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. Author manuscript; available in PMC: 2023 Jan 24.
Published in final edited form as: Investig Magn Reson Imaging. 2022 Dec 31;26(4):265–274. doi: 10.13104/imri.2022.26.4.265

Improving delineation of the corticospinal tract in the monkey brain scanned with conventional DTI by using a compressed sensing based algorithm

Yuguang Meng 1, Chun-Xia Li 1, Xiaodong Zhang 1,2,*
PMCID: PMC9873154  NIHMSID: NIHMS1864509  PMID: 36698482

Abstract

Background:

The corticospinal tract (CST) is a major tract for motor function. It can be impaired by stroke. Its degeneration is associated with stroke outcome. Diffusion tensor imaging (DTI) tractography plays an important role in assessing fiber bundle integrity. However, it is limited in detecting crossing fibers in the brain. The crossing fiber angular resolution of intra-voxel structure (CFARI) algorithm shows potential to resolve complex fibers in the brain. The objective of the present study was to improve delineation of CST pathways in monkey brains scanned by conventional DTI.

Methods:

Healthy rhesus monkeys were scanned by diffusion MRI with 128 diffusion encoding directions to evaluate the CFARI algorithm. Four monkeys with ischemic occlusion were also scanned with DTI (b = 1000 s/mm2, 30 diffusion directions) at 6, 48, and 96 hours post stroke. CST fibers were reconstructed with DTI and CFARI-based tractography and evaluated. A two-way repeated MANOVA was used to determine significances of changes in DTI indices, tract number, and volumes of the CST between hemispheres or post-stroke time points.

Results:

CFARI algorithm revealed substantially more fibers originated from the ventral premotor cortex in healthy and stroke monkey brains than DTI tractography. In addition, CFARI showed better sensitivity in detecting CST abnormality than DTI tractography following stroke.

Conclusion:

CFARI significantly improved delineation of the CST in the brain scanned by DTI with 30 gradient directions. It showed better sensitivity in detecting abnormity of the CST following stroke. Preliminary results suggest that CFARI could facilitate prediction of function outcomes after stroke.

Keywords: diffusion tensor imaging, nonhuman primate, fiber tracking, CFARI, compressed sensing, stroke

Introduction

The corticospinal tract (CST) connects sensorimotor areas of cerebral cortex to spinal cord. It is a major tract in the brain for motor function. The CST can be impaired by tumor, trauma, or stroke [1]. It can be examined using DTI tractography to assess motor malfunctions of the arm, hand, and foot after stroke [2, 3], tumor [4], or spinal cord injury [5]. Loss in the CST axial diffusivity (AD) and fractional anisotropy (FA) might be strong prognostic indicators of future motor functions in stroke patients with substantial initial motor impairment [6]. However, conventional DTI with low angular resolution does not have sufficient sensitivity in delineating lateral motor pathways [4]. It has limitations in detecting tracts with touching and crossing fibers which are prevalent in the human brain [7, 8]. High angular resolution diffusion imaging (HARDI), q-ball imaging (QBI), and diffusion spectrum imaging (DSI) techniques can be applied to resolve crossing-fiber tracking issues [9, 10]. However, the scanning time can be substantially increased compared to traditional DTI (with 30 or less gradient directions) because the total acquisition time is proportional to the number of applied gradient encoding directions. Consequently, applications of advanced techniques are limited in preclinical studies or clinical practices due to the demand of increased data acquisition time and/or higher gradient strength.

Compressed sensing (CS) algorithms allow for reconstructing under-sampled k-space data to retain MR image quality comparable to fully acquired data for acceleration of MRI data acquisition [11, 12]. CS approaches for diffusion MRI (dMRI) have been explored to accelerate data collection of HARDI [13, 14] and diffusion spectrum imaging (DSI) [15, 16] of human brains, showing great potential to substantially improve the performance of fiber tractography using dMRI data collected with low angular resolution. Interestingly, the crossing fiber angular resolution of intra-voxel structure (CFARI) algorithm is very promising for resolving crossing fibers of the brain using the constrained compressed sensing technique with a tensor mixture model [17], demonstrating its robustness to estimate complex micro-fiber characteristics by estimating local diffusion orientation distribution in healthy human brains scanned with DTI with 30 gradient directions and b-value of 700 s/mm2 [18]. Prior results indicated that the compressed sensing technique might be useful for substantially improving the detection of complex fiber pathways in the brain scanned by conventional DTI.

DTI has been widely used to examine stroke lesion for its sensitivity and robustness. Currently it is still a powerful tool to assess functional outcome in clinical studies of stroke [19–21]. In addition, DTI tractography could be utilized to examine CST injury following stroke for predicting motor recovery [2, 22]. However, it has limitations in detecting fibers originated from the lateral cortex. In the present study, we hypothesized that the CFARI algorithm could be employed to explore DTI data and reveal fibers missed by DTI tractography to improve estimation of the CST fiber bundle.

Monkeys have superior advantage than rodents in translational research of stroke [23]. They are also an excellent model because they resemble the organization of the CST neural mechanism of human better than other large animals such as rabbits, cats, and dogs [24]. In the present study, the CFARI algorithm was explored to delineate CSTs of monkey brains and examine the abnormality of CST pathways in stroke-injured rhesus monkey brains scanned by convenient DTI.

Materials and Methods

Four adult healthy rhesus monkeys (female, 10–13 years old, 9–12 kg) were utilized to evaluate the effectiveness of the CFARI algorithm in improving fiber tractgraphy. Monkeys were anesthetized with 1.0–1.5% isoflurane mixed with 100% O2 and immobilized in a supine position by a custom-made head holder. They breathed spontaneously during scanning. Heart rate, respiration rate, body temperature, Et-CO2, inhaled CO2, O2 saturation, and blood pressure were monitored continuously and maintained [25].

Four stroke rhesus monkeys (female, 10–21 years old, 8.6 ± 1.2 kg; ID: RVG4, RRI3, PH1019, RFA5) were utilized in the present study. Permanent middle cerebral artery occlusion (pMCAo) was induced in monkeys with a minimal interventional approach reported previously [26]. These animals were euthanized immediately after their last MRI scans. Their brains were harvested and kept in 10% buffered formalin. One brain (RRI3, 96 hours post stroke) was blocked and sectioned for Bielschowsky’s silver staining to detect degenerative nerve fibers.

MRI scans for healthy monkeys

In vivo MRI scans were performed with a Siemens 3T scanner (MAGNETOM TIM Trio, Siemens Healthineers, PA, USA) equipped with an 8-channel phased-array volume coil dedicated to monkey brain scanning. In order to explore the CFARI algorithm and evaluate its effectiveness in improving diffusion MRI tractography, healthy macaque monkeys were scanned using an echo planar imaging (EPI) sequence with GRAPPA (R =3) and the following parameters: TE/TR = 95 ms/3900 ms, isotropic voxel size = 1.0 mm × 1.0 mm × 1.0 mm. Two-shell dMRI data set using 128 non-collinear diffusion encoding directions with b = 1000 s/mm2 and 128 directions with b = 1700 s/mm2 (128D2b1000/1700) were collected and averaged to improve image SNR. A novel 3D shimming procedure was applied to minimize susceptibility to induced artifacts in diffusion weighted images of monkeys [27]. A phase-reversal acquisition approach was applied for EPI image distortion correction of NHP brains [28]. Additionally, T1-weighted images were acquired with a 3D MPRAGE with an inversion time of 950 ms for grey matter and white matter structural identification.

In order to evaluate effects of the number of gradient encoding directions and different b-values on the CFARI algorithm, data subsets with different diffusion-encoding schemes were selected from the acquired 2-shell dataset using the strategy reported previously [29] : 1) 32 directions with b = 1000 s/mm2 (32Db1000); 2) 32 directions with b = 1700 s/mm2 (32Db1700); 3) 32 directions with b = 1000 s/mm2 and 32 directions with b = 1700 s/mm2 (32D2b1000/1700); and 4) 128 directions with b = 1700 s/mm2 (128Db1700).

MRI scans for stroke monkeys

DTI data for stroke monkeys were acquired with a single-shot EPI sequence using parallel imaging (GRAPPA, R = 3) with TE/TR = 80ms/5000ms, isotropic voxel size = 1.5 mm × 1.5 mm × 1.5 mm, 30 gradient directions with b-value = 0,1000s/cm2 on Day 0 (6 hours), and repeated on Day 2 (48 hours) and Day 4 (96 hours) post-occlusion. A field map was collected for image distortion correction.

Diffusion MRI Data Processing

All data were processed for eddy-current and field inhomogeneity distortion corrections and diffusion MRI tractography with an FSL software package (University of Oxford). Deterministic streamline tractography was carried out with fiber assignment using a continuous tracking algorithm [30]. Conventional DTI tractography was also carried out with fiber assignment using the continuous tracking (FACT) algorithm [30]. FA threshold of 0.15 and maximum turn angle of 70 degree were used for DTI fiber tracking of both healthy and stroke monkey brains.

In addition, each dMRI dataset was further processed with the CFARI algorithm in which voxelwise mixture fractions were estimated with the same optimization criteria reported previously in a human brain study [18]. The flow chart for monkey dMRI data processing with CFARI algorithm was illustrated in Figure 1. Fiber tractography of each diffusion encoding scheme was carried out with an intra-voxel fiber assignment with the continuous tractography (INFACT) algorithm [18]. Delineated fiber maps were transformed to binary images of NIFTI-1 data format using Tract-Tool script in DTI-TK toolbox (dti-tk.sourceforge.net) for comparison purpose. CST passing through the left or right medulla was selected manually on b0 images of the brain for delineating tract number and tract volume. CST tractography results from DTI and CFARI algorithm were compared with those processed using HARDI with the original data set (128D2b1000/1700) [31].

Figure 1.

Figure 1.

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Flow chart showing fiber tractography processing of conventional diffusion tensor imaging (DTI) data using the crossing fiber angular resolution of intra-voxel structure (CFARI) algorithm.

For stroke monkeys, DTI data were processed with conventional DTI tractography using FSL software and reconstructed using the same CFARI approach. DTI indices including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), tract number, and tract volume of CSTs were extracted separately at each post-occlusion time point (Day 0, 2, or 4) for left and right hemispheres.

A two-way repeated measures multivariate analysis of variance (MANOVA) was carried out using SPSS 17.0 (SPSS Inc, Chicago, IL, USA) with independent factors of hemisphere (contralateral vs. ipsilateral) and post-occlusion time point (Day 0, 2, 4) followed by post hoc analysis (with p < 0.05 as significance threshold) and Bonferroni correction to determine differences of DTI indices (FA, AD, RD, MD), tract numbers, and tract volumes of CSTs between contralateral and ipsilateral hemispheres or post-occlusion time points.

All procedures for animal care and handling in the present study were approved by the Institutional Animal Care and Use Committee (IACUC) of Emory University in a facility accredited by AAALAC. They were in compliance with the Animal Welfare Act and the Public Health Service Policy on Humane Care and Use of Laboratory Animals, and the Guide for the Care and Use of Laboratory Animal Medicine (Eighth Edition).

Results

Estimation of complex fibers in healthy monkey brains using DTI and CFARI algorithm

As seen in Fig. 2, delineated fiber maps of the brain area including the dorsal premotor cortex, corpus callosum, internal capsule, and ventral premotor cortex were transformed to binary images and demonstrated. In comparison with conventional DTI tractography with 32 gradient directions in which very limited fibers from the dorsal premotor cortex were seen (Fig. 2, top row), the CFARI tractography revealed significantly more fibers in each corresponding scheme (Fig. 2, bottom row). With increasing number of gradient encoding directions, more fibers were seen in either conventional or CFARI algorithm.

Figure 2.

Figure 2.

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Fiber maps in cortical regions comprising dorsal/ventral premotor cortices overlapped on coronal T1-weighted images of a rhesus monkey brain. Fibers were delineated using conventional DTI/QBI tractography and CFARI algorithm with datasets from different diffusion-encoding schemes. Numbers of diffusion encoding directions in inner (b = 1000 s/mm2) and outer (b = 1700 s/mm2) shells are separated with a comma and exhibited for each scheme.

Tractography of healthy monkey CST

CST in a healthy monkey brain derived from different diffusion encoding schemes (32Db1000 and 128Db1700) is illustrated in Fig. 3. Using conventional DTI tractography, fibers originated from the dorsal cortex were mostly observed while few were observed from the lateral cortex with the 32Db1000 scheme (Fig. 3A, shaded area). In contrast, tractography results of the CFARI algorithm revealed obviously more fibers in the lateral cortex, similar to QBI results of the same monkey scanned with the 128Db1700 scheme (Figs. 3B and 3C). Statistical analysis results of CST volumes in normal monkeys showed that CST volumes derived from the CFARI approach were 94% of those by QBI tractography (5.8 ± 1.1 mm2 vs. 6.2 ± 1.2 mm2, p = 0.07). In contrast, CST volumes derived from traditional DTI tractography were only 68% of those by QBI tractography (4.2 ± 0.8 mm2 vs. 6.2 ± 1.2 mm2, p = 0.001).

Figure 3.

Figure 3.

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Corticospinal tract of a healthy monkey brain delineated using A) traditional DTI with b = 1000 s/mm2 and 32 gradient directions, B) traditional DTI data reconstructed using CFARI approach, and C) q-ball imaging (QBI) for dataset with b = 1700 s/mm2 and 128 gradient directions. Obvious differences of delineated fibers by DTI, CFARI, and QBI approaches are illustrated in shaded areas.

Tractography of CST in stroke monkey brains

Substantial ischemic infarction was seen in the MCA territory of each monkey. Progressive changes of infarction and CST in a monkey brain (RRI3) following stroke insult are demonstrated in Fig. 4. Tracts from the dorsal cortex of contralateral and ipsilateral hemispheres of the stroke monkey were mostly delineated with traditional DTI tractography. However, tracts originated from the lateral cortex (the ventral premotor cortex) were mostly missed by DTI tractography, but observed by CFARI-based tractography. Also, both tractography approaches showed progressive reduction of the tract number in the CST of the ipsilateral hemisphere during an acute stroke.

Figure 4.

Figure 4.

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Longitudinal alteration of the corticospinal tract (CST) in a stroke monkey brain (ID: RRI3) (overlaid on coronal diffusion-weighted images) following ischemic occlusion at Day 0, Day 2, and Day 4. CST pathways were constructed with conventional DTI (top) (b = 1000s/mm2, 30 gradient directions) and CFARI-based (bottom) tractography.

By comparing differences of DTI indices, tract numbers and tract volumes of the CST between results of contralateral and ipsilateral hemispheres, no significant differences in DTI indices (FA, MD, AD, RD), tract numbers, or tract volumes derived with either DTI or CFARI-based tractography (p > 0.25) on Day 0 were observed (Fig. 5, Table 1). There were no AD or RD changes at any time point either (data not shown). Significant reductions of tract numbers and tract volumes of the CST by DTI tractography were seen on Day 4 in comparison to the contralateral side (p < 0.05) (Figs. 5c and 5d).

Figure 5.

Figure 5.

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A graphical representation of temporal evolution of fractional anisotropy (FA) (a), mean diffusivity (MD) (b), tract number (c), and tract volume (d) of the corticospinal tract (CST) in the contralateral (Con.) or ipsilateral (Ips.) hemisphere of a monkey brain following an ischemic stroke.

*Significant differences (Bonferroni correction; p < 0.05) between two hemispheres using the same tractography (DTI or CS-DTI) at each time point. §, §§: significant difference (Bonferroni correction; § p < 0.05, §§ p < 0.01) between post-occlusion time points using the same (DTI or CS-DTI) tractography in the same hemisphere (contralateral or ipsilateral).

Table 1.

Diffusion indices and statistical p-values with Bonferroni correction

Measurement Day 0 Day 2 Day 4
DTI CFARI DTI CFARI DTI CFARI
Con. (SD) Ips. (SD) p value Con. (SD) Ips. (SD) p value Con. (SD) Ips. (SD) p value Con. (SD) Ips. (SD) p value Con. (SD) Ips. (SD) p value Con. (SD) Ips. (SD) p value
FA 0.34 0.34 0.88 0.33 0.32 0.74 0.34 0.33 0.74 0.33 0.31 0.19 0.34 0.32 0.18 0.33 0.30 0.03 *
(0.02) (0.01) (0.02) (0.01) (0.02) (0.01) (0.01) (0.02) (0.02) (0.01) (0.01) (0.01)
MD 0.79 0.79 0.88 0.81 0.81 0.89 0.79 0.78 0.89 0.82 0.79 0.04 * 0.79 0.78 0.82 0.81 0.80 0.85
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.01) (0.02) (0.03) (0.02) (0.02) (0.02)
AD 1.10 1.09 0.44 1.10 1.09 0.70 1.09 1.09 0.70 1.10 1.07 0.25 1.09 1.10 0.75 1.10 1.07 0.14
(0.02) (0.03) (0.02) (0.04) (0.03) (0.03) (0.02) (0.02) (0.03) (0.04) (0.02) (0.02)
RD 0.65 0.65 0.95 0.68 0.67 0.73 0.65 0.64 0.73 0.68 0.68 0.72 0.66 0.66 0.98 0.68 0.69 0.38
(0.02) (0.01) (0.02) (0.02) (0.03) (0.02) (0.02) (0.03) (0.02) (0.03) (0.02) (0.03)
Tract number 345 307 0.55 535 481 0.30 355 261 0.30 548 387 0.02 * 387 172 0.04 * 609 352 0.02 *
(109) (93) (110) (113) (106) (128) (130) (159) (89) (96) (43) (115)
Tract volume 2302 1864 0.25 3373 3024 0.27 2253 1657 0.27 3447 2441 0.01 * 2445 989 0.02 * 3843 2221 0.02 *
(612) (542) (691) (692) (693) (816) (818) (1000) (566) (476) (264) (695)
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(*p < 0.05) by post hoc comparisons between hemispheres (contralateral (Con.) vs ipsilateral (Ips.)) of monkey brains (n=4) following stroke. Diffusion metrics (FA, MD (10−3 mm2/s), AD (10−3 mm2/s) and RD (10−3 mm2/s)), tract number and tract volume (mm3) in the corticospinal tract (CST) delineated with DTI or CFARI tractography are illustrated. SD: standard derivation.

In contrast, results of CFARI-based tractography showed significant reduction of MD on Day 2 in comparison to the contralateral side (Fig. 5). There were no AD or RD changes at any time point (data not shown). Additionally, FA was significant decreased on Day 4 in comparison to the contralateral side or that on Day 0 and Day 2 (Fig. 5a). Significant reductions of tract number and volume derived by the CFARI approach were seen on Day 2 and Day 4 (Figs. 5c and 5d) in comparison to the contralateral side (p < 0.05), or those on Day 0 and Day 2. Bielschowsky’s silver staining of a stroke monkey brain illustrated substantial degeneration of fiber bundles originated from the lateral cortex in the ipsilateral hemisphere of the brain (Fig. 6).

Figure 6.

Figure 6.

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Bielschowsky’s silver staining revealing degenerative white matter fiber bundles in a monkey brain (RRI3) at 96 hours post stroke. Bar = 3mm. Arrows mark white matter fiber bundles with or without stroke lesion.

Discussion

Although DTI tractography is a robust tool to examine corticospinal tract pathways, it is very limited in detecting pathways originated from the ventral premotor cortex when the number of gradient directions is low (~30 or less). Present results suggest that compressed sensing-based CFARI algorithm could substantially improve detection of the corticospinal tract in the brain scanned by conventional DTI. In addition, the CFARI-based tractography significantly improved the sensitivity of detecting early microstructural changes in the corticospinal tract of a stroke brain scammed by conventional DTI with b = 1000 s/mm2 and 30 directions. As conventional DTI is still widely used in clinic and preclinical studies of stroke, present preliminary results suggest the CFARI algorithm might be useful for exploiting conventional DTI data to improve the detection of CST abnormality for stroke outcome prediction.

Delineation of the corticospinal tract (CST) in healthy adult monkey brains

The CST originated from the sensorimotor cortex converges in the corona radiate. It then enters the internal capsule and cerebral peduncle to form the course of the CST pathways. It plays a major role in motor function [32]. As demonstrated in the present study, traditional DTI mostly showed fibers originated from the dorsal premotor cortex, while the CFARI approach could detect more fibers than conventional DTI tractography, especially in the ventral premotor cortex where traditional DTI tractography failed (Figs. 2 and 3), in good agreement with a previous study using CFARI algorithm in a healthy human brain [18].

The CFARI algorithm revealed many more fibers in the CST missed by regular DTI tractography (Fig. 3), suggesting that the compressed sensing based tractography could substantially improve the detection sensitivity of the CST in the monkey brain compared to traditional DTI. In particular, results of this study suggest that an increase of the CST fibers is likely from the ventral premotor cortex, thus facilitating the evaluation of motor function in patients or animal models scanned with traditional DTI.

Evaluation of CST tractography in stroke brains

One of the main consequences of stroke is the loss of motor function due to neuron death in the associated cortex. Stroke is the major cause of serious disability in adults accordingly. Since the CST originates from the somatomotor cortex, the integrity of descending CST could be damaged due to direct damage to the CST in the injured brain region and the effect of Wallerian degeneration [1, 33, 34]. Previous studies have demonstrated that CST integrity is associated with motor recovery in chronicle phase of stroke [2, 35–38]. In addition, the effect of stroke on CST can be observed in the acute phase of stroke [22, 39–41]. Early changes in DTI indices of the CST preceding Wallerian degeneration might be associated with clinical outcomes [33, 42].

Previous studies have also demonstrated that DTI indices are helpful for identifying the degree of the CST impairment in stroke patients [2, 22, 35–37, 39]. Accurate delineation of white matter pathways using quantitative diffusion metric measures is essential for assessing disrupted anatomical connectivity to predict motor-sensory dysfunction and recovery following stroke [2, 35–37, 39]. However, results from DTI tractography might be biased as it relies on single principle diffusion orientation. Thus, it could not properly delineate CST with abundant crossing fibers [7, 8, 10, 37].

Non-human primates (NHPs) are highly similar to humans in brain anatomical structures, metabolism, functional connections, and organizations [43, 44]. Compared to rodent models with little white matter, an NHP model of stroke provides a unique opportunity to longitudinally investigate white matter alterations after stroke onset [23, 45–47]. As demonstrated in Figs. 4 and 5, CST fiber volumes derived by DTI tractography decreased gradually following stroke onset in the lesion side of the stroke brain (Fig. 4, top row). Similar reduction was seen in fiber tracts derived by the CFARI algorithm as well (Fig. 4, bottom row). However, using the CFAFI approach, substantially more fibers originated from the lateral cortex in ipsilateral and contralateral regions of the stroke brain were seen. In addition, as the ventral premotor cortex plays a critical role in decision-making based on various sensory cues and preparation for upcoming motor behavior in non-human primate brains [48], improved fiber detection in the lateral cortex by the CS based tractography may provide additional connectivity information about cognitive dysfunctions in stroke patients.

As illustrated in Table 1, diffusion indices (FA, AD, RD, MD) of the CST derived by regular DTI tractography showed no significant difference between contralateral and ipsilateral sides at any time points (except for tract number and volume on Day 4 post-occlusion). In contrast, parameters derived by the CS-based tractography revealed significant differences not only in the tract number and tract volume, but also in diffusion index change (MD decrease) as early as on Day 2 post-occlusion, in agreement with previous results of lucus fast blue staining of the same stroke monkeys showing fiber degeneration on Day 2 and Day 4 post stroke [47] and findings of white matter degeneration in acute stroke patients and rodents [39, 49–51]. In particular, Bielschowsky’s silver staining of a stroke monkey brain (Fig. 6) illustrated substantial degeneration of fiber bundles originated from the lateral cortex, in agreement with CFARI-tracking results. These findings suggest that CFARI could improve the accuracy of fiber delineation and that crossing fibers in the CST should be monitored closely in stroke patients for better prediction of stroke outcomes.

For CST in the contralateral hemisphere of stroke monkeys, there were no changes in diffusion metric, tract number, or tract volume as expected, indicating that the contralateral side was minimally affected in MCAO monkey brains during the 4-day study period. This result is in agreement with a previous finding showing no significant change in the contralateral corticospinal tract of stroke patients from 1 week to 1 year after onset [41].

As the integrity of the corticospinal tract is closely associated with motor function, conventional DTI tractography (b = 1000mm/s2, ~30 gradient directions) is still a powerful tool to assess the CST integrity in stroke patients [52]. Preliminary results of this study using monkey brains suggest that the CFARI algorithm can exploit conventional DTI dataset to improve the delineation of CST pathways originated from both dorsal and ventral premotor cortices, accordingly facilitating the prediction of cognitive dysfunction in primate or stroke patients.

In conclusion, the CFARI algorithm can significantly improve the delineation of the CST in the brain scanned by conventional DTI, showing better sensitivity in detecting abnormity of the CST following stroke. Preliminary findings of this study suggest that the CFARI algorithm might be used to facilitate examination of the CST or other fiber pathways in stroke or other neuroscience studies using conventional DTI with less diffusion encoding directions. Results of the present study also suggest the importance of assessing full CST pathways for predicting function outcome after stroke.

Acknowledgements

This research was supported by the National Center for Research Resources P51RR000165 and the Office of Research Infrastructure Programs (OD P51OD011132).

Footnotes

Disclosure: The authors have conflicts of interests relevant to this study to disclose.

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