Fig. 2: Comparison between favorable and unfavorable group on each IVIM parameter.

Fig. 1: Image of IVIM parameters for analysis

Figure 1. Cartilage segmentation on bovine cartilage in normal cartilage (A), cartilage with 0.6mm holes (B), 0.9mm holes (C) and 1.2mm holes (D). (E, F) Segmentation area indicated with colors in sagittal plane and leg specimen. (G) Result of cartilaginous fluid fraction in each location.

Figure 3. Knee cartilage segmentations of 3D FSE image and PD map in osteoarthritis patient. A cartilage defect in posterior medial femoral condyle is demonstrated in 3D FSE image (green arrow) and shows good correlation with fluid exclusion image (FEI) on SFF map, better than FEI on T2 map.

Figure 3: Coronal MR images of cobalt-chromium and titanium-on-ceramic hip arthroplasty implant systems using the clinical protocol at 1.5T and the proposed protocol at 0.55T. Despite higher SEMAC encoding steps at 1.5T, metal artifacts are substantially smaller at 0.55T.

Figure 4: Axial MR images of cobalt-chromium and titanium-on-ceramic hip arthroplasty implant systems using matched protocols at 1.5T and 0.55T. Metal artifacts are substantially smaller at 0.55T.

MUFFIN processing. The first 5 spokes were discarded to reach a steady state. The rest was split into 14 frames. 40% apodization was a good tradeoff between resolution and undersampling-related artifacts. The density compensation function was V-shaped. After determining the reference frame (by picking the closest rotation vector to the mean rotation vector), all transformations to and from the reference frame were used in the optimization to obtain the corrected volume.

Top row: A sample slice for Patient 1 acquired with CT and MRI. The dashed red circles indicate the sutures (quite fine structures, and hence difficult to see without looking closely).The uncorrected image is missing both sutures. The green arrows exemplify the detail and sharpness recovered by MUFFIN. Bottom row: 3D renderings. The uncorrected one is barely showing any sutures, while the similarity between the CT skull and the MUFFIN MRI skull is striking.

Figure 1: A-C) Orthogonal imaging planes through the carpal bones of a 3D SPGR acquisition utilized to segment the bones of interest. Segmentation was performed manually and takes into account the cortical bone gap boundary on the images. D) Resulting point cloud of boundary points derived from the high-resolution segmentations. E) Sample slices of 3D dynamic SPGR images utilized to track the static segmentations and the points of interest identified on those segmentations. Kinematic profiles are derived from the tracked points on the static segmentations.

Figure 4: Exemplary kinematic profiles from the study cohort. A) Scaphoid-Capitate center of mass distance and B) Scaphoid-Capitate center of mass angle. For reference and to demonstrate the impact of the basic processing steps applied to generate these profiles, the raw input data for these profiles is displayed in C) scaphoid-capitate center of mass distance and D) scaphoid capitate center of mass angle.

Figure 1: Process for automated texture analysis based classification. Location of the sagittal slice selected for subsequent analysis is shown in a., The regions of interest (ROIs) selected manually for L1-L5 vertebrae are shown in b., the classes of texture features computed are shown in c., the process of feature selection is shown in d. and the steps for generation and selection of classifier models for classification of cases into osteoporotic or non-osteoporotic is shown in e.

Table 2: ROC analysis parameters for different classifiers indicating their respective effectiveness

Figure 2 a) Representative T₂* map of one tendon measured at 0° shows the difference between T₂* values of fascicle bundles and non-fasicle connective tissue (endotendon, epitenon). This difference is also visible in a morphological T₂ weighted image (TE= 6.6ms) (b).

c,d) Histological assessment was performed with Picrosirius red staining and polarized light microscopy as well as Safranin-O staining. The shape of the fiber bundles and the connective tissue between them are clearly visible.

Figure 3) One Achilles and one patellar tendon each were measured at 11 fiber-to-field angles. The mean monoexponential T₂* values (T₂*_m) from 30 consecutive slices are provided. The percentage of voxels preferentially following a bi-exponential decay (measured by AIC_C and F-test) is given for all voxels and orientations. Boxplots are shown on the right.

Flow chart illustrating workflow for the study. (A) representative sagittal UTE image. (B) axial slice of CT scan with the 3 calcium calibration rods. (C) 1-cm cortical analysis mask region which is chosen just inferior to the lesser trochanter. The proximal and distal slices of the UTE sequences are shown. Note higher signal within the cortical bone in the first than in the second echo image. Soft tissue is suppressed in the IR sequence, retaining only bound water signal from the cortical bone and the calibration sample. (D) overview of the mechanical testing methodology.

Correlation plots between imaging parameters and whole-bone stiffness. Error clouds indicate 95% confidence intervals. Asterisks indicate significant correlations. Pore water and porosity index, which are surrogates of cortical porosity, were negatively correlated to stiffness and mineral density. Pore water content and porosity index were also strongly positively correlated with each other. Pore water concentrations were greater than bound water concentrations and thus contributed more to the total water content.

Figure 5: Comparison of spDixon, STIR, UTE-SWI and CT in three patients with acute vertebral fractures (red arrows) and bone marrow edema (S1 and S2). The edema and fracture line were visible in the spDixon Water–fat images and the SWI images. The second subject showed signs of a chronic vertebral fracture (green arrow), without signal alterations neither in the STIR nor the UTE image. The third subject showed signs of an acute vertebral fracture such as a compaction zone (red arrows) as well as ventral and a small dorsal spondylophytes (white arrows) one level lower.

Figure 1: spDixon processing pipeline: A) The UTE phase $$$\angle\left(S_{\text{exp}}\right)$$$ contained contributions from the B₁ phase which varied along AP and RL (x-y-plane). B) A POCS-like algorithm was used to solve Equation [4]. The update dϕ was calculated using conjugate gradients (CG). C) After n-update steps the phase error ϕ_n was estimated. In the corrected UTE phase ϕ₀ – ϕ_n unwanted phase components and the B₁ phase contribution were removed.

Table 2 Clinical data and HISTO FF values of RA patients at each follow-up time point (mean ± SD) CRP, C-reactive protein; DAS28-ESR, 28-joint disease activity score using erythrocyte-sedimentation rate; ESR, erythrocyte-sedimentation rate; FF, fat fraction; HISTO, high-speed T2-corrected multiecho sequence; PLT, platelet count; RA, rheumatoid arthritis; RF, rheumatoid factor Change 1, the baseline value minus the value at 4 weeks Change 2, the value at 4 weeks minus the value at 8 weeks Change 3, the value at 8 weeks minus the value at 12 weeks

Fig. 2 Trend of the correlation between DAS28-ESR and HISTO values in RA patients during treatment. DAS28-ESR, 28-joint Disease Activity Score using erythrocyte-sedimentation rate; HISTO, high-speed T2-corrected multiecho sequence; FF, fat fraction; RA, rheumatoid arthritis

Figure 5. T1 and T2 relaxation time maps from right knee joint of 34-year-old female obtained with conventional inversion recovery (IR) and multi-echo spin-echo (MESE) sequences (total time acquisition 26:48 min) and MRF with 6shot (9:01 min) and 4shots (6:01 min).

Figure 3. MRF-T1 relaxation times (6shots) of superficial and deep cartilage layers from lateral (A) and medial (B) tibiofemoral subregions (n=10 knees).

Left | DESS knee MRI with a 140mm field of view; 0.7mm slice thickness and matrix of 384×384 with 160 slices and an acquisition time of 10.6 mins. Right | Cartilage components segmented using our fully automated segmentation method based on U-convolutional neural networks.

ROC curves for prediction of total knee replacement within 5-years. Blue | Radiomic signature with a C-index of 0.81 (95% CI, 0.76 – 0.84). Purple | Cox risk model including clinico-demographic covariates and the radiomic signature with a C-index of 0.85 (95% CI, 0.82 – 0.87). Red | cox risk model with clinico-demographic features alone had a C-index of 0.79 (95% CI, 0.75 – 0.83).

Figure 1: Visualization of principal component (PC) 1 mode of variation along with representative subjects with low and high PC1 scores. PC 1 was primarily characterized by greater two-year changes in T_1ρ relaxation times in the deep layers of acetabular cartilage. Top: Mean differences in hip cartilage T_1ρ relaxation times over two years across subjects plus or minus 3 standard deviations (stdev) of PC 1 variation. Bottom: Representative subjects with low and high PC 1 scores demonstrate notable changes in the deep layer of acetabular cartilage T_1ρ relaxation times over two years.

Figure 2: Visualization of principal component (PC) 2 mode of variation along with representative subjects with low and high PC 2 scores. PC 2 was primarily characterized by changes to more superficial layers of the acetabular cartilage in the superior region. Mean differences in hip cartilage T_1ρ relaxation times over two years across subjects plus or minus 3 standard deviations (stdev) of PC 2 variation. Representative subjects with low and high PC2 scores demonstrate two-year changes in the superior acetabular cartilage T_1ρ relaxation times.

T2 mapping (T2M) of the knee. (a) Control group, male, 10 yo; T2M pseudo-color imaging (PCI) shows a uniform cartilage color distribution and complete articular surface. (b) Group A, male, 9 yo; T2M PCI shows uneven cartilage color distribution, few red-yellow stage changes, and an increased cartilage T2 value. (c) Group B, male, 11 yo; T2M PCI shows greater uneven cartilage color distribution, a significantly diminished weight-bearing area, and a significantly increased cartilage T2 value.

A bar graph showing T2 values for the different regions of interest (ROIs) in the knee. Groups HC (control), A (International Cartilage Repair Society [ICRS] level 0), and B (ICRS levels I and II) were compared for each ROI. * represents a significant difference.

Figure 2: T₂ map and T_RAFF relaxation time maps with implemented pulse durations (PD) from two cartilage specimen; red rectangle shows areas of degraded cartilage and yellow rectangle points out to intact cartilage in T₂ map.

Figure 1: a) T_RAFF of collagen phantom measurements. b) T₂ image of collagen phantoms. c) Partial eta square percentage, representing T_RAFF relaxation time contrast/variation accounted by collagen concentration. d) Bloch-McConnell simulated T_RAFF.

Images from one representative participant (male, 17 years old) with hemophilic arthropathy. Conventional (A) T1WI. (B) PDWI. (C) T2*-anatomical image show relatively complete articular surface (yellow arrow) without damage. (D) T2* mapping shows uneven distribution in the cartilage and the decreases (red arrow) of cartilage T2* value indicate damage.

T2* values of articular cartilage in different regions (mean ± SD)

Note: * P<0.05 indicates statistical significance

Figure 1: A representative Cones UTE-MRI image from a 76-year-old female subject (TR=50 ms, for the selection the region of interests TE=2 ms was used because its provided higher contrast). Anterior and posterior tibialis (ATT and PTT) and proximal Achilles (PAT) tendons were obvious in the MRI images, as indicated in red.

Figure 3: Boxplots of (A, C, E) T1 and (B, D, F) MMF of (A,B) ATT, (C,D) PTT, and (E,F) PAT tendons in Ctrl subjects versus OPe and OPo patients. Averages, median, SD, first, and third quartiles are indicated in the boxplots.

Figure 4. UTE-DESS imaging with spDixon of knee joint of a healthy volunteer (37-year-old male). Both weighted echo subtractions with and without spDixon-based fat suppression achieve high contrast specific to short T₂ tissues including the osteochondral junction (yellow arrows), tendons (red arrows), menisci (blue arrows), and ligaments (green arrows).

Figure 5. UTE-DESS imaging with spDixon in two representative OA patients (A-H: 52-year-old male, I-P: 56-year-old male). (A, I) Magnitude images of S₊, (B, J) magnitude images of S_-, (C, K) T₂-FSE images, (D, L) T₁-FSE images, (E, N) spDixon water images from S₊, (F, M) spDixon water images from S_-, (G, O) weighted echo subtraction of water images from spDixon, and (H, P) weighted echo subtraction of S₊ and S_- without spDixon.

Representative 2D projections of femoral articular cartilage T2 relaxation time maps for the right knee (left not shown) of a subject scanned daily for 5 days [A] and weekly for 5 weeks [D]. Differences in T2 values at each timepoint were calculated with reference to the day 1 scan [C, E]. Variability between timepoints was calculated across the femoral surface, as well as within the anterior (A), central (C), and posterior (P) regions of the medial (M) and lateral (L) portions of the femur [B].

Both knees of 3 healthy female subjects were scanned using a bilateral quantitative double-echo in steady-state (qDESS) sequence on a 3T MRI system. Subjects were scanned daily for 5 consecutive days and weekly for 5 consecutive weeks. At each timepoint, the subjects recorded their hours of physical activity, step count, and Rate Perceived Exertion (RPE) for the 24 hours and week prior to the scan date.

Fig 3 Quantitative µMRI profiles and PLM images/profiles

Fig 2 µMRI images

Figure 2. Sample UTE-T2* maps. Top row: a 39-yr male DMT patient with a complex tear to the posterior horn of his medial meniscus (a) and arthroscopically detected intact-but-softened medial and lateral femoral condylar cartilage but partial to full-thickness disruptions of his tibial cartilages (a,b). Bottom row: an uninjured, healthy 24-yr male with homogeneously low UTE-T2* in both medial and lateral menisci and smoothly laminar UTE-T2* distributions in his articular cartilage (c,d).

Figure 1. Softened but intact cartilage regions (scope grade 1) tend to have elevated deep cartilage UTE-T2* values compared to uninjured controls (yellow bars, a,b,c), while cartilage regions with disrupted articular surfaces tend to show UTE-T2* values consistent with or lower than uninjured controls (red bars, a-d). ANOVA (or Kruskal-Wallis) found no significant differences between groups suggesting a high degree of variability in deep cartilage UTE-T2* values of DMT patients with both intact and disrupted articular surfaces. Error bars represent ± standard error of the mean.

Figure 2. A 16-year-old boy with the stage III, unstable JOCD lesion. (a) The first echo of the T₂*-weighted images with 4 evaluated regions: progeny (red), interface (yellow), parent bone (green), and control bone (blue). (b) The corresponding color-coded T₂*map. (c) A T₂-weighted turbo spin echo image with fat suppression depicting a break in articular cartilage and subchondral bone plate (arrow), a cyst (arrowhead), and a hyper-intense edema (asterisks). (d) A zoom of the JOCD lesion area.

Figure 3. Spearman rank correlation (ρ) plots. From left a significant negative correlation between the JOCD stage and the lesion T₂* (ρ=-0.871; p<0.001), a significant negative correlation between the JOCD stage and the interface T₂* (ρ=-0.649; p<0.001), and no significant correlation between the JOCD stage and the patient’s age (ρ=0.081; p=0.65).

Figure 4. The fiber tracts of articular cartilage for both DMM (b) and SHAM (a) rats.

Figure 2. The quantitative fractional anisotropy (FA) and mean diffusivity (MD) maps of articular cartilage for DMM and SHAM rats.

Figure3. Four example subjects, their processed point clouds and subject description. PAT-FEM-TIB, PAT-FEM, FEM-TIB, and MEN compartment combinations were used to train point cloud networks for OA diagnosis. Each compartment is represented by 8192 randomly sampled points. The first three examples are labelled as having osteoarthritis (Kellgren Lawrence grade >=2), while the last example does not. Output p(OA) is used as the shape biomarker feature for Cox PH models. FEM= femur, TIB=tibia, PAT=patella, MEN= menisci

Figure4. Per compartment test results on pretext OA diagnosis task. (L to R) ROC curve, PR curve, and calibration curve with respective performance metrics. Differences between ROC curves are tested using Delong's method, cartilage models were not significantly different from each other, while menisci was significantly different from all cartilage models (p=1e-14, 1e-10, 1e-15).

Figure 1. Comparison of the differences of the cartilage volume (a), thickness (b) and T2 relaxation times (c) between mild and moderate OA group.

* Statistically significant (P < 0.05).

Figure 2. An example of automated cartilage segmentation: (a) Sagittal view of a knee with the automated cartilage segmentation. According to the anatomical position, the cartilage of knee joint was automatically divided into several small regions; (b and c) automated segmentation view, and the software automatically extracted the cartilage and display 3D images.

Figure 3. Example of T₁ maps on control and ACL-injured limb before IA injection of Cy5.5-P15-1 (top row), and 24 h (middle row) and 48 h (bottom row) after injection. Control showed constant T₁ values over time, while ACLT-injured limbs have a trend of decreased T₁ values.

Figure 4. Boxplot showing the change of T1 values after injection of Cy5.5-P1-1 in controls (blue) and ACL-injured limbs (red) for femoral, tibial and patellar cartilage. Both femoral and patellar cartilage showed a significant difference in ΔT₁ (p<0.05, one-way ANOVA).

Figure 5: Representative T1rho maps of the medial knee joint at acceleration factor (AF) 12. The visual difference in the medial cartilage is more noticeable at this AF. The optimized SP is more important at high AFs.

Figure 2: a. Poisson disk sampling pattern (SP), used as an initial SP and b-d. SP obtained from optimization with bias-accelerated subset selection for CS with b. LR, c. L+S SFD, and d. STFD.

Figures 1A-1D show conventional 2D-MRI sagittal fsPDWI, CS-DSweak, CS-DSmediumand CS-DSstrongspectively.Figures 2A and 2B showlongitudinal cracks of the lateral meniscus(thin arrow). 2A is the reformatted transverse axis of CS-SENSE 3D scanning image (slicethickness of 1mm), and 2B is the reformatted sagittal images. Figures 3A and 3B show the horizontal crack in the posterior angle of the medial meniscus. 3A is the reformatted transverse axis of CS-SENSE 3D scanning image (slicethickness of1mm), and 3B is the reformatted sagittal images.

Figure 3: The average T₂ Relaxation Times for synchronized swimmers (synchro) and water polo players (water polo) across the total hip cartilage and four subregions: superior-posterior (sup-pos), superior-anterior (sup-ant), inferior-posterior (inf-pos), and inferior-anterior (inf-ant). The average T₂ relaxation time means are more stable in water polo players and have more variability in the synchronized swimmers.

Figure 1: Schematic overview of the study methodology.

Fig. 1. An example of patella cartilage T2 map acquired. T2 values were quantified in deep, intermediate, and superficial layers using Cartilage Assessment utility (Philips Portal)

Fig. 3. Comparison of the T2 values in patients of the normal group, the mild injury group, and the severe injury group. A – deep layer, B- intermediate layer, C – superficial layer.

Figure 3. Box plots of observer one’s first and second lateral (blue) and medial (orange) T₂ measurements and observer two’s first measurements. Boxes show minimum T₂, median with interquartile range and maximum T₂, along with individual data points.

Figure 4. Example T₂ map depicting increased T₂ in the lateral condylar articular cartilage of P2 and articulating P3. Colour bar in ms.

A diagram of fully automated proton MRI evaluation

A diagram of fully automated sodium MRI evaluation

Figure 3. Surface normal estimation. (a-c) the estimated surface normal. (d) the smoothed surface normal after 8-neighbors average-smoothing. (Green dot: voxel from the bone-cartilage interface; Red dot: voxel from outer boundary; Blue arrow: estimated surface normal; Black arrow: smoothed surface normal) (Image ID in the OAI-ZIB dataset: 9389580)

Figure 4. Comparison of 3dNN and SN method in cartilage thickness measurement. From the proposed SN method, both minimal and maximal thickness maps can be generated. The one shown here is the minimal thickness map. (3dNN: 3d nearest neighbor; SN: surface normal) (Image ID in the OAI-ZIB dataset: 9389580)

Illustrations of performance (with medium accuracy) of the U-Net and truncated U-Net on several slices from one 3D image (green: correctly segmented pixels [true positives]; blue: pixels incorrectly assigned to the background [false negatives]; and red: pixels incorrectly assigned to the cartilage [false positives]).

Results for layer-to-layer analysis: average DSC values for the zones with different percentage of cartilage within the 3D images; average 3D DSC; average DSC for medial slices; the time needed for the segmentation of one slice. *The network was tested on a PC with common characteristics (an Intel Core i5-7640X processor with 32 Gb of RAM).

Figure 2. Example of the cartilage layers implemented in the 3D Texture Analysis. Blue dashed line represents the thickness of the cartilage. L10, L50, L90 show the layer heights, in which the cartilage was analyzed. SUM represents the full cartilage thickness.

Table 1. Five best performing 3D Texture Analysis outputs (sorted by accuracy) per each layer using Naïve Bayes and selected tibial features.

Figure 3: GagCEST vs. Cartilage Volume/Height (mm³/m) in Femoral Cartilage of Control and OA patients. The y-axis of Volume/Height (mm³/m) shows the relationship of larger height and greater cartilage volume. This shows a positive linear relationship in both control and OA patients. On average, OA subjects have a lesser Volume/Height as well as a lesser slope.

Image 1: Manually Segmented Articular Knee Cartilage – on the left is a pyKNEEr generated segmentation and on the right is a manually corrected segmentation.

Figure 2: GLCM parameters visualization for flattened lesion and reference cartilage ROIs computed from 2-parametric T2 maps with offset 0°.

Figure 1: Preprocessing of ROIs for texture analysis.

Fig. 5. Anterior and posterior cruciate ligaments (ACL and PCL) in the ultra-high resolution UTE image at 7T for the same subject as in Fig. 1: a) full FOV and b) local view for ACL, and c) full FOV and d) local view for PCL.

Fig. 2. Patellar cartilage (long white arrow) and the chondro-osseous junction (red arrow, black region) in the ultra high resolution UTE image at 7T for the same subject as in Fig. 1: a) full FOV, and b) local view. A suspicious defect is clearly visible on the chondro-osseous junction above the red arrow in b).

FIGURE 1. The proposed $$$R_{1\rho}$$$ dispersion imaging method including a new SL scheme (a) for turbo-FLASH (b), and a prepared constant $$$M_{prep}$$$ (red dots), with respect to the varying ones (white dots) in the standard acquisition approach (c). The $$$M_{prep}$$$ dynamic range differs significantly between from the proposed (red) and from the standard (green and blue) methods (d), whereas a cluster (blue lines) of $$$M_{prep}$$$ evolve similarly toward steady-state $$$M_{SS}$$$ (red line) during FLASH imaging readout (e).

FIGURE 2. The measured (symbols) and modeled (lines) $$$R_{1\rho}$$$ dispersion profiles with three protocols (a-b), i.e. $$$M_{prep}$$$=50% (red), 60% (green), 70% (blue). The presented $$$R_{1\rho}$$$-weighted signals and REF datasets were taken from the segmented ROIs in the deep tibial (white arrow) and femoral cartilage (yellow arrow) (c). The differences between success fitting (or hit) rates (%) for modeling $$$R_{1\rho}$$$ dispersion in the deep cartilage using the current ($$$M_{prep}$$$=50-70%) and the previous ($$$MpVars$$$) protocols (d).

Figure 5. Diffusion tractography of ligament at different b value. The tracts (a, b) and collagen fiber directions (d, e) were visually comparable between b value of 500 s/mm2 and 1500 s/mm2. Both fiber direction estimation and tractography failed at b value of 2500 s/mm2 (c, f).

Figure 1. The FA maps of ligament, growth plate, and articular cartilage at different angular resolution. The FA values were largely overestimated with the angular resolution of 6. The values showed visually little differences when the angular resolution is higher than 14. Higher FA values were found in the center part of the ligament (purple arrows). Lower values were found in the center part of growth plate (white arrows).

Figure 2. Representative bilateral PD, T1, T2, T1ρ, and B1+ maps of the hip articular cartilages.

Figure 3. Boxplot comparison between subregions for (a) T1, (b) T2, and (c) T1ρ relaxation times.

Figure 1. Scheme of the MIXTURE T1FLAIR T1ρ-mapping.T1ρ mapping was performed using an inversion-recovery and double-refocusing spin-lock prepared segmented TSE sequence with spectral fat suppression. Inversion-recovery was used for suppression of synovial fluid. Three images with different SL preparation times (SL = 0, 25, and 50ms) were acquired with interleaved acquisition. Amplitude of the SL pulse was set to 500 Hz.

Figure 4. Representative sagittal, coronal, and axial MPR images of 3D isotropic T1ρ-mapping with 0.8mm3 acquisition using MIXTURE. The optimized sequence provided motion-insensitive high-quality isotropic images less than 9 minutes.

Figure 1: Representative maps of QMT parameters in sagittal slice of specimen 1 using a Gaussian lineshape fit.

Figure 2: Mean absolute percentage difference from the reference dataset for each design (1 through 8) for all qMT parameters (Top: Gaussian, bottom: Super Lorentzian)

FIGURE 2. A volume-rendered high-resolution 3D right knee image (A) and a coronal image (B) showing the locations of three sagittal slices (white lines) from lateral femoral condyle and of three sagittal slices (yellow lines) from medial femoral condyle .

FIGURE 3. Three imaging Slices 05 (A), 09 (B) and 13 (C) from medial knee cartilage (the 1^st column), superimposed with segmented ROIs, from which the measured (black circles) average T2W signal intensities were modeled using Fit A (solid red lines) and Fit B (dashed green lines) in the deep zone (the 2^nd column), and using Fit A and Fit C (dashed blue lines) in the superficial zone (the 3^rd column).

The T2* value change of cartilage

The volume change of cartilage

Figure 1: Example T1rho and T1 maps from healthy (contralateral) and grooved osteochondral samples. The blunt grooves can be seen in both maps as increases in the relaxation time values (indicated with the green arrows). The sharp grooves (black arrows) are not seen in the qMRI maps. On the right, anatomical image with large ROI indicated.

Figure 2: Relaxation time distributions for the different groups. 1: Contralateral controls, 2: sharp groove, and 3: blunt groove. Red plusses indicate outliers. Even though the values of different groups overlap, the difference between the T1rho of contralateral controls and bluntly grooved groups was statistically significant by ANCOVA (p<0.05). Black asterisk indicates statistically significant differences.

Figure 1: Axial proton density fat suppressed images performed at 3T (row A) and T2 weighted fat suppressed images performed at 7T (row B) demonstrating an intraneural ganglion cyst coursing along the anterior aspect of the tibial nerve (arrows). Notice the clearly distinct fascicles of the tibial nerve, as well as the increased signal to noise ratio, resolution, and tissue contrast seen at 7T.

Figure 3: Sagittal proton density and T2 weighted fat suppressed images performed at 3T (A and C, respectively) and 7T (B and D, respectively). Tissue contrast is suboptimal in the non-fat suppressed proton density image performed at 7T (B) compared to 3T (A). There is also an identifiable superior to inferior and anterior to posterior signal gradient related to the coil which is worse at 7T (B). Reasonable T2 weighted fat suppression is achieved at 7T (D); however, there is poor image uniformity seen superior to inferior and anterior to posterior compared to 3T (C).

Two-dimensional high-resolution (isotropic) coronal view of (a) µMRI and (b) µCT image of the same slice of intact rabbit joint. The red ROIs signify the multiple locations to compare T2 with the corresponding BMD.

The µMRI T2 (ms) versus µCT BMD (g.cm3) linear curve of the Medial Tibia (MT) and Medial Femur (MF).

Figure 2. HR-MAS ¹H-NMR spectrum of ovine Achilles tendon recorded at 298K. Abbreviations used: Lac, Lactate; CS, Chondroitin sulfate; Lip, Lipids; Ile, Isolecuine; Leu, Leucine; Val, Valine; Glu, Glutamate; Pro, Proline; Asn, Asparagine; Hyp, Hydroxyproline; Gln, Glutamine.

Table 1. Chemical shift assignments of HR-MAS ¹H-NMR spectra of ovine Achilles tendon and rat rotator cuff tendon. Abbreviations used: Lac, Lactate; CS, Chondroitin sulfate. All amino acids are labeled according to the three letter code.

Fig.1 Dynamic 31P MR spectra acquired from calf muscle at 7T. (A) Cross-sectional T2W image showing coil sensitive region located in calf muscle. (B) Dynamic 31P spectra at temporal resolution of 2 s collected at rest, during exercise and recovery. (C) Representative spectra at selected time points, with the highlighted Pi signals expanded in (D).

Fig.2 Plots of time course of PCr depletion-recovery (A), Pi rise-decay (B), and pH (C). PCr and Pi signals at resting steady-state are used as references. Note that the pH plot clearly indicates the metabolic changes with alkalization in early exercise and acidification in early recovery.