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. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: J Magn Reson Imaging. 2023 Aug 23;59(4):1312–1324. doi: 10.1002/jmri.28925

Characterization of Age-Related and Sex-Related Differences of Relaxation Parameters in the Intervertebral Disc Using MR-Fingerprinting

Rajiv G Menon 1,*, Anmol Monga 1,*, Richard Kijowski 1, Ravinder R Regatte 1
PMCID: PMC10935608  NIHMSID: NIHMS1940061  PMID: 37610269

Abstract

Background:

Multi-parameter characterization using MR fingerprinting (MRF) can quantify multiple relaxation parameters of intervertebral disc (IVD) simultaneously. These parameters may vary by age and sex.

Purpose:

To investigate age- and sex-related differences in the relaxation parameters of the IVD of the lumbar spine using a multi-parameter MRF technique.

Study Type:

Prospective.

Subjects:

17 healthy subjects (8 male; mean age=34±10 years, range 20 – 60 years).

Field Strength/Sequence:

3D-MRF sequence for simultaneous acquisition of proton density, T1, T2, and T1p maps at 3.0T.

Assessment:

Global mean T1, T2 and T1ρ of all lumbar IVDs and mean T1, T2 and T1ρ of each individual IVD (L1 – L5) were measured. Gray level co-occurrence matrix was used to quantify textural features (median, contrast, correlation, energy, and homogeneity) from T1, T2, and T1ρ maps.

Statistical Tests:

Spearman rank correlations (R) evaluated the association between age and T1, T2, and T1p of IVD. Mann-Whitney U-tests evaluated differences between males and females in T1, T2 and T1ρ of IVD. Statistical significance was defined as p-value <0.05.

Results:

There was a significant negative correlation between age and global mean values of all IVDs for T1 (R=−0.637), T2 (R=−0.509), and T1ρ (R=−0.726). For individual IVDs, there was a significant negative correlation between age and mean T1 at all IVD segments (R range =−0.530 to −0.708), between age and mean T2 at L2-L3, L3-L4, and L4-L5 (R range=−0.493 to 0.640), and between age and mean T1ρ at all segments except L1-L2 (R range = −0.632 to −0.763). There were no significant differences between sexes in global mean T1, T2, and T1ρ (p-value =0.23–0.76) The texture features with the highest significant correlations with age for all IVDs were global T1ρ mean (R=−0.726), T1 energy (R=−0.681), and T1 contrast (R=0.709).

Conclusion:

This study showed that the 3D-MRF technique has potential to characterize age-related differences in T1, T2, or T1ρ of IVD in healthy subjects.

Keywords: Osteoarthritis, MR Fingerprinting, intervertebral disc, degenerative, tissue characterization, musculoskeletal

INTRODUCTION

Low back pain is a chronic and debilitating condition that affects over half a billion people worldwide and has a major socio-economic impact on society due to a loss of job productivity and high costs for medical treatment [1]. The cause of low back pain in most patients is unknown and is likely multi-factorial[2]. However, one of the major contributors is degeneration of the intervertebral disc (IVD). IVD degeneration is a complex process involving multiple contributing factors including aging, genetics, pathology, and trauma [3]. Quantitative tissue characterization of the IVD has been shown to provide important information for diagnosing the cause of low pain and developing new treatment therapies [4, 5].

MRI is the method of choice for morphological imaging of the IVD due of its superior soft tissue contrast[6]. However, morphologic imaging alone does not provide adequate information regarding tissue changes that occur with IVD degeneration. More recently, quantitative MRI mapping sequences that measure T1, T2, and T1ρ have provided complementary information and insights in research studies[7, 8]. T2 and T1ρ mapping sequences have been most widely used to characterize IVD degeneration, as T2 provides information on water content and collagen organization and T1ρ serves as measure of proteoglycan content [5]. However, current T2 and T1ρ mapping sequences must be performed sequentially with different acquisitions, which results in long scan times.

Conventional quantitative mapping in MRI involves a long acquisition in which one target MR-tissue property is mapped at a time. T1 and T2 quantification using MRF was first described by Ma et al [9] as a novel method to generate maps of multiple MR-related tissue properties in a single acquisition. MR fingerprinting reduces the scan times required to quantify multiple relaxation parameters and result in automatic co-registration among quantitative maps [10, 11]. MRF uses rapidly changing parameter sets to acquire signal ‘fingerprints’, which are then pattern matched to a predefined dictionary of signal evolutions to quantify the multiple parameters simultaneously[10]. More recently multi-parameter MRF quantification methods have been used to assess knee and hip joint cartilage and muscle in human subjects using T1, T2, and T1ρ [12–14]. Reproducibility and repeatability studies have confirmed the utility of this technique to robustly quantify multiple parameters in articular cartilage [12, 14]. While MRF techniques have been widely implemented for brain, abdomen, prostrate, cartilage, and muscle imaging[8, 15, 16], methods used to quantify the IVD have been limited. A recent study compared T1, T2, and T2* in ex-vivo IVD specimens with histological correlation [17], and illustrated the importance of multi-parameter quantification when evaluating the IVD composition and structure.

The purpose of this study was to characterize age-related and sex-related differences in T1, T2, and T1ρ of the IVD of the lumbar spine in a single MRF acquisition in healthy subjects using multi-parameter quantification and textural analysis.

MATERIALS AND METHODS

This study was performed in compliance with the Health Insurance Portability and Accountability Act (HIPAA) regulations and with approval from our Institutional Review Board. All subjects provided written informed consent.

The study cohort consisted of 17 healthy subjects [9 females (34±10yrs), 8 males (34±10yrs), age range=20–60yrs] who underwent an MRF scan of the lumbar spine using a 3T-MRI scanner (Prisma, Siemens Healthcare, Germany). All subjects had no current or prior history of lower back pain, trauma, surgery, or osteoarthritis. When categorizing the subjects by age, there were 10 subjects between 20–30 years, 4 subjects between 30–40 years, 2 subjects between 40–50 years, and 4 subjects between 50–60 years of age. In this study, for characterizing sex-related differences, we are exclusively investigating biological and physiological differences between males and females, and not gender, which is referred to as socially constructed characteristics of women and men.

A 3D implementation of the MRF sequence is shown in Figure 1 [12, 13]. An adiabatic inversion pulse is followed by two fast imaging with steady-state precession (FISP) segments that encode for T1 and T2, where each FISP segment consists of 250 radiofrequency (RF) excitations, and two fast low angle shot (FLASH) segments that encode for T1 and B1. The flip angles (FA) for the FISP and FLASH segments vary from 0° to 20°, and from 0° to 60° for the first and second segments, respectively. This is followed by a T1ρ preparation module followed by 125 RF excitations for each spin lock pulse, with FAs ranging from 0° to 20°. This implementation used 6 spin lock pulses with spin lock times=2, 4, 7, 13, 25, 45ms. Golden angle radial readouts following each RF excitation were used with centric out readout in the kz dimension.

Figure 1:

Figure 1:

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(a) 3D-MRF pulse sequence that was used to simultaneously quantify PD, T1, T2, T1ρ, and B1 of the IVD. (b) Representative examples of PD, T1, T2, T1ρ, and B1 maps of the IVD obtained after iterative reconstruction and pattern matching. (c) ROIs of each individual IVD that were manually segmented. (d) Sub-regional ROIs of the IVD delineated for textural analysis.

The 3D-MRF sequence acquired 10 sagittal slices of the lumbar spine with 4 shots in 10mins. The MRI acquisition parameters included: field of view=240 mm x 240 mm, orientation=sagittal, in-plane voxel resolution= 0.7×0.7 mm2, through plane slice thickness=4mm, repetition time=7.5ms, TE=3.5 ms, bandwidth=500Hz/pixel, frequency of spin lock =500Hz. Extended phase graph simulations were performed to compute a dictionary of simulated MR fingerprints with a T1 range of 50–3000ms, T2 range of 2–200ms, and T1ρ range of 2–200ms in steps of 6% [18]. Singular value decomposition (SVD) compression was used to speed up the reconstruction [19], which was performed offline. An iterative dictionary pattern matching algorithm was used to produce quantitative maps of proton density (PD), T1, T2, T1ρ, and B1 (figure 1(b)).

The segmentation of the intervertebral disc (IVD) was carried out manually on proton density maps, which were generated using the Magnetic Resonance Fingerprinting (MRF) technique. To accomplish this, the Volume Segmented toolbox in MATLAB was utilized. Through this process, the boundaries of the IVD were identified and delineated on the proton density maps. Manual segmentation and quantitative mapping was performed by two independent readers (reader1:R.G.M., 10 years of experience in medical imaging, and reader2: A.M., 6 years of experience in medical imaging). Figure 1(c) shows the global IVD segments that were delineated. Global mean T1, T2, and T1ρ of all IVDs and mean T1, T2, and T1ρ of each individual IVD were measured based on these regions of interest (ROIs). Textural analysis of T1, T2, and T1ρ of the IVD was also performed to investigate additional parameters that may be associated with age-related and sex-related changes. To investigate sub-regional changes of the IVD, the ROIs were further segmented to delineate the nucleus pulposus (NP), the posterior annulus fibrosus (PAF), and the anterior annulus fibrous (AAF) as shown in figure 1(d). The sub-regions of the intervertebral disc (IVD) were segmented using a semi-automated approach. The NP-AF length ratio and NP height/Length ratios, as defined in [22], were employed to determine the length and height of the NP, PAF, and AAF. Based on these calculations, ellipses representing the dimensions of the NP were constructed in each sagittal slice. To ensure accurate segmentation, the orientation and size of the sub-regions were manually adjusted, aligning them with the orientation of the manually segmented IVD. The remaining IVD region was then divided between the PAF and AAF segments. This comprehensive process enhances the precision of IVD sub-region segmentation. Mean T1, T2, and T1ρ and gray level co-occurrence matrix (GLCM) texture features of T1, T2, and T1ρ were measured in the subregion ROIs for each individual IVD [5]. The GLCM features used for textural analysis included median, contrast, correlation, energy, and homogeneity.

Statistical Analysis

Statistical analysis was performed using the MATLAB 2021b program environment (Mathworks, Inc., Natick, Massachusetts) with non-parametric tests due to the small sample size. Statistical significance was defined as a p-value less than 0.05. Spearman rank correlation coefficients (R) were used to evaluate the association between age and T1, T2 and T1ρ of the IVD. Mann-Whitney tests were used to evaluate differences between males and females in T1, T2, and T1ρ of the IVD. To minimize the number of comparisons, statistical analysis was first used to investigate the primary hypothesis, which was to determine the association between age and sex and global mean T1, T2, and T1ρ of all IVDs. Exploratory analysis was then performed for parameters significantly associated with global mean T1, T2, and T1ρ to investigate the association between age and sex and mean T1, T2, and T1ρ of each individual IVD and mean T1, T2, and T1ρ and texture features of T1, T2, and T1ρ in the subregion ROIs of each individual IVD.

RESULTS

Figure 2(a) demonstrates age comparison showing multi-parameter PD, T1, T2, T1ρ, and B1 maps from representative subjects from different arbitrarily chosen age groups (20–30yrs, 31–40yrs, 41–50yrs, 51–60yrs). Figure 2(b) demonstrates sex comparison showing multi-parameter PD, T1, T2, T1ρ, and B1 maps from a representative male and female subject.

Figure 2:

Figure 2:

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(a) Multi-parameter PD, T1, T2, and T1ρ, and B1 maps from representative subjects from different arbitrarily chosen age groups. (b) Multi-parameter PD, T1, T2, and T1ρ, and B1 maps from a representative male and female subject.

Figure 3 shows regression plots between age and global mean T1, T2, and T1ρ and mean T1, T2, and T1ρ of each individual IVD with the Spearman correlation coefficients and p-values provided in Table 1. There was a significant negative correlation between global mean values of all IVDs for T1 (R=−0.637), T2 (R=−0.509), and T1ρ (R=−0.726). For the individual IVDs, there was a significant negative correlation between age and mean T1 at all IVD segments (R range between −0.530 to −0.708), between age and mean T2 at L2-L3, L3-L4, and L4-L5 (R range=−0.493 to 0.640), and between age and mean T1ρ at all IVD segments except L1-L2 (R range = −0.632 to −0.763). Table 2 shows the intra-class correlation coefficient as a measure of inter-reader agreement with an average value of 0.91 indicating an excellent agreement between two readers for IVD segmentation, relaxation mapping and textural features.

Figure 3:

Figure 3:

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Regression plots showing the association between age and global mean T1, T2, and T1ρ of all IVDs and mean T1, T2, and T1ρ of the IVDs at each individual level.

Table 1:

Spearman correlation coefficients with p-values for the association between age and global mean T1, T2, and T1ρ of all IVDs and mean T1, T2, and T1ρ of each individual IVD. Significant p-values are marked in bold.

L1-L2 L2-L3 L3-L4 L4-L5 L5-S1 Global
T1 −0.606 −0.708 −0.530 −0.590 −0.574 −0.637
T2 −0.043 −0.568 −0.493 −0.640 −0.373 −0.509
T1ρ −0.191 −0.763 −0.632 −0.703 −0.539 −0.726
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Table 2:

Inter-reader agreement of manual ROI segmentation and sub-regional analysis results indicated by intraclass correlation coefficient (ICC) and 95% confidence intervals (CI)

T1 T2 T1ρ
NP AAF PAF NP AAF PAF NP AAF PAF
ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB] ICC 95 % CI [LB, UB]
Mean 0.91 [0.22,0.99] 0.90 [0.52, 0.99 0.88 [0.65, 0.99] 0.95 [0.31, 0.99] 0.83 [0.48, 0.99] 0.91 [0.52, 0.99] 0.92 [0.32, 0.95] 0.86 [0.55, 0.99] 0.89 [0.55, 0.99]
Median 0.90 [0.08,0.99] 0.88 [0.79, 0.99] 0.95 [0.44, 0.99] 0.91 [0.05, 0.99] 0.89 [0.44, 0.99] 0.9 [0.41, 0.99] 0.88 [0.05, 0.95] 0.92 [0.44, 0.99] 0.94 [0.45, 0.95]
Contrast 0.94 [0.56, 0.99] 0.90 [0.63, 0.99] 0.91 [0.12, 0.99] 0.95 [0.65, 0.99] 0.95 [0.75, 0.99] 0.96 [0.15, 0.99] 0.94 [0.55, 0.94] 0.92 [0.65, 0.94] 0.97 [0.10, 0.96]
Correlation 0.96 [0.05,0.99] 0.92 [0.41, 0.99] 0.99 [0.15, 0.99] 0.92 [0.04, 0.99] 0.94 [0.46, 0.99] 0.89 [0.16, 0.99] 0.91 [0.04, 0.99] 0.94 [0.44, 0.97] 0.85 [0.18, 0.99]
Energy 0.92 [0.24, 0.99] 0.92 [0.43, 0.99] 0.95 [0.06, 0.99] 0.93 [0.32, 0.99] 0.94 [0.42, 0.99] 0.85 [0.04, 0.99] 0.86 [0.54, 0.97] 0.89 [0.55, 0.99] 0.95 [0.05, 0.99]
Homogeneity 0.95 [0.23, 0.99] 0.93 [0.09, 0.99] 0.88 [0.23, 0.99] 0.94 [0.21, 0.99] 0.95 [0.01, 0.99] 0.91 [0.24, 0.99] 0.92 [0.22, 0.99] 0.92 [0.95, 0.99] 0.91 [0.24, 0.99]
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Figure 4 shows box plots illustrating the differences in global mean T1, T2, and T1ρ of all IVDs between male and female subjects. There were no significant differences between male and female subjects in global mean T1 (1029 ms ±107 ms for males and 1041 ms ± 210 ms for females; p = 0.54), T2 (37 ms ± 4 ms for males and 33 ms ± 7 ms for females; p=0.32) or T1ρ (68 ms ± 8 ms for males and 61 ms ± 16 ms for females; p=0.32).

Figure 4:

Figure 4:

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Boxplots showing the differences between male and female subjects for global mean T1, T2, and T1ρ of all IVDs.

Figure 5 shows the Spearman correlation coefficients and p-values for the association between age and global mean T1, T2, and T1ρ and global texture features of T1, T2, and T1ρ of all IVDs. For T1 textural analysis, there were significant negative correlations between age and mean T1 (R=−0.637) and contrast T1 (R=−0.709) and significant positive correlations between age and energy T1 (R=0.681) and homogeneity T1 (R=0.693). For T2 textural analysis, there was a significant negative correlation between age and mean T2 (R=−0.509). For T1ρ textural analysis, there was a significant negative correlation between age and mean T1ρ (R=−0.726) and median T1ρ (R=−0.566).

Figure 5:

Figure 5:

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Regression plots showing association of age and global textural features of T1, T2, and T1ρ for all IVDs. The table shows Spearman correlation coefficients and p-values for the association between age and global texture features of T1, T2, and T1ρ for all IVDs.

Figures 6, 7, and 8 show regression plots, Spearman correlation coefficients, and p-values for the association between age and mean T1, T2, and T1ρ and texture features of T1, T2, and T1ρ for the NP, PAF, and AAF for each individual IVD. For all features, the correlations with age were higher in the NP than PAF and AAF. Furthermore, texture features of T1 were more strongly correlated with age than texture features of T2 and T1ρ with significant correlations between contrast T1 and homogeneity T1 present in the AAF as well as the NP. The texture features with the highest correlation with age were contrast T1 in the NP at L2-L3 (R=−0.826) followed by homogeneity T1 in the NP at L2-L3 (R=0.799), energy T1 in the AAF at L2-L3 (R=0.728), homogeneity T1 in the NP at L4-L5 (R=0.725), and contrast T1 in the NP at L4-L5 (R=0.715).

Figure 6:

Figure 6:

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Regression plots showing the association between age and textural features of T1 for subregions (NP, PAF, and AAF) of each individual IVD. Spearman correlation coefficients for each subregion are tabulated with significant p-values marked in bold. NP-nucleus pulposus; PAF-posterior annulus fibrosus; AAF-anterior annulus fibrosus.

Figure 7:

Figure 7:

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Regression plots showing the association between age and textural features of T2 for subregions (NP, PAF, and AAF) of each individual IVD. Spearman correlation coefficients for each subregion are tabulated with significant p-values marked in bold. PAF-posterior annulus fibrosus; AAF-anterior annulus fibrosus.

Figure 8:

Figure 8:

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Regression plots showing the association between age and textural features of T1ρ for subregions (NP, PAF, and AAF) of each individual IVD. Spearman correlation coefficients for each subregion are tabulated with significant p-values marked in bold. PAF-posterior annulus fibrosus; AAF-anterior annulus fibrosus.

DISCUSSION

This study investigated age-related and sex-related differences in T1, T2 and T1ρ of IVD of the lumbar spine in healthy subjects measured simultaneously using the 3D-MRF technique. There were significant negative correlations between age and mean T1, T2 and T1ρ of IVD. However, there were no significant differences between males and females in mean T1, T2 and T1ρ of IVD. Our results document the influence of aging on the composition and structure of IVD and demonstrate the use of the 3D-MRF technique for in-vivo evaluation of IVD. The use of multi-parameter mapping techniques like MR fingerprinting result in significant scan time savings. The current implementation of MRF that quantifies T1, T2 and T1ρ took 10 minutes to acquire compared to a sequential mapping of T1, T2 and T1ρ would take 25 minutes to acquire.

Asymptomatic IVD degeneration typically begins in the third decade of life and is characterized by decreased proteoglycan and water content within the nucleus pulposus [2, 20, 21]. This may be responsible for the stronger inverse correlations between age and mean T1, T2 and T1ρ of the nucleus pulposus than annular fibrosis in our study. In addition, mean T1ρ of the IVD had the strongest correlation with age, which suggests that decreased proteoglycan content of the IVD measured with T1ρ is more strongly affected during the aging process than decreased free water content measured with T1 and T2. Loss of hydrophilic proteoglycans in the nucleus pulpous is the first step in IVD degeneration, which reduces the fixed charge density of IVD [20, 22]. The loss of fixed charge density subsequently results in a decrease in the free water content and osmotic pressure of IVD, which alters its mechanical properties and ability to withstand loads [20, 22–24].

Our results agree with the findings of previous studies [4, 5, 25]. Wu et al found a significant negative correlation between age and T2 of IVD of the lumbar spine in 69 healthy subjects [25], while Bonnheim et al reported a significant negative correlation between age and T1p of IVD in 56 healthy subjects [16]. Menezes-Reis et al found a significant negative correlation between age and T2 but not T1p of IVD of the lumbar spine in 90 healthy subjects [4]. However, this study recruited only healthy adults between 20 and 40 years old, which may be the reason that a significant correlation between age and T1p of IVD was not observed. In addition, multiple studies have reported significantly lower T2 and T1p of IVD of the lumbar spine in subjects with low back pain compared to healthy control subjects, which indicates that similar patterns of change in T2 and T1ρ occur in degenerative IVD as senescent IVD [5, 23, 26].

Our study also investigated age-related differences in the composition and structure of IVD using textural analysis of T1, T2, and T1ρ. Energy T1 and contrast T1 were the texture features with the strongest correlations with age in our study. The lower T1 energy and higher T1 contrast of IVD in older subjects both reflect greater variations in T1 in adjacent voxels, which are likely due to more spatially heterogeneous changes in the composition and structure of the IVD [27]. The correlations between age and texture features in our study were stronger for the nucleus pulposus than annular fibrosis and stronger for T1 than T2 and T1ρ. This indicates that spatially heterogeneous changes especially occur in the high frequency interactions between water and proteoglycan in the nucleus pulposus, which is measured with T1[8]. However, significant correlations between age and energy T1 and contrast T1 were also found for the annulus fibrosis, indicating some degree of spatially heterogeneous changes in the high frequency interactions between water and collagen macromolecules in the annulus fibrosis during the aging process.

Our study found no significant differences between males and females in T1, T2, and T1ρ of IVD. Our results are similar to the findings of Menezes-Reis et al who reported no sex-related differences in T2 and T1p of IVD of the lumbar spine in 90 healthy subjects between 20 and 40 years of age [4]. However, in a study involving 63 healthy subjects under 25 years of age, Zobel et al found a significantly lower T1p of IVD of the lumbar spine in females compared to males, indicating a lower proteoglycan content of IVD [26]. Studies have shown that females have a higher prevalence of low back pain with more severe pain and disability and greater co-morbidities [28] and higher rates of disc height loss of the lumber spine on radiographs [29] compared to males. However, there are conflicting results in the literature on whether the higher prevalence and greater clinical burden of IVD degeneration in females is due to baseline inherent sex-related differences in the composition and structure of IVD assessed using quantitative MRI[4, 26].

Limitations

First, the number of subjects was small and skewed bimodally towards younger and older individuals. Furthermore, the degree of IVD degeneration in our subjects could not be assessed due to absence of morphologic T2-weighted images in the MRI protocol to assign a Pfirrmann grade. In addition, our study did not compare the utility of the 3D-MRF technique with conventional methods used to individually measure T1, T2 and T1ρ for assessing age-related and sex-related differences of IVD. Finally, our study did not include histologic correlation, and thus, the mechanisms behind the observed changes in T1, T2 and T1ρ of IVD with increasing age could not be investigated.

CONCLUSION

Our study documented the ability of the 3D-MRF technique to characterize age-related differences in the composition and structure of IVD of the lumbar spine in healthy subjects. Our results showed significant negative correlations between age and mean T1, T2, and T1ρ of IVD, which were stronger for the nucleus pulposus than the annulus fibrosis. This may indicate that aging of IVD is primarily characterized by a decrease in proteoglycan and water content of the nucleus pulposus. Our study also documented the ability of T1 textural analysis to detect age-related increases in the spatial heterogeneity of high frequency interactions between water and proteoglycans in the nucleus pulposus. The 3D-MRF technique has potential to provide a more time-efficient comprehensive evaluation of the composition and structure of IVD in patients with low back pain to better characterize disease severity and monitor treatment response.

Grant Support:

This study was supported by NIH grants R21-AR078357, R01-AR076328-01A1, R01-AR076985-01A1, and R01-AR078308-01A1, and was performed under the rubric of the Center of Advanced Imaging Innovation and Research (CAI2R) at NYU Grossman School of Medicine, a NIBIB Biomedical Technology Resource Center (NIH P41 EB017183).

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