Advanced Magnetic Resonance Imaging and Molecular Imaging of the Painful Knee

Chronic knee pain is a common condition. Causes of knee pain include trauma, inflammation, and degeneration, but in many patients the pathophysiology remains unknown. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research focused on the pathophysiology of chronic musculoskeletal pain and more specifically inflammation. The forthcoming new insights can help develop better targeted treatment, and some imaging techniques may even serve as imaging biomarkers for predicting and assessing treatment response in the future. This review highlights the latest developments in perfusion MRI, diffusion MRI, and molecular imaging with positron emission tomography/MRI and their application in the painful knee. The primary focus is synovial inflammation, also known as synovitis. Bone perfusion and bone metabolism are also addressed.</p

Advanced Magnetic Resonance Imaging and Molecular Imaging of the Painful Knee

Chronic knee pain is a common condition. Causes of knee pain include trauma, inflammation, and degeneration, but in many patients the pathophysiology remains unknown. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research focused on the pathophysiology of chronic musculoskeletal pain and more specifically inflammation. The forthcoming new insights can help develop better targeted treatment, and some imaging techniques may even serve as imaging biomarkers for predicting and assessing treatment response in the future. This review highlights the latest developments in perfusion MRI, diffusion MRI, and molecular imaging with positron emission tomography/MRI and their application in the painful knee. The primary focus is synovial inflammation, also known as synovitis. Bone perfusion and bone metabolism are also addressed.</p

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

Improved Visualization of Cartilage Canals Using Quantitative Susceptibility Mapping

<div><p>Purpose</p><p>Cartilage canal vessels are critical to the normal function of epiphyseal (growth) cartilage and damage to these vessels is demonstrated or suspected in several important developmental orthopaedic diseases. High-resolution, three-dimensional (3-D) visualization of cartilage canals has recently been demonstrated using susceptibility weighted imaging (SWI). In the present study, a quantitative susceptibility mapping (QSM) approach is evaluated for 3-D visualization of the cartilage canals. It is hypothesized that QSM post-processing improves visualization of the cartilage canals by resolving artifacts present in the standard SWI post-processing while retaining sensitivity to the cartilage canals.</p><p>Methods</p><p>Ex vivo distal femoral specimens from 3- and 8-week-old piglets and a 1-month-old human cadaver were scanned at 9.4 T with a 3-D gradient recalled echo sequence suitable for SWI and QSM post-processing. The human specimen and the stifle joint of a live, 3-week-old piglet also were scanned at 7.0 T. Datasets were processed using the standard SWI method and truncated k-space division QSM approach. To compare the post-processing methods, minimum/maximum intensity projections and 3-D reconstructions of the processed datasets were generated and evaluated.</p><p>Results</p><p>Cartilage canals were successfully visualized using both SWI and QSM approaches. The artifactual splitting of the cartilage canals that occurs due to the dipolar phase, which was present in the SWI post-processed data, was eliminated by the QSM approach. Thus, orientation-independent visualization and better localization of the cartilage canals was achieved with the QSM approach. Combination of GRE with a mask based on QSM data further improved visualization.</p><p>Conclusions</p><p>Improved and artifact-free 3-D visualization of the cartilage canals was demonstrated by QSM processing of the data, especially by utilizing susceptibility data as an enhancing mask. Utilizing tissue-inherent contrast, this method allows noninvasive assessment of the vasculature in the epiphyseal cartilage in the developing skeleton and potentially increases the opportunity to diagnose disease of this tissue in the preclinical stages, when treatment likely will have increased efficacy.</p></div

Comparison of QSM, plain GRE, SWI and QSM-WI at 7.0 T <i>in vivo</i>.

<p>Comparison of QSM, GRE, SWI and QSM-WI of a 3-week-old piglet scanned at 7.0 T <i>in vivo</i>. In the first pane, showing an axial plane perpendicular to B0, the datasets appeared visually similar. In the second pane, with views parallel to B0, artifactual splitting of the vessels was observed for the SWI data while both QSM datasets and the unprocessed GRE appeared artifact-free.</p

Main pre- and post-processing steps for SWI, QSM and QSM-WI.

<p>Main pre- and post-processing steps depicted for a single slice (in a plane parallel to <i>B</i>₀) from the distal femur of a 3-week-old pig at 9.4 T. Original GRE magnitude (A) and phase (B). Generation of segmentation mask was initiated with a single-slice manual ROI (C), which was extended to the entire 3-D volume automatically (D), generating a segmentation mask for further processing (E). In SWI post-processing, high-pass filtering of the phase was first done using homodyne filtering (F). The phase was converted to a negative phase mask (G) and the SWI data was generated by applying the phase mask to the original magnitude data (H). Finally the segmentation mask was also applied to the SWI data for further visualizations (I). For QSM post-processing, the phase was first processed using Laplacian and SHARP filtering (J) and, in turn, converted to a quantitative susceptibility map with k-space inversion (K) and masked with the segmentation and contrast-inverted to match the appearance of SWI (L). Finally, the susceptibility map was converted into an enhancing mask (M) and finally applied to the magnitude data to generate a QSM-WI dataset (N).</p

Quantitative susceptibility values of the cartilage canals.

<p>Relative susceptibility values of the cartilage canals with respect to the surrounding tissue in a 1-month-old human cadaveric distal femur scanned at 9.4 T (A) and at 7.0 T (B), and in a 3-week-old piglet scanned at 7.0 T <i>in vivo</i> (C) as a function of the truncation factor used in the k-space dipole inversion. Inset images in A-C depict single slices from the quantitative susceptibility maps at truncation factor values of 0.5, 5 and 20 at an intensity scale normalized with the intensity of the cartilage canals to facilitate visual comparison of the streaking artifacts. The second row shows the susceptibility histograms acquired for the corresponding cartilage canal ROIs for the respective specimens as a function of the truncation factor (D-F).</p

Three-dimensional reconstructions of cartilage canals using SWI and QSM.

<p>3-D reconstructions of the cartilage canals in the medial femoral condyle of an 8-week-old pig scanned at 9.4 T. The QSM processing (A) allowed visualization of the cartilage canals without artifacts. In the SWI post-processed data (B), the splitting artifact was seen. The red arrows point to a matching vessel identified in the two datasets.</p

Comparison of QSM, plain GRE, SWI and QSM-WI at 9.4 T.

<p>Comparison of QSM, SWI and QSM-WI post-processing as well as unprocessed GRE for the visualization of cartilage canals in a 1-month-old human cadaveric distal femur in 3 mm-thick minimum intensity projections in the main imaging planes with respect to the scanner geometry at 9.4 T (TE = 15.05 ms and bandwidth = 37 Hz/pixel). The first pane shows the axial view, perpendicular to <i>B</i>₀: both truncated k-space QSM (QSM) and QSM-weighted imaging (QSM-WI) results appeared nearly identical to the SWI result. The plain GRE appeared similar, but lacked some of the detail. The second pane shows coronal and sagittal views, parallel to the <i>B</i>₀ field. Both QSM visualizations demonstrate the vasculature without artifacts whereas, in the SWI data, the splitting of the vessels along the <i>B</i>₀ direction is noted. The plain GRE appeared similar to QSM and also did not show artifacts, but clearly lacked the definition seen with QSM. White arrows point to several matching vessels to aid comparison. The QSM contrast (first row) was inverted to match the contrast of the SWI data.</p

Multiparametric MRI of Epiphyseal Cartilage Necrosis (Osteochondrosis) with Histological Validation in a Goat Model.

To evaluate multiple MRI parameters in a surgical model of osteochondrosis (OC) in goats.Focal ischemic lesions of two different sizes were induced in the epiphyseal cartilage of the medial femoral condyles of goats at 4 days of age by surgical transection of cartilage canal blood vessels. Goats were euthanized and specimens harvested 3, 4, 5, 6, 9 and 10 weeks post-op. Ex vivo MRI scans were conducted at 9.4 Tesla for mapping the T1, T2, T1ρ, adiabatic T1ρ and TRAFF relaxation times of articular cartilage, unaffected epiphyseal cartilage, and epiphyseal cartilage within the area of the induced lesion. After MRI scans, safranin O staining was conducted to validate areas of ischemic necrosis induced in the medial femoral condyles of six goats, and to allow comparison of MRI findings with the semi-quantitative proteoglycan assessment in corresponding safranin O-stained histological sections.All relaxation time constants differentiated normal epiphyseal cartilage from lesions of ischemic cartilage necrosis, and the histological staining results confirmed the proteoglycan (PG) loss in the areas of ischemia. In the scanned specimens, all of the measured relaxation time constants were higher in the articular than in the normal epiphyseal cartilage, consistently allowing differentiation between these two tissues.Multiparametric MRI provided a sensitive approach to discriminate between necrotic and viable epiphyseal cartilage and between articular and epiphyseal cartilage, which may be useful for diagnosing and monitoring OC lesions and, potentially, for assessing effectiveness of treatment interventions