trains to achieve strong T2‐weighting for imaging fluid‐filled structures such as the biliary and pancreatic ducts. The main advantage of 3D FSE is the nearly isotropic resolution, which allows for multiplanar reformation for improved visualization of the small ductal anatomy. However, 3D FSE requires much longer scan time than 2D FSE and is therefore highly sensitive to respiratory motion, making effective motion compensation essential for routine clinical use.

3.2. T1‐Weighted Imaging

T1‐weighted imaging in abdominal MRI is typically performed using single‐echo or multi‐echo GRE sequences for four major applications. First, standard fat‐suppressed, single‐echo 3D GRE is used for pre‐ and post‐contrast imaging, where images are acquired before and following the administration of a gadolinium‐based contrast agent at multiple enhancement phases, typically over several breath‐holds. This multi‐phase acquisition, also referred to as DCE‐MRI, is a cornerstone of routine abdominal MRI for detection, delineation, and characterization of focal lesions [12].

Second, dual‐echo 3D GRE is used for in‐phase and out‐of‐phase images at two echo times where fat and water signals add or cancel. This sequence is typically acquired in a single breath‐hold and is widely used for tissue characterization, particularly for identifying microscopic fat in the liver [13, 14].

Third, multi‐echo 3D GRE is used for chemical‐shift‐encoded imaging. This sequence typically acquires six echoes to enable robust estimation of proton‐density fat fraction (PDFF) and R2*, which are routinely used for assessment of hepatic steatosis and iron overload, respectively [15, 16, 17, 18, 19]. To keep the acquisition within a single breath‐hold, multi‐echo 3D GRE is often performed at lower spatial resolution than single‐echo 3D GRE because of its longer TR. Despite this, it is clinically valuable for assessing diffuse liver disease such as fatty liver disease, where high spatial resolution is often less critical. Recent studies suggest that switching from 3D multi‐echo GRE to 2D multi‐echo GRE may provide additional advantages [20, 21], which will be discussed in Section 5.

Finally, MR elastography (MRE) is also commonly implemented using 2D GRE‐based acquisitions in clinical practice [22, 23], where motion‐encoding gradients are incorporated into the sequence to encode tissue displacement caused by propagating shear waves for stiffness estimation.

3.3. Echo‐Planar Imaging

Echo‐planar imaging (EPI) is a rapid imaging sequence widely used in abdominal MRI. It is most commonly implemented as a 2D spin‐echo‐based sequence (SE‐EPI) and is routinely used for diffusion‐weighted imaging (DWI) [24, 25, 26], in which diffusion encoding applies bipolar gradients to sensitize the signal to microscopic water motion, suppressing signal from tissues with relatively unrestricted diffusion while preserving signal in tissues with restricted diffusion (e.g., tumors or inflammation). Because diffusion encoding increases sequence duration, routine abdominal DWI is usually performed with single‐shot EPI (ss‐EPI) to maximize efficiency. Similar to SSFSE, ss‐EPI acquires each slice within a single long readout (EPI echo train, ∼50–150 ms), which makes each individual diffusion‐weighted image relatively tolerant of intraframe respiratory motion.

In practice, abdominal DWI requires multiple b‐values and diffusion directions, and multiple signal averages are also used to ensure adequate signal‐to‐noise ratio (SNR). These requirements make breath‐holding difficult, and therefore, abdominal DWI is often performed during free‐breathing at many institutions, although breath‐hold and respiratory‐triggered acquisitions are also considered in some settings [25, 27]. With free‐breathing DWI, individual diffusion‐weighted images may be relatively motion‐free because of its efficient data acquisition. However, images across slices, b‐values, diffusion directions, and averages may correspond to different respiratory states, which results in slice‐to‐slice inconsistency and inter‐image misalignment. This directly affects downstream processing, including combination of different diffusion directions and averages, as well as voxel‐wise estimation of apparent diffusion coefficient (ADC) maps [27, 28, 29, 30].

At present, there is no universally accepted guideline defining an optimal motion management strategy for abdominal DWI, and implementation varies across institutions. In this review, discussion of DWI motion sensitivity and motion management primarily reflects the widely used free‐breathing, multi‐average approach.

In addition, SE‐EPI is also used for MRE at many institutions [22, 23]. For SE‐EPI‐based MRE, breath‐hold acquisition is typically required, as stiffness estimation relies on images acquired at multiple phase offsets, and consistent motion conditions across these measurements are essential. MRE also deserves special consideration because both GRE‐ and SE‐EPI‐based implementations are available. GRE‐MRE remains widely used in clinical practice and formed the basis of many early clinical validation studies. Comparative studies have demonstrated good agreement between GRE‐ and SE‐EPI‐based liver stiffness measurements [31, 32]. These studies also reported several practical advantages of SE‐EPI‐based MRE, including improved robustness in patients with iron overload and reduced technical failure in certain clinical scenarios [31, 32].

3.4. Respiratory Motion Sensitivity of Routine Abdominal MRI Sequences

Respiratory motion affects abdominal MRI sequences in different ways depending on their acquisition strategies. A common feature of current default abdominal MRI protocols is that nearly all sequences use Cartesian sampling. Yes, ss‐EPI can also be considered Cartesian, although its effective trajectory may deviate from the ideal path and often requires correction. With Cartesian sampling, respiratory motion is typically manifested as ghosting and spatial blurring in 3D imaging and multi‐shot 2D imaging, and as slice‐to‐slice inconsistencies, inter‐image misalignment, and motion‐averaged blurring in free‐breathing single‐shot 2D imaging.

Motion sensitivity is substantially different between 2D and 3D acquisitions. All 3D Cartesian sequences are inherently sensitive to respiratory motion because a full 3D volume is acquired over multiple respiratory cycles. Therefore, even modest respiratory motion can introduce blurring and/or ghosting artifacts, with increasing severity for larger respiratory displacement, as shown in Figure 2. These challenges affect both 3D T1‐weighted GRE and 3D T2‐weighted FSE, making reliable breath‐holding or alternative respiratory management strategies essential. Respiratory motion can also cause fat/water separation errors in multi‐echo 3D GRE [33, 34], which may introduce bias in quantitative measurements. In particular, R2* estimation is highly sensitive to motion‐induced signal inconsistency across echoes [35, 36].

FIGURE 2.

Comparison of 3D GRE acquisitions during breath‐hold, normal breathing, and deep breathing. Respiratory motion leads to blurring and ghosting along the phase‐encoding directions, and larger respiratory displacement produces more severe artifacts.

Single‐shot 2D sequences, such as SSFSE and ss‐EPI, acquire each slice within a short window (e.g., within 1 s), which reduces intraframe motion sensitivity. However, during free‐breathing, slices are acquired sequentially over many breathing cycles, each at a different respiratory state. This results in slice‐to‐slice misalignment, as shown in Figure 1 for SSFSE imaging. In free‐breathing DWI, these inconsistencies are further complicated by variation of contrast and SNR across b‐values, diffusion directions, and averages, making inter‐image misalignment a major limitation even though individual ones may appear motion‐free, as shown in Figure 3. In addition to inter‐image misregistration across different individual diffusion‐weighted images, abdominal DWI is also intrinsically sensitive to respiratory motion during diffusion encoding. Strong diffusion‐sensitizing gradients make the signal highly sensitive to phase errors induced by both respiratory and cardiovascular motion, which can lead to signal dropout, ghosting, and quantification bias. Sequence‐level approaches such as gradient moment nulling (e.g., M1 and M2 compensation) have been proposed to mitigate this effect [37, 38, 39, 40] but require prolonged echo times and additional optimization, which have limited clinical adoption to date. A detailed discussion of diffusion‐encoding motion compensation is beyond the scope of this review, which focuses on respiratory motion management at the acquisition and reconstruction levels. A related consideration also applies to MRE, where consistent motion conditions across phase offsets are required to ensure accurate stiffness estimation, even though individual images may be relatively robust to intraframe motion.

FIGURE 3.

Effect of signal averaging on free‐breathing liver DWI (b = 50 s/mm²). Images are shown for a single average, 4 averages, and 8 averages. Increasing the number of averages improves signal‐to‐noise ratio but leads to spatial blurring due to respiratory motion during acquisition.

In contrast, multi‐shot 2D FSE is sensitive to both intraframe and inter‐slice motion. Motion across shots corrupts phase inconsistencies within a slice, while variations between slices introduce additional inconsistency. As a result, multi‐shot sequences typically require reliable breath‐holding or other effective respiratory compensation strategies. Failed breath‐hold can result in severe motion artifacts, as shown in Figure 4. The yellow arrows in this figure highlight that multi‐shot 2D FSE remains sensitive to respiratory motion even during breath‐hold acquisitions due to its longer TR between shots and increased sensitivity to inter‐shot inconsistencies.

FIGURE 4.

Comparison of breath‐hold and free‐breathing acquisitions in multi‐shot 2D FSE. Free‐breathing acquisitions result in blurring and ghosting artifacts that degrade image quality compared to breath‐hold acquisitions. Yellow arrows indicate that multi‐shot 2D FSE is sensitive to motion even during breath‐hold acquisitions due to the longer TR.

In summary, the nature of motion sensitivity varies across sequences. For 3D imaging and multi‐shot 2D imaging, motion compensation strategies should focus more on mitigating intraframe motion that causes blurring and/or ghosting. For single‐shot 2D imaging, particularly free‐breathing DWI, major limitations arise from slice‐to‐slice inconsistency and inter‐image misalignment, for which robust image alignment or registration‐based methods may be especially beneficial. For quantitative imaging such as R2* mapping and MRE, respiratory motion may also introduce bias by disrupting signal or phase consistency across measurements.

In addition to respiratory motion, other sources such as peristalsis and cardiac motion can also degrade image quality in abdominal MRI, even when respiratory motion is well controlled. These effects are often sequence‐ and application‐dependent. For example, in MRCP, peristaltic motion can lead to blurring despite effective respiratory compensation [41, 42]. In general, these motion sources are less predictable and more difficult to correct using conventional respiratory motion management strategies, which may limit image quality in certain clinical scenarios.

4. Classical Respiratory Motion Management Methods for Abdominal MRI: Clinical Utility and Limitations

This section summarizes the classical methods routinely used in clinical abdominal MRI for respiratory motion management, including breath‐holding, accelerated acquisition, respiratory triggering, motion‐averaged acquisition, and motion‐tolerant acquisition. It also discusses why these approaches continue to be used in clinical practice and highlights their limitations.

4.1. Breath‐Holding

Breath‐holding remains the most widely used and effective approach to avoid respiratory motion artifacts in clinical abdominal MRI. When patients are able to hold their breath, breath‐hold imaging is simple, efficient, and robust. However, a major limitation of breath‐hold imaging is its strong dependence on patient compliance, and performance can be reduced in patients with limited breath‐hold capacity.

The requirement for breath‐hold is different between 2D and 3D acquisitions. In 2D imaging, acquisition can be divided into multiple breath‐holds using the concatenation strategy described earlier. With this approach, only a subset of slices is acquired during each breath‐hold; therefore, reducing the breath‐hold duration required for each acquisition. The trade‐off is that the patient needs to perform more breath‐holds to cover the entire imaging volume.

In contrast, all k‐space samples contribute to a single reconstructed volume in 3D imaging. This is different from multi‐slice 2D acquisitions, where slices are acquired independently. As a result, a single, continuous breath‐hold is required to acquire the full 3D dataset, which often necessitates longer breath‐holding than 2D imaging. To make such breath‐holding feasible, spatial resolution may need to be sacrificed, particularly for sequences with long TRs such as multi‐echo 3D GRE.

4.2. Accelerated Acquisition

Accelerated acquisition may seem distinct from motion management, but it is in fact one of the most useful strategies for reducing motion artifacts [43, 44] and is used in almost every abdominal MRI exam. Fast imaging directly shortens the breath‐hold duration and makes it easier for patients to better cooperate with the exam. Even in free‐breathing scans, faster acquisition helps reduce total scan time and thus decrease the likelihood of respiratory drifts or cycle‐to‐cycle motion variability. Accelerated imaging can also shorten echo trains in SSFSE and ss‐EPI sequences, which in turn reduces T2/T2* blurring or geometric distortion. Early feasibility studies suggest that highly accelerated acquisition may allow fast 3D FSE MRCP within a single prolonged breath‐hold to achieve improved scan efficiency [45, 46, 47], although such approaches are not widely available in routine practice yet. In addition, increased imaging speed can also be traded for higher spatial resolution without prolonging scan time.

Common acceleration techniques in clinical abdominal MRI include partial Fourier imaging, parallel imaging, compressed sensing, simultaneous multi‐slice imaging, and more recently, deep learning‐based reconstruction [44]. All of these methods play an essential role in motion management by minimizing the acquisition window over which respiratory motion can affect the data.

A key limitation of accelerated acquisition is that it mitigates motion primarily by shortening the acquisition window rather than explicitly correcting motion, and its performance is therefore still affected by residual motion. In addition, higher acceleration factors may lead to reduced SNR, increased reconstruction artifacts, or reliance on more complex reconstruction algorithms, which can limit robustness for clinical implementation.

4.3. Respiratory Triggering With External or Internal Motion Signals

When patients cannot perform reliable breath‐holds, respiratory triggering provides an alternative motion management strategy [2, 48, 49]. Triggering can be implemented using external devices (e.g., respiratory bellows) or internal motion surrogates (e.g., image‐based navigators). During normal breathing, motion is monitored continuously and data are acquired only when the motion signal satisfies a predefined condition, commonly near end‐expiration. This increases consistency of respiratory phase in the acquired data, so motion artifacts can be effectively mitigated.

Despite its benefits, respiratory triggering has important limitations. The major drawback is reduced scan efficiency, since acquisition occurs during only a fraction of the respiratory cycle and the scanner remains idle for the rest. Scan duration may also become unpredictable due to variability in breathing patterns. In addition, triggering is generally not suitable for DCE‐MRI, where rapid imaging is required to capture contrast dynamics. In current clinical practice, respiratory triggering is most commonly used in 3D MRCP [50, 51], where its long scan time makes breath‐holding difficult. Triggering has also been shown to improve image quality in abdominal DWI [29, 30], but this is less preferred in clinical practice due to prolonged scan time [25].

4.4. Free‐Breathing Motion Averaged Acquisition

Motion‐averaged acquisition, which repeats data acquisition multiple times and then averages the resulting images, is another simple but clinically useful motion mitigation strategy when breath‐holding is not feasible [3, 52]. Because respiratory motion varies across repetitions, averaging effectively reduces structured motion artifacts and translates them into spatial blurring, which may be more clinically acceptable than pronounced ghosting. Figure 5 compares breath‐hold (left) with single‐ and five‐average free‐breathing acquisitions (middle, right) for 3D GRE and multi‐shot 2D FSE. Increased averaging reduces motion artifacts but leads to blurring and longer scan time.

FIGURE 5.

Effect of motion averaging during free‐breathing for multi‐shot 2D FSE and 3D GRE imaging. Increasing the number of averages reduces motion artifacts but introduces spatial blurring and prolongs acquisition time.

In general, motion‐averaged acquisition is more commonly used for multi‐shot 2D FSE than 3D GRE. This is because 3D GRE is commonly used for DCE‐MRI where rapid acquisition is required to capture contrast enhancement. As described above, motion‐averaged acquisition is routinely used for DWI, where the primary purpose of averaging is to improve SNR, as ss‐EPI is relatively tolerant of intraframe motion. Averaging across repeated acquisitions can also suppress incoherent (e.g., irregular) respiratory motion, often at the cost of motion‐induced blurring.

4.5. Motion‐Tolerant Acquisition

Non‐Cartesian sampling is known to be more tolerant of motion than Cartesian sampling, in part because repeated sampling of k‐space center introduces a motion‐averaging effect and spreads motion‐induced phase errors more broadly [53, 54, 55, 56, 57]. In clinical abdominal MRI, radial and PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) are the two most commonly used motion‐tolerant alternatives. Radial sampling, usually implemented using the stack‐of‐stars trajectory for 3D GRE [53, 54], has been extensively demonstrated as a motion‐tolerant option for patients who cannot hold breath‐holds in abdominal MRI, as shown in Figure 6a. PROPELLER FSE has long been a motion‐tolerant alternative to multi‐shot 2D Cartesian FSE. Like radial sampling, the rotating blade acquisition in PROPELLER inherently reduces in‐plane motion artifacts [56], as shown in Figure 6b.

FIGURE 6.

Comparison of Cartesian and non‐Cartesian (radial and PROPELLER) sampling strategies in free‐breathing T1‐ and T2‐weighted liver MRI. Although relatively robust to motion, free‐breathing non‐Cartesian acquisitions may introduce spatial blurring that degrades image quality.

Despite their advantages and increasing clinical adoption, both radial sampling and PROPELLER imaging have important limitations. First, they are motion‐tolerant rather than motion‐free. As shown in Figure 6, both radial sampling and PROPELLER sampling are prone to motion‐induced image blurring. Second, standard radial and PROPELLER sampling are less efficient than Cartesian acquisition and often require longer scan times to satisfy the Nyquist criterion unless advanced acceleration methods are used. Third, as shown in Figure 7, trajectory‐specific artifacts, such as streaking in radial imaging, remain a significant concern for routine use [54], and these artifacts make it less robust when applied in non‐axial orientations.

FIGURE 7.

Radial sampling (and non‐Cartesian sampling in general) is better suited for axial imaging, while coronal and sagittal imaging tend to exhibit more pronounced streaking artifacts with undersampling due to strong high‐intensity signal components near the periphery of the field of view. Figure was reproduced from figure 15 in Feng [54] with permission from the journal.

4.6. Choice of Breath‐Hold Versus Free‐Breathing in Clinical Abdominal MRI

In current clinical practice, the choice between breath‐holding and free‐breathing acquisitions depends primarily on patient conditions, sequence requirements, and institutional preference. In general, 3D FSE MRCP is typically performed during free‐breathing with respiratory triggering. While this prolongs scan time, MRCP is not performed in every abdominal MRI exam and is only needed for patients who require dedicated biliary evaluation. Abdominal DWI is often performed during free‐breathing, although breath‐hold and respiratory‐triggered acquisitions are also used at some institutions. 2D SSFSE may be performed during free‐breathing or over multiple breath‐holds depending on diagnostic needs, but breath‐hold acquisitions are generally more preferred due to consistent slice positions. Standard 3D Cartesian GRE is typically performed in a single breath‐hold, unless a motion‐tolerant alternative (such as stack‐of‐stars GRE) is used. For multi‐shot 2D FSE, increasing the number of concatenations is a practical strategy to shorten individual breath‐hold at the cost of additional breath‐holds and longer total scan time. PROPELLER‐based T2 FSE is usually reserved for patients who are unable to perform reliable breath‐holds.

In routine clinical practice today, breath‐hold remains the first‐line choice whenever feasible—except for DWI and 3D FSE—because it is fast, efficient, and highly effective in minimizing respiratory artifacts. Free‐breathing alternatives are generally used when breath‐holding is not possible and often require longer scan time and/or more complex reconstruction. One potential exception is DCE‐MRI, which may represent an early application where the practical implementation could shift toward free‐breathing acquisition without a scan‐time penalty, since the contrast‐enhancement window spans several minutes regardless of the acquisition choice. Therefore, free‐breathing DCE‐MRI, particularly when combined with advanced dynamic reconstruction that leverages temporal correlations, may become increasingly attractive for clinical translation, which will be discussed further in Section 5.

5. Advanced Respiratory Motion Management Methods for Abdominal MRI: Opportunities and Translational Gaps

This section describes both long‐standing and more recent motion management strategies that have potential clinical value but have not been widely translated into routine practice for abdominal MRI. These include registration‐based motion correction, motion‐resolved and motion‐weighted reconstruction, sub‐second imaging using ultra‐fast acquisition, and deep learning‐based motion compensation. For each approach, we discuss opportunities, its clinical relevance, and translational gaps that currently limit clinical adoption.

5.1. Registration‐Based Motion Correction and Alignment

5.1.1. Opportunity

Registration‐based motion correction using rigid or non‐rigid models is one of the most extensively studied strategies [58, 59]. While highly effective in neuroimaging, where motion is predominantly rigid [60], its application to abdominal MRI is considerably more challenging due to the complex, deformable, and irregular respiratory patterns, which limit the robustness and generalizability of simple rigid motion models [61, 62, 63].

Despite these challenges, abdominal MRI is expected to greatly benefit from reliable and accurate non‐rigid image registration, particularly for applications affected by slice‐to‐slice inconsistency or inter‐image misalignment, such as SSFSE and free‐breathing DWI. Several pilot studies have shown that registration‐based correction can reduce inconsistencies and improve image quality in free‐breathing liver DWI [27, 64], although these approaches have not been widely validated and adopted in clinical practice.

5.1.2. Translational Gap

Overall, progress in registration‐based motion correction for abdominal MRI has been slow, primarily due to substantial variability in patient breathing patterns, the inherent error‐prone nature of deformable registration, and the computational complexity of non‐rigid motion estimation. For DWI, two additional barriers have to be considered. First, inter‐image misalignment occurs not only across slices or averages but also between images acquired with different b‐values and diffusion directions, which have varying contrast and SNR and make deformation estimation more challenging. Second, the inherently low SNR of DWI further complicates robust image registration.

In addition, the performance of registration‐based motion correction also varies across different organs. The kidneys, for example, often exhibit more rigid‐like motion due to their retroperitoneal location and surrounding fascial support [65, 66, 67, 68]. As a result, respiratory‐induced kidney motion occurs with relatively limited deformation compared to other organs, and the sharp anatomical boundaries of the kidneys further facilitate image registration. However, kidney‐focused MRI exams are generally less common in current clinical practice than other abdominal organs. As a result, although registration‐based motion correction is conceptually attractive for abdominal MRI, substantial technical advances are still needed before it can be reliably used in clinical practice.

5.2. Motion‐Resolved and Motion‐Weighted Reconstruction

5.2.1. Opportunity

As discussed in Section 4, respiratory triggering is an established technique for motion management in abdominal MRI but comes at the cost of substantially reduced scan efficiency. To address this limitation, reconstruction‐based approaches have been developed to utilize all acquired k‐space data and achieve near 100% imaging efficiency. An early strategy involved sorting data into different motion phases to generate motion‐resolved images, followed by image registration to combine these images into a single motion‐corrected result [69, 70, 71, 72, 73, 74, 75, 76]. However, this approach is limited by the difficulty of accurate image registration in abdominal MRI and by severe undersampling artifacts in motion‐sorted images.

Motion‐resolved reconstruction addresses these challenges by jointly reconstructing images across multiple respiratory states [77, 78, 79, 80]. It exploits the pseudo‐periodic nature of respiration to sort k‐space data acquired over multiple breathing cycles into a set of distinct respiratory states (Figure 8a–c). Images reconstructed from each state exhibit reduced intraframe motion and form an additional respiratory dimension. While each motion state is undersampled, the resulting respiratory dimension provides new temporal correlations that can be leveraged using advanced reconstruction methods, such as compressed sensing, to reduce undersampling artifacts. This concept was demonstrated in XD‐GRASP (eXtra‐Dimensional Golden‐angle RAdial Sparse Parallel imaging) MRI, which enables motion‐resolved reconstruction for free‐breathing imaging using golden‐angle radial sampling and sparse reconstruction [77], as shown in Figure 8c,d.

FIGURE 8.

(a, b) Motion‐guided data sorting using a respiratory motion signal extracted from an external device or from the acquired k‐space data (self‐navigation). Data can be sorted into multiple motion states spanning from end‐expiration to end‐inspiration with reduced motion blurring in each sorted image. Golden‐angle rotated sampling schemes are typically used to ensure adequate k‐space coverage after motion sorting. (c) Despite reduced motion blurring, the sorted images exhibit undersampling artifacts that degrade image quality. (d) Sparsity‐based reconstruction can be applied to the sorted data to exploit temporal correlations along the respiratory dimension and generate motion‐resolved images with improved image quality. Figure was reproduced from figure 1 in Feng et al. [77] with permission from the journal.

Practical implementation of motion‐resolved reconstruction requires a respiratory surrogate to guide data sorting (Figure 8a), which can be derived from self‐navigation or external devices. In addition, each motion state should maintain sufficiently uniform k‐space coverage after sorting (Figure 8b), typically achieved using a golden‐angle sampling scheme or its variants [54]. In abdominal MRI, motion‐resolved reconstruction has primarily been demonstrated for free‐breathing 3D GRE imaging for both DCE‐MRI [81] and PDFF/R2* quantification [82, 83], and is also expected to benefit 3D FSE MRCP as a more efficient alternative to respiratory triggering [84]. Related approaches based on self‐gating have also been explored, particularly for quantitative MRI [36, 85, 86, 87].

Motion‐weighted reconstruction, also known as soft‐gating, mitigates motion artifacts without explicit data sorting [88, 89, 90]. Instead of resolving data into distinct motion states, it assigns different weights to k‐space samples based on their respiratory states. For example, data acquired near end‐expiration can be weighted more, whereas motion‐corrupted data during inspiration are down‐weighted, as shown in Figure 9 for two examples. This approach does not alter the underlying k‐space sampling trajectory but instead modifies the contribution of each individual k‐space measurement within the data fidelity term. As such, it can be interpreted as a weighted least‐squares formulation that reduces the influence of motion‐affected data without requiring additional density compensation [88]. This implicit motion‐compensation strategy has been shown to effectively reduce motion artifacts without generating an extra motion dimension or substantially increasing reconstruction time [88, 89, 90, 91, 92, 93].

FIGURE 9.

Comparison of motion‐averaged and motion‐weighted (soft‐gated) reconstruction. Motion‐averaged images exhibit noticeable motion blurring (green arrows), while motion‐weighted reconstruction reduces intraframe motion artifacts with sharper image features.

5.2.2. Translational Gap

Despite their advantages, both motion‐resolved and motion‐weighted reconstruction face several practical limitations. First, they depend on accurate and robust respiratory motion detection, which can be difficult in abdominal MRI, particularly in the presence of dynamic contrast changes such as in DCE‐MRI. Second, motion‐resolved reconstruction requires sufficient data for each motion state, which can increase acquisition time compared with breath‐hold imaging and limits its use in time‐sensitive applications. Third, the additional respiratory dimension in motion‐resolved reconstruction increases computational complexity and reconstruction time, yet the resulting respiratory‐resolved images often do not provide additional diagnostic information for routine abdominal MRI. Finally, both approaches often rely on specialized acquisition trajectories and are not naturally compatible with routine clinical sequences. As a result, although these reconstruction‐based methods provide a promising strategy for handling respiratory motion, their widespread clinical translation has so far been limited.

5.3. Sensor‐Guided Free‐Breathing Imaging

5.3.1. Opportunity

A common limitation of many motion compensation approaches is their reliance on accurate respiratory motion signals, which are often difficult to obtain in abdominal MRI. This challenge has motivated the development of non‐contact sensor‐based methods for improved motion monitoring in recent years, and several vendor‐integrated solutions, such as PilotTone or Beat Sensor (Siemens Healthineers) [94, 95] and VitalEye (Philips Healthcare) [96], are now available for clinical evaluation. These technologies provide respiratory traces without requiring specific patient cooperation and can be used for motion gating or data sorting. A key advantage of these motion sensors is that they are directly integrated into MRI systems, which enables relatively seamless incorporation into clinical workflows.

5.3.2. Translational Gap

Despite growing interest, sensor‐guided motion monitoring does not directly enable motion compensation and must be combined with dedicated correction algorithms. As such, many limitations associated with existing motion compensation methods remain. In addition, most existing sensors provide only a single global respiratory signal at each time point, which may be insufficient to capture irregular breathing patterns, spatially heterogeneous motion, or baseline drift. Furthermore, motion signals obtained from these sensors are often not directly usable and require careful preprocessing (e.g., filtering) before they can be used for motion compensation. While published studies often focus on the downstream motion compensation results, the importance of robust respiratory signal extraction and preprocessing is generally underappreciated. In practice, the need for these additional processing steps may reduce robustness and complicate clinical implementation. To date, there is limited evidence to support that motion sensors can achieve widespread clinical adoption in free‐breathing abdominal MRI, despite promising results reported in research studies. Future advances that provide richer spatial motion information and more robust respiratory signal extraction in motion sensors may improve the accuracy of respiratory detection and facilitate more reliable motion correction.

5.4. Sub‐Second Imaging Using Ultra‐Fast Acquisition

5.4.1. Opportunity

One fundamental reason MRI is sensitive to respiratory motion artifacts is its relatively slow imaging speed. If images can be acquired sufficiently fast (e.g., with a temporal footprint of less than 1 s), the impact of respiratory motion can be reduced because breathing occurs on a much longer timescale. In fact, single‐shot sequences are inherently more robust to intraframe motion for this reason, as discussed in Section 3. While this principle is straightforward for 2D imaging, extending it to 3D acquisitions has historically been more challenging.

Recent advances in image reconstruction have demonstrated that a full 3D volume can be acquired in less than 1 s for DCE‐MRI by aggressively accelerating each contrast phase and exploiting temporal correlations in image reconstruction [97, 98, 99, 100, 101]. One example is GRASP‐Pro (GRASP with imProved performance), which integrates multicoil compressed sensing with a low‐rank subspace model to enable continuous free‐breathing DCE‐MRI with a sub‐second temporal footprint [97, 98], as shown in Figure 10. Acquiring each 3D volume within 1 s reduces intraframe motion artifacts and also eliminates the need for explicit motion detection and motion compensation. This approach can also improve robustness to irregular breathing or bulk motion compared with motion‐resolved reconstruction, as shown in Figure 11 [102].

FIGURE 10.

Sub‐second DCE‐MRI using low‐rank subspace‐based reconstruction demonstrates improved image quality compared with conventional dynamic reconstruction based on temporal sparsity.

FIGURE 11.

Time‐resolved, sub‐second 4D dynamic MRI demonstrates improved robustness to irregular breathing and bulk motion (indicated by green arrow) compared to motion‐resolved reconstruction. Yellow arrows indicate motion‐induced blurring in the motion‐resolved images. Figure was reproduced with adaption from figure 3 in Feng [102] with permission from the journal.

While this idea is relatively new in abdominal MRI, similar concepts have long been explored in cardiac MRI, where real‐time ungated imaging has proved valuable for patients with arrhythmias by rapidly acquiring each cardiac phase [103, 104, 105, 106]. Related concepts have also been explored in other applications. For example, a recently proposed quantitative fat/water imaging approach based on 2D chemical‐shift‐encoded acquisitions with flip‐angle modulation (FAM) acquires each slice within a short temporal window (typically < 1 s) and repeats with different flip angles [20, 21]. Compared to conventional multi‐echo 3D GRE, 2D FAM enables robust free‐breathing imaging with minimal intraframe motion while also correcting for residual T1 bias [20].

5.4.2. Translational Gap

Sub‐second volumetric imaging is particularly attractive for free‐breathing DCE‐MRI, where strong temporal correlations can be leveraged to aggressively accelerate each contrast phase. However, several practical challenges remain. First, these approaches often impose substantial computational demands, particularly when advanced iterative or model‐based reconstructions are needed. Second, the large number of reconstructed volumes (e.g., one 3D volume per second or faster) necessitates automated identification of clinically relevant contrast phases. Third, the effective use of temporal resolution in sub‐second dynamic imaging requires specially designed trajectories (e.g., golden‐angle rotated sampling), similar to motion‐resolved reconstruction.

In addition, while sub‐second imaging effectively mitigates intraframe motion, it does not eliminate inter‐frame respiratory variation. This may affect quantitative analysis where temporal motion consistency is required, although it is less likely to limit current clinical practice, where image interpretation remains largely qualitative. Integration of deep learning into both reconstruction and post‐processing may provide promising solutions to address these barriers.

5.5. Deep Learning‐Based Motion Compensation

5.5.1. Opportunity

Deep learning has been increasingly adopted for various medical imaging applications, including clinical abdominal MRI, where it has been successfully deployed to accelerate data acquisition and improve image quality [107]. However, the use of deep learning specifically for motion compensation in abdominal MRI remains at a relatively early stage. In this section, we discuss three major categories of deep learning techniques relevant to motion management: (a) deep learning‐based reconstruction for accelerated acquisition, (b) deep learning‐based motion artifact suppression, and (c) joint deep learning reconstruction and motion compensation.

Deep learning‐based reconstruction is now widely implemented clinically [108]. By enabling higher acceleration with reduced scan time, these methods shorten the acquisition window during which respiratory motion can corrupt data. Although not a direct motion correction strategy, scan time reduction remains one of the most effective ways to mitigate motion artifacts.

Beyond acceleration, deep learning‐based motion artifact suppression has been explored as a post‐processing strategy to reduce blurring and ghosting caused by respiratory motion. In this paradigm, neural networks are trained to map motion‐corrupted images to artifact‐reduced outputs, and pilot studies have shown promising results [109, 110, 111].

More advanced approaches integrate deep learning with image reconstruction and motion modeling, enabling simultaneous estimation of motion fields/vectors and recovery of motion‐corrected images from undersampled data. These methods often integrate physics‐based constraints and explicit motion models to improve performance [112, 113, 114, 115, 116, 117].

5.5.2. Translational Gap

Despite their promise, deep learning‐based motion artifact suppression methods are largely data‐driven and do not explicitly incorporate the physics of respiratory motion. This raises concerns regarding generalization across different patients and breathing patterns, as well as risks of hallucination or inadvertent suppression of subtle pathology. As a result, these approaches remain largely exploratory at the current stage.

Similarly, although joint reconstruction and motion compensation frameworks are conceptually attractive, practical implementation is often complex. Accurate estimation of non‐rigid abdominal motion from highly undersampled data is intrinsically challenging, particularly when image contrast varies rapidly. Most studies are limited to proof‐of‐concept studies with small datasets and lack large‐scale validation to demonstrate robustness and reproducibility.

Overall, deep learning holds substantial potential for improving motion management in abdominal MRI. However, unlike deep learning‐based reconstruction that enables accelerated data acquisition, which is now routinely used, deep learning‐based motion compensation remains in an early developmental phase. Major translational challenges include robust generalization across heterogeneous respiratory patterns, prevention of hallucination, and reliable modeling of complex abdominal motion. Addressing these challenges will require large‐scale validation across diverse patient cohorts, and this will be essential before routine clinical integration is possible.

6. Future Directions for Respiratory Motion Management in Abdominal MRI

By this point, readers are expected to have a clear understanding of the clinical needs for effective respiratory motion management in abdominal MRI and the cause of the gap between various motion compensation techniques and routine clinical translation. This section summarizes sequence‐specific considerations and highlights future research directions that may better align technical developments with clinical workflows.

6.1. Sequence‐Specific Consideration and Clinical Relevance

As described in Section 3, the requirements for respiratory motion management vary substantially across abdominal MRI sequences. For 2D SSFSE and DWI, reliable methods to align different slices and correct inter‐image misalignment in free‐breathing acquisitions remain major unmet needs. For these two sequences, faster acquisition is also desired to shorten echo trains, reduce T2/T2* blurring and geometric distortion, and improve spatial resolution.

For DWI, improved alignment across b‐values, diffusion directions, and averages could improve the reliability of abdominal DWI by reducing motion‐averaging blurring, which is particularly important for small motion‐sensitive structures such as the pancreas. In a recent study, deep learning has been applied to automatically identify relatively consistent diffusion‐weighted images and selectively combine them for averaging [118]. This data‐driven frame selection approach improves image quality and reduces motion‐induced blurring compared to conventional averaging of all repetitions.

For multi‐shot 2D and 3D FSE sequences, faster imaging could shorten breath‐hold duration and reduce the need for more concatenations. This may also enable single‐breath‐hold 3D FSE for MRCP while maintaining image quality comparable to respiratory‐triggered acquisitions. For free‐breathing 3D FSE, approaches that increase scan efficiency, such as motion‐resolved or motion‐weighted reconstruction combined with deep learning‐based reconstruction, may provide more consistent acquisition time compared to respiratory triggering or gating. In addition, non‐respiratory motion, such as peristalsis, may further limit image quality in certain applications (e.g., MRCP), even when respiratory motion is well controlled.

For 3D GRE sequences, DCE‐MRI may be one of the first applications to transition from breath‐hold to free‐breathing acquisition, as it does not necessarily incur a scan‐time penalty because the acquisition must span the full contrast enhancement period regardless of the acquisition strategy. In contrast, for other 3D GRE applications such as multi‐echo fat/water imaging or hepatobiliary‐phase imaging with Gadoxetate disodium (Eovist/Primovist), breath‐hold remains preferred for now due to short scan time. Free‐breathing methods are likely to remain reserved for patients unable to perform reliable breath‐holds unless scan time can be reduced to match current breath‐hold protocols.

Respiratory motion also has important implications for quantitative imaging. Motion‐induced inconsistencies may introduce bias and reduce reproducibility in the estimation of PDFF, R2*, and liver stiffness. This highlights the importance of motion‐consistent acquisition and reconstruction strategies for reliable quantitative assessment. For MRE, consistent respiratory states across phase offsets are required for accurate stiffness estimation, which is typically achieved using breath‐hold acquisitions in current clinical practice.

6.2. Practical Considerations for Clinical Translation of Free‐Breathing Techniques

Based on these considerations, five practical requirements can be identified for free‐breathing techniques to become the first‐line clinical option in abdominal MRI. First, acquisitions should not substantially prolong total scan time compared to standard breath‐hold protocols, as scan efficiency remains a primary constraint in clinical workflows. Second, image reconstruction should be efficient with minimal latency to enable timely quality assessment and interpretation. Third, image quality should be comparable to that achieved with successful breath‐hold imaging to maintain diagnostic confidence. Fourth, methods should be simple, robust, and generalizable across a wide range of patients and breathing patterns without complicated parameter tuning. Finally, approaches that minimize reliance on explicit respiratory motion detection or complex data sorting may improve robustness under irregular breathing. If these requirements are not met, free‐breathing techniques are likely to remain limited to patients unable to hold their breath. From this perspective, many advanced motion compensation techniques described in Section 5 do not fully satisfy these requirements, which helps explain the persistent gap between technical development and routine clinical adoption.

6.3. Intelligent Selection of Breath‐Hold or Free‐Breathing Acquisitions

As discussed earlier, breath‐hold acquisitions are typically the first choice for most sequences in abdominal MRI, and free‐breathing alternatives are used only when a patient cannot hold breath. In many exams, however, this decision is often made after several failed breath‐hold attempts, which results in unnecessary delays and reduced workflow efficiency. Intelligent scan‐selection based on artificial intelligence could guide technologists in choosing the appropriate strategy, either breath‐hold or free‐breathing, at the start of the exam. Such intelligent triage can incorporate patient factors, prior imaging from the same subject, or even features extracted from localizers, and it holds great potential to reduce repeated acquisitions and improve efficiency and consistency.

6.4. Advanced Image Denoising

Like accelerated imaging, denoising can also indirectly reduce motion artifacts by enabling shorter scans and reducing the need for signal averaging. As discussed in Section 3, DWI typically requires multiple signal averages to ensure adequate SNR. Because these averages are often acquired under free‐breathing, the resulting averaged images can suffer from blurring due to the combination of images acquired at different respiratory states (Figure 3). As a result, reducing the number of averages could decrease such blurring at the cost of lower SNR.

Advanced denoising, particularly self‐supervised deep learning approaches, offers a promising way to restore SNR while allowing fewer averages [119, 120]. Such approaches could improve the quality of DWI [121], especially for small or motion‐sensitive structures, and may extend to other sequences that require multiple averages. Furthermore, joint image reconstruction and denoising may provide additional benefit to improve image quality and scan efficiency together [122].

6.5. Improved Radial and PROPELLER Imaging

Radial and PROPELLER acquisitions have both demonstrated great clinical value in abdominal MRI by enabling free‐breathing imaging. However, these sequences are primarily used as backup options rather than default choices to date due to several factors including reduced imaging efficiency, longer scan time, challenges in maintaining consistent image quality, and trajectory‐specific artifacts (Figure 7).

Advances in reconstruction methods that improve image quality and robustness could allow radial and PROPELLER imaging to be more time‐efficient while providing improved diagnostic quality in all patients regardless of their breath‐hold capacity. Such progress could shift these approaches from secondary options to preferred acquisition strategies. Radial sampling, in particular, offers advantages for free‐breathing DCE‐MRI due to its motion robustness and flexible data acquisition [77, 98]. With continued improvements in reconstruction speed, artifact suppression, and robustness, such approaches may see broader clinical adoption in abdominal imaging.

6.6. Further Advances in Sub‐Second Imaging Approaches

As discussed in Section 5, sub‐second imaging approaches hold great potential for addressing intraframe respiratory motion in a more robust and generalizable way. A key advantage of this type of approach is that it does not rely on strong assumptions or complex motion models and instead exploits the simple principle that motion artifacts and blurring can be effectively minimized when the data acquisition window is shorter than the timescale of respiratory motion. Existing studies have demonstrated the promise of this imaging strategy in free‐breathing DCE‐MRI [98], where rapid imaging has led to improved robustness against variable breathing patterns.

This concept can potentially be extended to other sequences as well. For example, in multi‐shot 2D FSE acquisitions, one could perform shot‐resolved imaging using the idea of motion‐resolved image reconstruction, in which images from individual shots are reconstructed separately instead of being combined into a single image. By leveraging temporal correlations across shots, this approach could reduce sensitivity to inter‐shot motion and enable more robust free‐breathing multi‐shot 2D FSE imaging.

6.7. More Advanced Registration for Motion Alignment

Inter‐image motion alignment has been discussed throughout this review and remains an important unmet need in abdominal MRI. Despite extensive research, existing registration‐based motion alignment algorithms are generally not ready for immediate clinical use due to limitations in robustness and accuracy. Emerging deep learning‐based registration methods may provide improved speed, robustness, and consistency. In addition, they may better leverage multi‐contrast information, which is particularly important in DWI, where contrast varies across b‐values and diffusion directions. Advanced generative approaches, such as diffusion models and other foundation‐models, may further improve the performance of image registration. It is also important to realize that registration may be most clinically relevant for correcting inter‐image and slice‐to‐slice inconsistency in abdominal MRI, while intraframe motion in 3D acquisitions may be better addressed through other approaches discussed above, unless the need for registration is carefully justified.

7. Conclusion and Lessons Learned From the Gap in Respiratory Motion Management

Respiratory motion remains a major challenge in abdominal MRI, and the development of effective motion management strategies continues to be an active area of research. This review analyzed and discussed the barriers underlying the gap between technical innovation and clinical translation in respiratory motion management, clarifies the practical clinical needs for addressing the motion challenge in abdominal MRI, and highlights future directions that may better align research advances with clinical workflows and diagnostic priorities.

The translational gaps discussed in the previous sections provide lessons extending beyond respiratory motion compensation in abdominal MRI. Overall, these observations indicate that successful translation of new MRI technologies depends not only on methodological innovation but, perhaps more importantly, on close alignment with practical clinical needs and workflow. The following considerations may help guide future technical development of general MRI technologies.

First, technical innovation should begin with a clear understanding of the clinical problem, the underlying clinical needs, and the limitations of current methods. Active engagement with experienced radiologists and, when appropriate, MRI vendors, can provide valuable insights into unmet clinical needs, practical workflow constraints, and realistic development pathways. In this context, commonly used image quality metrics, such as root mean square error (RMSE), structural similarity index (SSIM), and other related measures, are insufficient to ensure meaningful clinical value [123], and evaluation of new methods should involve experienced end users (e.g., radiologists) and focus more on diagnostic confidence, robustness in routine workflows, and potential clinical impact when clinical adoption is the goal.

Second, many novel techniques are limited to proof‐of‐concept studies in controlled settings. Methods aiming for clinical adoption should incorporate a clear pathway toward larger‐scale, heterogeneous, and preferably multicenter evaluation to establish robustness and generalizability in clinical environments. Although such studies are inherently challenging and require coordinated efforts among physicists, clinicians, and often institutional stakeholders, they represent an essential step toward ultimate clinical translation.

Third, even technically sophisticated methods may face adoption barriers if they require scanner‐specific or institution‐specific adjustment to acquisition protocols or reconstruction algorithms. As a result, approaches that are compatible with existing scanners and routine MRI protocols are more likely to demonstrate meaningful clinical value. In this context, open‐source dissemination and transparent reporting of implementation details may facilitate cross‐scanner and cross‐vendor validation and thereby accelerate broader clinical translation.

Fourth, robustness to patient variability (e.g., breathing patterns) should be treated as a central consideration in technical development. One lesson from the rapid clinical translation of some prominent MRI techniques, such as parallel imaging and, more recently, deep learning‐based reconstruction, is their minimal reliance on patient‐specific assumptions. In contrast, many motion management methods depend on strong assumptions about respiratory models, and they are often demonstrated only in small proof‐of‐concept studies involving healthy volunteers. This limitation can significantly restrict generalizability and clinical impact.

Fifth, in addition to technical considerations, non‐technical factors also contribute to the limited clinical adoption of advanced MRI techniques. These include workflow complexity, technologist training and comfort, radiologist familiarity, and the lack of standardized protocols. As a result, even when new techniques are available on clinical scanners, these factors may limit routine use. At the same time, these barriers are often linked to underlying technical performance, as methods that are time‐consuming, less robust, or operationally complex are less likely to be adopted. This also highlights the importance of defining clinically acceptable image quality, including the evaluation of motion artifacts in the context of specific clinical tasks in abdominal MRI.

Finally, successful translation of new MRI techniques into routine clinical practice typically requires close collaboration between academic researchers, clinicians, and industry partners. New models of academic‐industry partnership that can facilitate broad clinical evaluation will therefore be crucial to expedite translation of new MRI technologies, including respiratory motion management techniques.

Funding

This work was supported by the National Institutes of Health (P41EB017183, R01DK143170, R01EB030549, R01EB031083, and R21EB032917).

Conflicts of Interest

L.F. and H.C. are co‐inventors of a patent on the GRASP and XD‐GRASP MRI techniques. H.C. receives research support from Siemens Healthineers under an institutional master research agreement and travel and speaker bureau support from Siemens Healthineers.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.