Advances in Musculoskeletal Imaging and Their Applications, 2nd Edition

Topic Information

Dear Colleagues,

Radiographic acquisition techniques have undergone tremendous improvements since their invention. Image resolution has greatly increased and the reduction in the dose of X-ray radiation required for its creation has been achieved. The increased amount of imaging data does not necessarily mean that more medical information is accessible to the reader. Some (but often important) information is hidden from the radiologist. This is especially true for radiographic techniques.

The purpose of advanced image-analysis systems is to extract occulted data to improve the objectivity of diagnosis for a given case. The treatment of clinical problems with information obtained using advanced image analyses has increased. In musculoskeletal radiology, proven associations exist between bone scan analyses, patient health and metabolic status. Moreover, the processes of bone maturation, bone healing, bone demineralization and deformation due to overuse can be extensively analyzed with the use of CR, CT and MRI. Advanced methods significantly improve differentiation and hence the diagnostic process of medication for different lesions including neoplasms of the bone.

Papers investigating the application of both classical image processing and artificial intelligence (AI) methods in the analysis and extraction of diagnostically useful data from medical images are welcomed in this Special Issue. Such methods assist in the investigation of the shape and geometry of, for example, bone tissue or its fragments. Other AI approaches allow for the automatic detection and segmentation of tissues or organs and the assessment of their pathologies. For this purpose, the achievements of radiomics are particularly useful, including image-texture analyses. Various machine learning methods are also useful for exploring medical imaging data and are widely used in medical diagnostic support systems. Deep learning algorithms play a particularly important role in this respect. Recently, dynamic developments have been achieved in the field of deep learning algorithms, and their effectiveness has been confirmed in numerous applications of medical image analyses of various modalities.

Prof. Dr. Rafał Obuchowicz
Dr. Monika Ostrogórska
Prof. Dr. Michał Strzelecki
Prof. Dr. Adam Piórkowski
Topic Editors

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Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
BioMed biomed	-	-	2021	20.3 Days	CHF 1000	Submit
Diagnostics diagnostics	3.0	4.7	2011	20.5 Days	CHF 2600	Submit
Journal of Clinical Medicine jcm	3.0	5.7	2012	17.3 Days	CHF 2600	Submit
Journal of Imaging jimaging	2.7	5.9	2015	20.9 Days	CHF 1800	Submit

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17 pages, 1591 KiB  

Open AccessReview

MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle

by Marnee J. McKay, Kenneth A. Weber II, Evert O. Wesselink, Zachary A. Smith, Rebecca Abbott, David B. Anderson, Claire E. Ashton-James, John Atyeo, Aaron J. Beach, Joshua Burns, Stephen Clarke, Natalie J. Collins, Michel W. Coppieters, Jon Cornwall, Rebecca J. Crawford, Enrico De Martino, Adam G. Dunn, Jillian P. Eyles, Henry J. Feng, Maryse Fortin, Melinda M. Franettovich Smith, Graham Galloway, Ziba Gandomkar, Sarah Glastras, Luke A. Henderson, Julie A. Hides, Claire E. Hiller, Sarah N. Hilmer, Mark A. Hoggarth, Brian Kim, Navneet Lal, Laura LaPorta, John S. Magnussen, Sarah Maloney, Lyn March, Andrea G. Nackley, Shaun P. O’Leary, Anneli Peolsson, Zuzana Perraton, Annelies L. Pool-Goudzwaard, Margaret Schnitzler, Amee L. Seitz, Adam I. Semciw, Philip W. Sheard, Andrew C. Smith, Suzanne J. Snodgrass, Justin Sullivan, Vienna Tran, Stephanie Valentin, David M. Walton, Laurelie R. Wishart and James M. Elliottadd Show full author list remove Hide full author list

J. Imaging 2024, 10(11), 262; https://doi.org/10.3390/jimaging10110262 - 22 Oct 2024

Viewed by 2099

Abstract

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in individuals with neuromuscular [...] Read more.

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in individuals with neuromuscular and musculoskeletal disorders is the compositional changes to muscles, evinced by the expression of fatty infiltrates. Quantification of skeletal muscle composition by MRI has emerged as a sensitive marker for the severity of these disorders; however, little is known about the composition of healthy muscles across the lifespan. Knowledge of what is ‘typical’ age-related muscle composition is essential to accurately identify and evaluate what is ‘atypical’. This innovative project, known as the MuscleMap, will achieve the first important steps towards establishing a world-first, normative reference MRI dataset of skeletal muscle composition with the potential to provide valuable insights into various diseases and disorders, ultimately improving patient care and advancing research in the field. Full article

(This article belongs to the Topic Advances in Musculoskeletal Imaging and Their Applications, 2nd Edition)

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Figure 1

Figure 1
<p>(<b>A</b>) Axial cervical spine muscle segmentations at the C4 vertebral level from manual segmentation and an automated computer-vision model overlaid over a water image from Dixon fat–water MRI. (<b>B</b>) Three-dimensional renderings of cervical spine muscle segmentations. The muscle groups segmented include the multifidus and semispinalis cervicis (left = light pink, right = aqua), longus colli and longus capitis (left = light green, right = gold), semispinalis capitis (left = orange, right = yellow), splenius capitis (left = dark pink, right = light blue), levator scapula (left = indigo, right = dark green), sternocleidomastoid (left = blue, right = red), and trapezius (left = brown, right = magenta). L = left, R = right, A = anterior, P = posterior, S = superior, I = inferior. Adapted from Weber et al., 2021 [<a href="#B5-jimaging-10-00262" class="html-bibr">5</a>].</p> Full article ">Figure 2
<p>(<b>A</b>) Axial lumbar spine muscle segmentations at the L4 vertebral level from manual segmentation and an automated computer-vision model overlaid over a spin-echo T₂-weighted image. (<b>B</b>) Three-dimensional renderings of the lumbar spine muscle segmentations. The muscle groups segmented include the multifidus (left = light orange, right = dark orange), erector spinae (left = light blue, right = dark blue), and psoas major (left = light green, right = dark green). L = left, R = right, A = anterior, P = posterior, S = superior, I = inferior. Adapted from Wesselink et al., 2022 [<a href="#B6-jimaging-10-00262" class="html-bibr">6</a>].</p> Full article ">Figure 3
<p>(<b>A</b>) Axial right hip muscle segmentation overlaid over a fat image from Dixon fat–water MRI. (<b>B</b>) Three-dimensional renderings of the hip muscle segmentations. The muscle groups segmented include the gluteus maximus (blue), gluteus medius (green), and gluteus minimus (red). Adapted from Perraton et al., 2024 [<a href="#B69-jimaging-10-00262" class="html-bibr">69</a>].</p> Full article ">Figure 4
<p>Three-dimensional renderings of the intrinsic foot muscles from Dixon fat–water imaging. The muscle groups segmented included the abductor hallucis (plum), quadratus plantae (light blue), flexor digitorum brevis (fuchsia), abductor digiti minimi (orange), lumbricals (yellow), extensor digitorum brevis (pink), flexor hallucis brevis medial head (red), flexor hallucis brevis lateral head (salmon), adductor hallucis (dark blue), flexor digiti minimi (purple), and plantar and dorsal interossei (green). Adapted from Franettovich et al., 2021 [<a href="#B80-jimaging-10-00262" class="html-bibr">80</a>].</p> Full article ">

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11 pages, 1109 KiB  

Open AccessArticle

Musculoskeletal Dimension and Brightness Reference Values in Lumbar Magnetic Resonance Imaging—A Radio-Anatomic Investigation in 80 Healthy Adult Individuals

by Horst Balling, Boris Michael Holzapfel, Wolfgang Böcker, Dominic Simon, Paul Reidler and Joerg Arnholdt

J. Clin. Med. 2024, 13(15), 4496; https://doi.org/10.3390/jcm13154496 - 1 Aug 2024

Viewed by 1061

Abstract

Background/Objectives: Magnetic resonance imaging (MRI) is the preferred diagnostic means to visualize spinal pathologies, and offers the possibility of precise structural tissue analysis. However, knowledge about MRI-based measurements of physiological cross-sectional musculoskeletal dimensions and associated tissue-specific average structural brightness in the lumbar [...] Read more.

Background/Objectives: Magnetic resonance imaging (MRI) is the preferred diagnostic means to visualize spinal pathologies, and offers the possibility of precise structural tissue analysis. However, knowledge about MRI-based measurements of physiological cross-sectional musculoskeletal dimensions and associated tissue-specific average structural brightness in the lumbar spine of healthy young women and men is scarce. The current study was planned to investigate characteristic intersexual differences and to provide MRI-related musculoskeletal baseline values before the onset of biological aging. Methods: At a single medical center, lumbar MRI scans of 40 women and 40 men aged 20–40 years who presented with moderate nonspecific low back pain were retrospectively evaluated for sex-specific differences in cross-sectional sizes of the fifth lumbar vertebrae, psoas and posterior paravertebral muscles, and respective sex- and age-dependent average brightness alterations on T2-weighted axial sections in the L5-level. Results: In women (mean age 33.5 years ± 5.0 (standard deviation)), the investigated musculoskeletal cross-sectional area sizes were significantly smaller (p < 0.001) compared to those in men (mean age 33.0 years ± 5.7). Respective average musculoskeletal brightness values were higher in women compared to those in men, and most pronounced in posterior paravertebral muscles (p < 0.001). By correlating brightness results to those of subcutaneous fat tissue, all intersexual differences, including those between fifth lumbar vertebrae and psoas muscles, turned out to be statistically significant. This phenomenon was least pronounced in psoas muscles. Conclusions: Lumbar musculoskeletal parameters showed significantly larger dimensions of investigated anatomical structures in men compared to those in women aged 20–40 years, and an earlier onset and faster progress of bone loss and muscle degradation in women. Full article

(This article belongs to the Topic Advances in Musculoskeletal Imaging and Their Applications, 2nd Edition)

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Figure 1

Figure 1
<p>Example showing measurement schemes of LIC area size and brightness in the fifth lumbar vertebral body, with both psoas and both erector spinae muscles. Axial MRI slice through the lower lumbar spine showing vertebral and muscle structures of a 39-year-old man without spinal pathologies. LIC indicates largest inscribed circle; MRI, magnetic resonance imaging; L5, lumbar vertebra V; PM, psoas muscle; PPVM, posterior paravertebral muscle; MVB, mean vertebral (body) brightness; MPB, mean psoas (muscle) brightness; MPPVB, mean posterior paravertebral (muscle) brightness.</p> Full article ">Figure 2
<p>(<b>a</b>–<b>c</b>) Results from MRI-based LIC area (<b>a</b>) and mean brightness measurements (<b>b</b>) in LV5s, PMs, and PPVMs in 40 women (circles) and 40 men (triangles) ordered by increasing age (x-axis: age in years). Black dashed lines in graphs are trend lines of males’ data points, and grey dotted lines are trend lines of females’ data points. LIC areas were significantly larger in men, and PPVMs were significantly brighter in women. As soon as brightness measurements were correlated to subcutaneous fat tissues (<b>c</b>), significantly higher values resulted for all investigated structures in women. MRI indicates magnetic resonance imaging; LIC, largest inscribed circle; L5, lumbar vertebra V; PM, psoas muscle; PPVM, posterior paravertebral muscle; MVB, mean vertebral (body) brightness; MPB, mean psoas (muscle) brightness; MPPVB, mean posterior paravertebral (muscle) brightness; rel, related to the brightness of subcutaneous fat tissue.</p> Full article ">Figure 3
<p>MRI-based cross-sectional muscle area measurements consisting of LIC areas of PMs and PPVMs at a horizontal plane cut parallel through the upper half of the L5-level in 40 women (circles) and 40 men (triangles) ordered by increasing age. The black dashed line is the trend line of males’ data points, and the grey dotted line is the trend line of females’ data points. Total cross-sectional muscle mass seems to be constant in women, but decreases in men with growing age (x-axis: age in years; y-axis: mm²). MRI indicates magnetic resonance imaging; LIC, largest inscribed circle; PM, psoas muscle; PPVM, posterior paravertebral muscle; L5, lumbar vertebra V; mm², square millimeter.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>f</b>) Graphical depiction of means and 95% CIs of cross-sectional vertebral body (<b>a</b>) and muscle dimensions (<b>b</b>–<b>d</b>) at the upper half of the L5-level, and of absolute (<b>e</b>) and relative brightness (<b>f</b>) in corresponding locations, i.e., LV5, PM, PPVM, and total perivertebral muscle mass in 40 women and 40 men aged 20–40 years. L5/LV5 indicates lumbar vertebra V; 95% CI, 95% confidence interval; LIC, largest inscribed circle; mm², square millimeter; PM, psoas muscle; PPVM, posterior paravertebral muscle; MVB, mean vertebral (body) brightness; MPB, mean psoas (muscle) brightness; MPPVB, mean posterior paravertebral (muscle) brightness; MTMB, mean total muscle brightness; rel, related to the brightness of subcutaneous fat tissue.</p> Full article ">

16 pages, 1755 KiB  

Open AccessArticle

Cross-Sectional Area and Echogenicity Reference Values for Sonography of Peripheral Nerves in the Lithuanian Population

by Evelina Grusauskiene, Agne Smigelskyte, Erisela Qerama and Daiva Rastenyte

Diagnostics 2024, 14(13), 1373; https://doi.org/10.3390/diagnostics14131373 - 28 Jun 2024

Viewed by 855

Abstract

Objectives: We aimed to provide data of nerve sizes and echogenicity reference values of the Lithuanian population. Methods: High-resolution ultrasound was bilaterally performed according to the Ultrasound Pattern Sum Score and Neuropathy ultrasound protocols for healthy Lithuanian adults. Cross-sectional area (CSA) measurement and [...] Read more.

Objectives: We aimed to provide data of nerve sizes and echogenicity reference values of the Lithuanian population. Methods: High-resolution ultrasound was bilaterally performed according to the Ultrasound Pattern Sum Score and Neuropathy ultrasound protocols for healthy Lithuanian adults. Cross-sectional area (CSA) measurement and echogenicity were used as the main parameters for investigation. Echogenicity was evaluated using ImageJ, and nerves were categorized in classes according to echogenicity. Results: Of 125 subjects enrolled, 63 were males (mean age 47.57 years, range 25–78 years) and 62 were females (mean age 50.50 years, range 25–80 years). Reference values of nerve sizes and values of echogenicity as a fraction of black in percentage of cervical roots, upper and middle trunks of the brachial plexus and the following nerves: vagal, median, ulnar, radial, superficial radial, tibial, fibular, and sural in standard regions were established. Mild to moderate correlations were found between nerves CSA, echogenicity values and anthropometric measurements with the differences according to sex. Inter-rater (ICC 0.93; 95% CI 0.92–0.94) and intra-rater (ICC 0.94; 95% CI 0.93–0.95) reliability was excellent. Conclusions: Reference values of nerve size and echogenicity of Lithuanians were presented for the first time as a novel such kind of publication from the Baltic countries. Full article

(This article belongs to the Topic Advances in Musculoskeletal Imaging and Their Applications, 2nd Edition)

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Figure 1

Figure 1
<p>Cross-sectional area measurement methodology. Arrow is pointing to the median nerve at the forearm. CSA of the nerve is circled along the hyperechoic epineurium.</p> Full article ">Figure 2
<p>Arrow is pointing to the median nerve at the forearm. Image converted into an 8-bit image, each pixel in a range calculated between 0 (black) and 255 (white).</p> Full article ">Figure 3
<p>Overview over the significant findings among males and females. The figure shows the distribution of CSA of different nerves between males and females. The bars denote the mean and the SD for different measurement sites. Black bars denote females, and blue bars denote males. * <span class="html-italic">p</span> value &lt; 0.05, ** <span class="html-italic">p</span> value &lt; 0.001.</p> Full article ">Figure 4
<p>Distribution of nerve echogenicity classes according to gender ((<b>A</b>) upper limb, (<b>B</b>) lower limb, vagal nerve, and brachial plexus). (<b>A</b>) Mean, <span class="html-italic">p</span>-values for difference between echogenicity classes between males and females. Black bars denote echogenicity class 1, grey bars denote echogenicity class 2, blue bars denote echogenicity class 3, N—subject number. (<b>B</b>) Mean, <span class="html-italic">p</span>-values for the difference between echogenicity classes between male and female. Black bars denote echogenicity class 1, grey bars denote echogenicity class 2, blue bars denote echogenicity class 3, N—subject number.</p> Full article ">Figure 4 Cont.
<p>Distribution of nerve echogenicity classes according to gender ((<b>A</b>) upper limb, (<b>B</b>) lower limb, vagal nerve, and brachial plexus). (<b>A</b>) Mean, <span class="html-italic">p</span>-values for difference between echogenicity classes between males and females. Black bars denote echogenicity class 1, grey bars denote echogenicity class 2, blue bars denote echogenicity class 3, N—subject number. (<b>B</b>) Mean, <span class="html-italic">p</span>-values for the difference between echogenicity classes between male and female. Black bars denote echogenicity class 1, grey bars denote echogenicity class 2, blue bars denote echogenicity class 3, N—subject number.</p> Full article ">

13 pages, 6554 KiB  

Open AccessArticle

Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality

by Wojciech Kazimierczak, Kamila Kędziora, Joanna Janiszewska-Olszowska, Natalia Kazimierczak and Zbigniew Serafin

J. Clin. Med. 2024, 13(5), 1502; https://doi.org/10.3390/jcm13051502 - 5 Mar 2024

Cited by 4 | Viewed by 1834

Abstract

Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This [...] Read more.

Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study. Full article

(This article belongs to the Topic Advances in Musculoskeletal Imaging and Their Applications, 2nd Edition)

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Figure 1

Figure 1
<p>Sample ROI positioning: ROI₁—condyle, ROI₄—buccal adipose tissue (<b>A</b>); ROI₂—articular space (<b>B</b>); ROI₃—masseter muscle (<b>C</b>).</p> Full article ">Figure 2
<p>Results of mean signal calculations in ROI₁ (<b>A</b>), ROI₂ (<b>B</b>), and mean noise calculations (<b>C</b>). (<span class="html-italic">mean values, 95% confidence intervals (CI), ranges</span>).</p> Full article ">Figure 3
<p>Results of CNR calculations in mandibular condyles (<b>A</b>), articular spaces (<b>B</b>) (<span class="html-italic">mean values, 95% confidence intervals (CI), ranges</span>).</p> Full article ">Figure 4
<p>Qualitative image analysis: (<b>A</b>)—(1 point) anatomical structures not identifiable and images of no diagnostic value; (<b>B</b>)—(2 points) structures identifiable in adequate image quality; (<b>C</b>)—(3 points) anatomical structures still fully assessable in all parts and acceptable image quality; (<b>D</b>)—(4 points) clear delineation of structures and good image quality; (<b>E</b>)—(5 points) excellent delineation of structures and excellent image quality.</p> Full article ">Figure 5
<p>Summary of subjective image quality assessments performed by both readers.</p> Full article ">Figure 6
<p>Diagram presenting the results of lesions detected in both reconstructions by both readers (<b>A</b>,<b>B</b>).</p> Full article ">Figure 7
<p>Sample patient diagnosed with erosions, oteophytes, condyle flattening and deformation. Circular ROIs placed in adipose tissue. Reconstructions: (<b>A</b>) Native—mean signal −36.6 noise 49.7; (<b>B</b>) DLM—mean signal −39.8, noise 43.2.</p> Full article ">

13 pages, 2360 KiB  

Open AccessArticle

Correlation between Subchondral Insufficiency Fracture of the Knee and Osteoarthritis Progression in Patients with Medial Meniscus Posterior Root Tear

by Bing-Kuan Chen, Yi-Cheng Lin, Yu-Hsin Liu, Pei-Wei Weng, Kuan-Hao Chen, Chang-Jung Chiang and Chin-Chean Wong

Diagnostics 2023, 13(23), 3532; https://doi.org/10.3390/diagnostics13233532 - 26 Nov 2023

Cited by 4 | Viewed by 1898

Abstract

A medial meniscus posterior root tear (MMPRT) contributes to knee joint degeneration. Arthroscopic transtibial pullout repair (ATPR) may restore biomechanical integrity for load transmission. However, degeneration persists after ATPR in certain patients, particularly those with preoperative subchondral insufficiency fracture of the knee (SIFK). [...] Read more.

A medial meniscus posterior root tear (MMPRT) contributes to knee joint degeneration. Arthroscopic transtibial pullout repair (ATPR) may restore biomechanical integrity for load transmission. However, degeneration persists after ATPR in certain patients, particularly those with preoperative subchondral insufficiency fracture of the knee (SIFK). We explored the relationship between preoperative SIFK and osteoarthritis (OA) progression in retrospectively enrolled patients who were diagnosed as having an MMPRT and had received ATPR within a single institute. Based on their preoperative magnetic resonance imaging (MRI), these patients were then categorized into SIFK and non-SIFK groups. OA progression was evaluated by determining Kellgren–Lawrence (KL) grade changes and preoperative and postoperative median joint widths. SIFK characteristics were quantified using Image J (Version 1.52a). Both groups exhibited significant post-ATPR changes in medial knee joint widths. The SIFK group demonstrated significant KL grade changes (p < 0.0001). A larger SIFK size in the tibia and a greater lesion-to-tibia length ratio in the coronal view were positively correlated with more significant KL grade changes (p = 0.008 and 0.002, respectively). Thus, preoperative SIFK in patients with an MMPRT was associated with knee OA progression. Moreover, a positive correlation was observed between SIFK lesion characteristics and knee OA progression. Full article

(This article belongs to the Topic Advances in Musculoskeletal Imaging and Their Applications, 2nd Edition)

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Figure 1

Figure 1
<p>Flowchart of patient inclusion, exclusion, and group stratification.</p> Full article ">Figure 2
<p>Subchondral insufficiency fracture was identified on medial femoral condyle in the coronal view images (<b>a</b>). Depressed part of the subchondral plate and the region exhibiting bone marrow edema were chosen for analysis (<b>b</b>). In sagittal view, hyperintense signal was found at anterior portion of tibial plateau (<b>c</b>). The thickened subchondral plate with a depressed part in the sagittal view was selected, but the ill-defined area was excluded (<b>d</b>).</p> Full article ">Figure 3
<p>Measurement of the lesion length on coronal view images. Upper horizontal yellow line indicates the horizontal lesion length, and the lower horizontal yellow line indicates the horizontal distance from the medial condyle to the adjacent peak of the tibial spine.</p> Full article ">Figure 4
<p>ATPR for an MMPRT in a 48-year-old man. (<b>a</b>) Evaluation of the tear pattern and gap of the meniscal root tear (red arrowhead). (<b>b</b>) Creation of transtibial tunnel using an aiming drill guide. (<b>c</b>) Torn meniscal root sutured using a No. 0 nonabsorbable braided suture with a knee Scorpion needle. (<b>d</b>) Reduction of the meniscus to the footprint site by tensioning the free ends of the sutures. MFC, medial femoral condyle; MMPH, medial meniscus posterior horn; MTP, medial tibial plateau.</p> Full article ">Figure 5
<p>Changes in medial knee joint width (<b>A</b>) and alterations in the median KL grades (<b>B</b>) postoperatively in each group, assessed using paired <span class="html-italic">t</span> and Wilcoxon signed-rank tests, respectively. Medial knee joint width and median KL grade improvements were compared between the two groups using independent-sample <span class="html-italic">t</span> and Mann–Whitney <span class="html-italic">U</span> tests, respectively. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001, ns—not significant.</p> Full article ">Figure 6
<p>Chi-square test to compare Kellgren–Lawrence grade changes between the SIFK and non-SIFK groups.</p> Full article ">

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