3D-T1ρ Mapping Research Articles

3D B1+ corrected simultaneous myocardial T1 and T1ρ mapping with subject-specific respiratory motion correction and water-fat separation.

To develop a 3D free-breathing cardiac multi-parametric mapping framework that is robust to confounders of respiratory motion, fat, and B1+ inhomogeneities and validate it for joint myocardial T1 and T1ρ mapping at 3T. An electrocardiogram-triggered sequence with dual-echo Dixon readout was developed, where nine cardiac cycles were repeatedly acquired with inversion recovery and T1ρ preparation pulses for T1 and T1ρ sensitization. A subject-specific respiratory motion model relating the 1D diaphragmatic navigator to the respiration-induced 3D translational motion of the heart was constructed followed by respiratory motion binning and intra-bin 3D translational and inter-bin non-rigid motion correction. Spin history B1+ inhomogeneities were corrected with optimized dual flip angle strategy. After water-fat separation, the water images were matched to the simulated dictionary for T1 and T1ρ quantification. Phantoms and 10 heathy subjects were imaged to validate the proposed technique. The proposed technique achieved strong correlation (T1: R2 = 0.99; T1ρ: R2 = 0.98) with the reference measurements in phantoms. 3D cardiac T1 and T1ρ maps with spatial resolution of 2 × 2 × 4 mm were obtained with scan time of 5.4 ± 0.5 min, demonstrating comparable T1 (1236 ± 59 ms) and T1ρ (50.2 ± 2.4 ms) measurements to 2D separate breath-hold mapping techniques. The estimated B1+ maps showed spatial variations across the left ventricle with the septal and inferior regions being 10%-25% lower than the anterior and septal regions. The proposed technique achieved efficient 3D joint myocardial T1 and T1ρ mapping at 3T with respiratory motion correction, spin history B1+ correction and water-fat separation.

Performance of MR learned pulse sequences for 3D bi-exponential, stretched-exponential, and mono-exponential T2 and T1ρ mapping of knee cartilage.

To compare the performance of a learned magnetization-prepared gradient echo (L-MPGRE) sequence against a commonly used sequence for 3D T2 and T1ρ mapping of the knee joint, the magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots (MAPSS), on bi-exponential (BE), stretched-exponential (SE), and mono-exponential (ME) relaxation models. We used a combined differentiable and non-differentiable optimization to learn pulse sequence structure and its parameters for 3D T2 and T1ρ mapping of the knee joint using ME, SE, and BE models. The learned pulse sequence framework was used to improve quantitative accuracy and SNR and to reduce filtering effects. We compare the measured multi-compartment values between the two sequences (n = 8), and their repeatability (n = 4) in healthy volunteers (n = 12 total). The voxel-wise median absolute percentage difference (MAPD) between the T2 and T1ρ maps obtained with each sequence was 18.6% and 19.9%, respectively. The T2 and T1ρ repeatability tests showed a MAPD of 18.5% and 19.1% for MAPSS, and 16.8% and 15.5% for L-MPGRE. Bland-Altman region of interest (ROI)-wise analysis shows that bias is small, close to -1.5%, and the coefficient of variation is around 5.5% when comparing ROIs from both sequences. The L-MPGRE sequences can be used as a replacement for MAPSS for T2 and T1ρ mapping in the knee cartilage with advantages, achieving similar accuracy and 15% better repeatability in only half of its scan time.

Optimizing variable flip angles in magnetization-prepared gradient-echo sequences for efficient 3D-T1ρ mapping.

To optimize the choice of the flip angles of magnetization-prepared gradient-echo sequences for improved accuracy, precision, and speed of 3D-T1ρ mapping. We propose a new optimization approach for finding variable flip-angle values that improve magnetization-prepared gradient-echo sequences used for 3D-T1ρ mapping. This new approach can improve the accuracy and SNR, while reducing filtering effects. We demonstrate the concept in the three different versions of the magnetization-prepared gradient-echo sequences that are typically used for 3D-T1ρ mapping and evaluate their performance in model agarose phantoms (n = 4) and healthy volunteers (n = 5) for knee joint imaging. We also tested the optimization with sequence parameters targeting faster acquisitions. Our results show that optimized variable flip angle can improve the accuracy and the precision of the sequences, seen as a reduction of the mean of normalized absolute difference from about 5%-6% to 3%-4% in model phantoms and from 15%-16% to 11%-13% in the knee joint, and improving SNR from about 12-28 to 22-32 in agarose phantoms and about 7-14 to 13-17 in healthy volunteers. The optimization can also compensate for the loss in quality caused by making the sequence faster. This results in sequence configurations that acquire more data per unit of time with SNR and mean of normalized absolute difference measurements close to its slower versions. The optimization of the variable flip angle can be used to increase accuracy and precision, and to improve the speed of the typical imaging sequences used for quantitative 3D-T1ρ mapping of the knee joint.

Data-driven optimization of sampling patterns for MR brain T1ρ mapping.

The goal of this study was to apply a fast data-driven optimization algorithm, called bias-accelerated subset selection, for MR brain T1ρ mapping to generate optimized sampling patterns (SPs) for compressed sensing reconstruction of brain 3D-T1ρ MRI. Five healthy volunteers were recruited, and fully sampled Cartesian 3D-T1ρ MRIs were obtained. Variable density (VD) and Poisson disc (PD) undersampling was used as the input to SP optimization process. The reconstruction used 3 compressed sensing methods: spatiotemporal finite differences, low-rank plus sparse with spatial finite differences, and low rank. The performance of images and T1ρ maps using PD-SP and VD-SP and their optimized sampling patterns (PD-OSP and VD-OSP) were compared to the fully sampled reference using normalized root mean square error (NRMSE). The VD-OSP with spatiotemporal finite differences reconstruction (NRMSE=0.078) and the PD-OSP with spatiotemporal finite differences reconstruction (NRMSE=0.079) at the highest acceleration factors (AF=30) showed the largest improvement compared to the respective nonoptimized SPs (VD NRMSE=0.087 and PD NRMSE=0.149). Prospective undersampling was tested at AF=4, with VD-OSP NRMSE=0.057 versus PD-OSP NRMSE=0.060, with optimized sampling performing better that input PD or VD sampling. For brain T1ρ mapping, the VD-OSP with low rank reconstruction for AFs <10 and VD-OSP with spatiotemporal finite differences for AFs >10 perform better. The study demonstrated that the appropriate use of data-driven optimized sampling and suitable compressed sensing reconstruction technique can be employed to potentially accelerate 3D T1ρ mapping for brain imaging applications.

Accelerated 3D free-breathing high-resolution myocardial T1ρ mapping at 3Tesla.

To develop a fast free-breathing whole-heart high-resolution myocardial T1ρ mapping technique with robust spin-lock preparation that can be performed at 3Tesla. An adiabatically excited continuous-wave spin-lock module, insensitive to field inhomogeneities, was implemented with an electrocardiogram-triggered low-flip angle spoiled gradient echo sequence with variable-density 3D Cartesian undersampling at a 3 Tesla whole-body scanner. A saturation pulse was performed at the beginning of each cardiac cycle to null the magnetization before T1ρ preparation. Multiple T1ρ -weighted images were acquired with T1ρ preparations with different spin-lock times in an interleaved fashion. Respiratory self-gating approach was adopted along with localized autofocus to enable 3D translational motion correction of the data acquired in each heartbeat. After motion correction, multi-contrast locally low-rank reconstruction was performed to reduce undersampling artifacts. The accuracy and feasibility of the 3D T1ρ mapping technique was investigated in phantoms and in vivo in 10 healthy subjects compared with the 2D T1ρ mapping. The 3D T1ρ mapping technique provided similar phantom T1ρ measurements in the range of 25-120 ms to the 2D T1ρ mapping reference over a wide range of simulated heart rates. With the robust adiabatically excited continuous-wave spin-lock preparation, good quality 2D and 3D in vivo T1ρ -weighted images and T1ρ maps were obtained. Myocardial T1ρ values with the 3D T1ρ mapping were slightly longer than 2D breath-hold measurements (septal T1ρ : 52.7 ± 1.4ms vs. 50.2 ± 1.8ms, P < 0.01). A fast 3D free-breathing whole-heart T1ρ mapping technique was proposed for T1ρ quantification at 3T with isotropic spatial resolution (2 mm3 ) and short scan time of ∼4.5min.

A pilot study with 3D T1ρ-mapping for quantitative evaluation of cartilage after open-wedge high tibial osteotomy

PurposeTo evaluate the recently developed three-dimensional (3D) T1ρ-mapping technique for the quantitative evaluation of cartilage changes after open-wedge high tibial osteotomy (OWHTO). MethodEleven patients with medial knee osteoarthritis and spontaneous osteonecrosis of the medial femoral condyle underwent T1ρ magnetic resonance imaging. The full-thickness cartilage of the medial femoral condyle (MFC), lateral femoral condyle, medial tibial condyle, and lateral tibial condyle, and the trochlea and patella were set as regions of interest; mean T1ρ values were calculated using two-dimensional (2D) and 3D analyses. The state of cartilage injury was evaluated using the International Cartilage Repair Society grading system (ICRS grade). T1ρ values before OWHTO and after hardware removal were compared using the Wilcoxon signed-rank test. The correlation between T1ρ values and the ICRS grade was explored using the Spearman rank correlation coefficient. ResultsCartilage repair at the MFC was observed in a high proportion of patients (82%), but 2D analysis did not reveal a significant difference after surgery at any site. In contrast, 3D analysis revealed a significant decrease in T1ρ values after surgery at the MFC (before surgery: 44.1 ms; after surgery: 38.9 ms; p = 0.016), but no significant difference at any other site. In the MFC, there was a positive correlation between 3D T1ρ values and arthroscopic findings before and after surgery (rs = 0.62, p < 0.01), which was absent from the 2D T1ρ values. Conclusions3D T1ρ-mapping may be an effective tool for detecting changes in cartilage quality after OWHTO.

Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model.

Magnetic resonance (MR) quantitative T1ρ imaging has been increasingly used to detect the early stages of osteoarthritis. The small volume and curved surface of articular cartilage necessitate imaging with high in-plane resolution and thin slices for accurate T1ρ measurement. Compared with 2D T1ρ mapping, 3D T1ρ mapping is free from artifacts caused by slice cross-talk and has a thinner slice thickness and full volume coverage. However, this technique needs to acquire multiple T1ρ-weighted images with different spin-lock times, which results in a very long scan duration. It is highly expected that the scan time can be reduced in 3D T1ρ mapping without compromising the T1ρ quantification accuracy and precision. To accelerate the acquisition of 3D T1ρ mapping without compromising the T1ρ quantification accuracy and precision, a signal-compensated robust tensor principal component analysis method was proposed in this paper. The 3D T1ρ-weighted images compensated at different spin-lock times were decomposed as a low-rank high-order tensor plus a sparse component. Poisson-disk random undersampling patterns were applied to k-space data in the phase- and partition-encoding directions in both retrospective and prospective experiments. Five volunteers were involved in this study. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled at R=5.2, 7.7, and 9.7, respectively. Reference values were obtained from the fully sampled data. Prospectively undersampled data for R=5 and R=7 were acquired from 2 volunteers. Bland-Altman analyses were used to assess the agreement between the accelerated and reference T1ρ measurements. The reconstruction performance was evaluated using the normalized root mean square error and the median of the normalized absolute deviation (MNAD) of the reconstructed T1ρ-weighted images and the corresponding T1ρ maps. T1ρ parameter maps were successfully estimated from T1ρ-weighted images reconstructed using the proposed method for all accelerations. The accelerated T1ρ measurements and reference values were in good agreement for R=5.2 (T1ρ: 40.4±1.4 ms), R=7.7 (T1ρ: 40.4±2.1 ms), and R=9.7 (T1ρ: 40.9±2.2 ms) in the Bland-Altman analyses. The T1ρ parameter maps reconstructed from the prospectively undersampled data also showed promising image quality using the proposed method. The proposed method achieves the 3D T1ρ mapping of in vivo knee cartilage in eight minutes using a signal-compensated robust tensor principal component analysis method in image reconstruction.

Rapid mono and biexponential 3D-T1\u03c1 mapping of knee cartilage using variational networks

In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin–lattice relaxation time in the rotating frame (T1ρ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T1ρ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T1ρ parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T1ρ mapping, with MNAD around 5% for AF = 2, which increases almost linearly with the AF to an MNAD of 13% for AF = 8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4% for AF = 2 and reaching MNAD of 13.1% for AF = 8. The VN was able to produce 3D-T1ρ mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5%.

Three-Dimensional GRE T1ρ mapping of the brain using tailored variable flip-angle scheduling.

To introduce a new approach called tailored variable flip-angle (VFA) scheduling for SNR-efficient 3D T1ρ mapping of the brain using a magnetization-prepared gradient-echo sequence. Simulations were used to assess the relative SNR efficiency, quantitative accuracy, and spatial blurring of tailored VFA scheduling for T1ρ mapping of brain tissue compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS), a state-of-the-art technique for accurate 3D gradient-echo T1ρ mapping. Simulations were also used to calculate optimal imaging parameters for tailored VFA scheduling versus MAPSS, without and with nulling of CSF. Four participants were imaged at 3T MRI to demonstrate the feasibility of tailored VFA scheduling for T1ρ mapping of the brain. Using MAPSS as a reference standard, in vivo data were used to validate the relative SNR efficiency and quantitative accuracy of the new approach. Tailored VFA scheduling can provide a 2-fold to 4-fold gain in the SNR of the resulting T1ρ map as compared with MAPSS when using identical sequence parameters while limiting T1ρ quantification errors to 2% or less. In vivo whole-brain 3D T1ρ maps acquired with tailored VFA scheduling had superior SNR efficiency than is achievable with MAPSS, and the SNR efficiency improved with a greater number of views per segment. Tailored VFA scheduling is an SNR-efficient GRE technique for 3D T1ρ mapping of the brain that provides increased flexibility in choice of imaging parameters compared with MAPSS, which may benefit a variety of applications.

Accelerated mono- and biexponential 3D-T1ρ relaxation mapping of knee cartilage using golden angle radial acquisitions and compressed sensing.

To use golden-angle radial sampling and compressed sensing (CS) for accelerating mono- and biexponential 3D spin-lattice relaxation time in the rotating frame (T1ρ ) mapping of knee cartilage. Golden-angle radial stack-of-stars and Cartesian 3D-T1ρ -weighted knee cartilage datasets (n = 12) were retrospectively undersampled by acceleration factors (AFs) 2-10. CS-based reconstruction using 8 different sparsifying transforms were compared for mono- and biexponential T1ρ -mapping of knee cartilage, including spatio-temporal finite differences, wavelets, dictionary from principal component analysis, and exponential decay models, and also low rank and low rank plus sparse models (L+S). Complex-valued fitting was used and Marchenko-Pastur principal component analysis filtering also tested. Most CS methods performed well for an AF of 2, with relative median normalized absolute deviation below 10% for monoexponential and biexponential mapping. For monoexponential mapping, radial sampling obtained a median normalized absolute deviation below 10% up to AF of 10, while Cartesian obtained this level of error only up to AF of 4. Radial sampling was also better with biexponential T1ρ mapping, with median normalized absolute deviation below 10% up to AF of 6. Golden-angle radial acquisitions combined with CS outperformed Cartesian acquisitions for 3D-T1ρ mapping of knee cartilage, being it is a good alternative to Cartesian sampling for reducing scan time and/or improving image and mapping quality. The methods exponential decay models, spatio-temporal finite differences, and low rank obtained the best results for radial sampling patterns.

Compressed sensing acceleration of biexponential 3D-T1ρ relaxation mapping of knee cartilage.

Use compressed sensing (CS) for 3D biexponential spin-lattice relaxation time in the rotating frame (T1ρ ) mapping of knee cartilage, reducing the total scan time and maintaining the quality of estimated biexponential T1ρ parameters (short and long relaxation times and corresponding fractions) comparable to fully sampled scans. Fully sampled 3D-T1ρ -weighted data sets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared for biexponential T1ρ -mapping of knee cartilage, including temporal and spatial wavelets and finite differences, dictionary from principal component analysis (PCA), k-means singular value decomposition (K-SVD), exponential decay models, and also low rank and low rank plus sparse models. Synthetic phantom (N=6) and in vivo human knee cartilage data sets (N=7) were included in the experiments. Spatial filtering before biexponential T1ρ parameter estimation was also tested. Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative median normalized absolute deviation (MNAD) around 10%. Some sparsifying transforms, such as low rank with spatial finite difference (L+S SFD), spatiotemporal finite difference (STFD), and exponential dictionaries (EXP) significantly improved this performance, reaching MNAD below 15% with AF up to 10, when spatial filtering was used. Accelerating biexponential 3D-T1ρ mapping of knee cartilage with CS is feasible. The best results were obtained by STFD, EXP, and L+S SFD regularizers combined with spatial prefiltering. These 3 CS methods performed satisfactorily on synthetic phantom as well as in vivo knee cartilage for AFs up to 10, with median error below 15%.

Imaging muscle stiffness post-stroke

Post-stroke spasticity develops a result of CNS injury, but secondary non-neural changes can contribute to upper limb muscle stiffness. The hylauronan hypothesis postulates that an increase in the concentration of hyaluronan, a glycosaminoglycan (GAG) in the extra-cellular matrix (ECM) of muscles, may contribute to muscle stiffness in spastic patients. This study sought to provide imaging evidence of the hyaluronan hypothesis. Five post-stroke patients (3 males, 2 females, age = 46 ± 15) and five healthy control subjects (3 males, 2 females, age = 27 ± 2) underwent Dixon based water-fat imaging and 3D-T1rho relaxation mapping of upper arm muscles were used to determine intramuscular GAG content. The MRI was study was conducted on a 3 T clinical scanner (Prisma, Siemens Healthcare, Germany). Following the MRI, the patients underwent off-label intramuscular hyaluronidase injections (Hylenex, Halozyme Therapeutics, Inc.) for clinical treatment. Post-injection MRIs were conducted approximately two weeks after the treatment. Two tailed student t -tests were used for comparing the groups. A paired t -test was used for the pre- and post-injection comparison. Following the hyaluronidase injections patients reported significant relief from muscle stiffness and showed increased arm movement. The hyaluronan concentration in the biceps and triceps was significantly higher in patients pre-injection, and was reduced post-injection. Note the change in muscle shape from triangular to more circular as in controls post-injection in the patient. 3D T1ρ mapping of the muscle can be used to detect intramuscular GAG content (hyaluronan) non-invasively. The muscle micro-environment can contribute to muscle stiffness, and injections of the enzyme hyaluronidase can be used to restore normal muscle microenvironment and reduce muscle stiffness post-stroke.

Cartilage Endplate Thickness Variation Measured by Ultrashort Echo-Time MRI Is Associated With Adjacent Disc Degeneration.

A magnetic resonance imaging study of human cadaver spines. To investigate associations between cartilage endplate (CEP) thickness and disc degeneration. Damage to the CEP is associated with spinal injury and back pain. However, CEP morphology and its association with disc degeneration have not been well characterized. Ten lumbar motion segments with varying degrees of disc degeneration were harvested from six cadaveric spines and scanned with magnetic resonance imaging in the sagittal plane using a T2-weighted two-dimensional (2D) sequence, a three-dimensional (3D) ultrashort echo-time (UTE) imaging sequence, and a 3D T1ρ mapping sequence. CEP thicknesses were calculated from 3D UTE image data using a custom, automated algorithm, and these values were validated against histology measurements. Pfirrmann grades and T1ρ values in the disc were assessed and correlated with CEP thickness. The mean CEP thickness calculated from UTE images was 0.74 ± 0.04 mm. Statistical comparisons between histology and UTE-derived measurements of CEP thickness showed significant agreement, with the mean difference not significantly different from zero (P = 0.32). Within-disc variation of T1ρ (standard deviation) was significantly lower for Pfirrmann grade 4 than Pfirrmann grade 3 (P < 0.05). Within-disc variation of T1ρ and adjacent CEP thickness heterogeneity (coefficient of variation) had a significant negative correlation (r = -0.65, P = 0.04). The standard deviation of T1ρand the mean CEP thickness showed a moderate positive correlation (r = 0.40, P = 0.26). This study demonstrates that quantitative measurements of CEP thickness measured from UTE magnetic resonance imaging are associated with disc degeneration. Our results suggest that variability in CEP thickness and T1ρ, rather than their mean values, may serve as valuable diagnostic markers for disc degeneration. N/A.

Accelerating 3D-T1ρ mapping of cartilage using compressed sensing with different sparse and low rank models.

To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D-T1ρ mapping of cartilage and to reduce total scan times without degrading the estimation of T1ρ relaxation times. Fully sampled 3D-T1ρ datasets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared, including finite differences, temporal and spatial wavelets, learned transforms using principal component analysis (PCA) and K-means singular value decomposition (K-SVD), explicit exponential models, low rank and low rank plus sparse models. Spatial filtering prior to T1ρ parameter estimation was also tested. Synthetic phantom (n = 6) and in vivo human knee cartilage datasets (n = 7) were included. Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative T1ρ error lower than 4.5%. Some sparsifying transforms, such as spatiotemporal finite difference (STFD), exponential dictionaries (EXP) and low rank combined with spatial finite difference (L+S SFD) significantly improved this performance, reaching average relative T1ρ error below 6.5% on T1ρ relaxation times with AF up to 10, when spatial filtering was used before T1ρ fitting, at the expense of smoothing the T1ρ maps. The STFD achieved 5.1% error at AF = 10 with spatial filtering prior to T1ρ fitting. Accelerating 3D-T1ρ mapping of cartilage with CS is feasible up to AF of 10 when using STFD, EXP or L+S SFD regularizers. These three best CS methods performed satisfactorily on synthetic phantom and in vivo knee cartilage for AFs up to 10, with T1ρ error of 6.5%.

Bone marrow edema-like lesions (BMELs) are associated with higher T1ρ and T2 values of cartilage in anterior cruciate ligament (ACL)-reconstructed knees: a longitudinal study.

To evaluate the longitudinal changes of bone marrow edema-like lesions (BMELs) in patients after anterior cruciate ligament (ACL) reconstruction and to investigate the effect of BMELs on cartilage matrix composition changes measured using MR T1ρ and T2 mapping. Patients with acute ACL tear were enrolled in a prospective study. MR imaging was performed at baseline (before surgeries) and at 6-month, 1-year and 2-year after ACL reconstruction. MR imaging included sagittal high-resolution, 3D fast spin-echo (CUBE) sequences for BMEL evaluation, and 3D T1ρ mapping and T2 mapping for cartilage assessment. BMELs were assessed using whole-organ magnetic resonance imaging score (WORMS), and the volume of BMELs was measured by a semi-automatic method. Generalized estimating equation (GEE) was used to explore association between BMELs at baseline and cartilage changes during follow-up. Fifty four patients were included in the present study and 39 patients had completed 2-year follow-up. BMELs were noted in 42 injured knees (77.8%) with 105 lesions and in 7 contralateral knees (13.0%) with 9 lesions (χ2=45.763, P<0.001) at the baseline. The WORMS and volume of BMELs of the injured knees were 2.36±0.65 and 386.98±382.54 mm3 (r=0.681, P<0.001), respectively. 87 BMELs were found at baseline in 34 patients (87.2%) of the 39 patients who had completed 2 years follow-up. During the follow-up, 18 (20.7%), 12 (13.8%), and 5 (5.7%) baseline lesions were still seen at 6-month, 1-year and 2-year, respectively. The changes of BMELs prevalence regarding bone compartments over time points were statistically significant (χ2=163.660, P<0.001). Except T2 value at 6 months, T1ρ and T2 values of cartilage overlying baseline BMELs in the injured knees were higher than that of anatomically matched cartilage in the contralateral knees at baseline and each follow-up time-point. In the injured knees, GEE analysis showed that baseline BMELs were significantly associated with higher T1ρ and T2 values of cartilage after adjustment of age, gender, body mass index (BMI), effusion and meniscus tear. The association between BMELs and Knee Injury and Osteoarthritis Outcome Scores (KOOS) scores was not statistically significant. BMEL is a common finding in patients with acute ACL injury and resolves rapidly over time after ACL reconstruction. It is often associated with increased T1ρ and T2 values of cartilage. BMEL at baseline is an independent predictor for faster cartilage degeneration during follow-up.