Skeletal Radiology最新文献

{"title":"Slipped capital femoral epiphysis: emphasis on early recognition and potential pitfalls","authors":"Daniel G. Rosenbaum, Anthony P. Cooper","doi":"10.1007/s00256-024-04798-x","DOIUrl":"https://doi.org/10.1007/s00256-024-04798-x","url":null,"abstract":"<p>Slipped capital femoral epiphysis is a shearing injury through the growth plate of the proximal femur and is the most common hip disorder in adolescence. Delays in diagnosis persist across practice settings despite ongoing innovations in imaging. Recent insights into pathomechanics highlight the importance of femoral head surface morphology and rotational microinstability centered at the epiphyseal tubercle in causing early physeal changes, which can be detected on imaging prior to frank slip. Scrutiny of physeal morphology and comparison to the contralateral hip is critical at all stages of disease progression, and improper technique may result in undue diagnostic delay. Selective use of cross-sectional imaging can be helpful for troubleshooting equivocal early slips and can inform operative technique and adjuvant therapy candidacy in more severe cases. This review provides a comprehensive approach to imaging suspected slipped capital femoral epiphysis, with an emphasis on early detection and potential pitfalls.</p>","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT.","authors":"Yejin Jeon, Bo Ram Kim, Hyoung In Choi, Eugene Lee, Da-Wit Kim, Boorym Choi, Joon Woo Lee","doi":"10.1007/s00256-024-04796-z","DOIUrl":"https://doi.org/10.1007/s00256-024-04796-z","url":null,"abstract":"<p><strong>Objective: </strong>To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).</p><p><strong>Materials and methods: </strong>This retrospective study involved 109 patients undergoing LCTs and ACTs between January 2014 and July 2021. The dural sac on CT images was manually segmented and classified as normal or stenosed (dural sac cross-sectional area ≥ 100 mm² or < 100 mm², respectively). A deep learning model based on U-Net architecture was developed to automatically segment the dural sac and classify the central canal stenosis. The classification performance of the model was compared on a testing set (990 images from 9 patients). The accuracy, sensitivity, and specificity of automatic segmentation were quantitatively evaluated by comparing its Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) with those of manual segmentation.</p><p><strong>Results: </strong>In total, 990 CT images from nine patients (mean age ± standard deviation, 77 ± 7 years; six men) were evaluated. The algorithm achieved high segmentation performance with a DSC of 0.85 ± 0.10 and ICC of 0.82 (95% confidence interval [CI]: 0.80,0.85). The ICC between ACTs and LCTs on the deep learning algorithm was 0.89 (95%CI: 0.87,0.91). The accuracy of the algorithm in diagnosing LCCS with dichotomous classification was 84%(95%CI: 0.82,0.86). In dataset analysis, the accuracy of ACTs and LCTs was 85%(95%CI: 0.82,0.88) and 83%(95%CI: 0.79,0.86), respectively. The model showed better accuracy for ACT than LCT.</p><p><strong>Conclusion: </strong>The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.</p>","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anterior talofibular ligament footprint dimension measured using three-dimensional magnetic resonance imaging.","authors":"Kenta Kono, Satoshi Yamaguchi, Seiji Kimura, Yukio Mikami, Kaoru Kitsukawa, Koji Matsumoto, Mutsuaki Edama, Yuki Shiko, Manato Horii, Takahisa Sasho, Seiji Ohtori","doi":"10.1007/s00256-024-04778-1","DOIUrl":"https://doi.org/10.1007/s00256-024-04778-1","url":null,"abstract":"<p><strong>Objective: </strong>Knowledge of footprint anatomy is essential for ankle anterior talofibular ligament repair and reconstruction. We aimed to determine the intra- and inter-rater measurement reliability of the anterior talofibular ligament footprint dimension using three-dimensional MRI.</p><p><strong>Methods: </strong>MRI images of 20 ankles with intact ligaments, including 11 with a single bundle and nine with double-bundle ligaments, were analyzed. Imaging was performed using a 3.0-Tesla MRI. Isotropic three-dimensional proton density-weighted images with a voxel size of 0.6 mm were obtained. The fibular and talar footprints were manually segmented using image processing software to create three-dimensional ligament footprints. The lengths, widths, and areas of each sample were measured. A certified orthopedic surgeon and a senior orthopedic fellow performed the measurements twice at 6-week intervals. The intra- and inter-rater differences in the measurements were calculated.</p><p><strong>Results: </strong>The length, width, and area of the single-bundle fibular footprint were 8.7 mm, 5.4 mm, and 37.4 mm², respectively. Those of the talar footprint were 8.4 mm, 4.3 mm, and 30.1 mm², respectively. The inferior bundle of the double-bundle ligament was significantly smaller than the single and superior bundles (p < 0.001). No differences were observed between intra-rater measurements by either rater, with maximum differences of 0.7 mm, 0.5, and 1.7 mm², in length, width, and area, respectively. The maximum inter-rater measurement differences were 1.9 mm, 0.5, and 2.4 mm², respectively.</p><p><strong>Conclusion: </strong>Measurements of the anterior talofibular ligament dimensions using three-dimensional MRI were sufficiently reliable. This measurement method provides in vivo quantitative data on ligament footprint anatomy.</p>","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Test yourself answer: Multiple bone and subcutaneous lesions.","authors":"Diogo Goulart Corrêa, Talita Mourão Chaves Corriça Loyola, Marcelo Bragança Dos Reis Oliveira, Flavia Martins Costa","doi":"10.1007/s00256-024-04791-4","DOIUrl":"https://doi.org/10.1007/s00256-024-04791-4","url":null,"abstract":"","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neck pain in a 32-year-old female.","authors":"Sheng Dai, Youwen Dong","doi":"10.1007/s00256-024-04789-y","DOIUrl":"https://doi.org/10.1007/s00256-024-04789-y","url":null,"abstract":"","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Declining Medicare reimbursement in spinal imaging: a 15-year review.","authors":"Evan H Richman, Parker J Brown, Ian D Minzer, Joseph C Brinkman, Michael S Chang","doi":"10.1007/s00256-024-04792-3","DOIUrl":"https://doi.org/10.1007/s00256-024-04792-3","url":null,"abstract":"<p><strong>Objective: </strong>To analyze and quantify the change in United States of America Medicare reimbursement rates for the 30 most commonly performed spinal imaging procedures.</p><p><strong>Materials and methods: </strong>The Physician Fee Schedule Look-Up Tool from the Centers for Medicare & Medicaid Services was utilized to find and extract the 28 most billed spinal imaging procedures. All data was adjusted for inflation and listed in 2020 US dollars. Percent change in reimbursement and Relative Value Units between 2005 and 2020, both unadjusted and adjusted, were calculated and compared. Additionally, percent change per year and compound annual growth rate were calculated and compared.</p><p><strong>Results: </strong>After adjusting for inflation, the average reimbursement for all analyzed spinal imaging procedures between the years 2005 and 2020 decreased by 45.9%. The adjusted reimbursement rate for all procedures decreased at an average 4.3% per year and experienced an average compound annual growth rate (CAGR) of - 4.4%. Magnetic resonance imaging (MRI) had the most substantial adjusted decline of all imaging modalities at - 72.6%, whereas x-ray imaging had the smallest decline at - 27.33%. The average total RVUs per procedure decreased by 50.1%, from 7.96 to 3.97.</p><p><strong>Conclusion: </strong>From the years 2005 to 2020, Medicare reimbursement significantly decreased for all advanced imaging modalities involving the most common spinal imaging procedures. Among all practices, imaging procedures may be experiencing some of the largest decreases from Medicare reimbursement cutbacks.</p>","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Test Yourself: neck pain in a 32-year-old female.","authors":"Sheng Dai, Youwen Dong","doi":"10.1007/s00256-024-04788-z","DOIUrl":"https://doi.org/10.1007/s00256-024-04788-z","url":null,"abstract":"","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Test yourself question: Multiple bone and subcutaneous lesions.","authors":"Diogo Goulart Corrêa, Talita Mourão Chaves Corriça Loyola, Marcelo Bragança Dos Reis Oliveira, Flavia Martins Costa","doi":"10.1007/s00256-024-04790-5","DOIUrl":"https://doi.org/10.1007/s00256-024-04790-5","url":null,"abstract":"","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model.","authors":"Wolfgang Wirth, Susanne Maschek, Anna Wisser, Jana Eder, Christian F Baumgartner, Akshay Chaudhari, Francis Berenbaum, Felix Eckstein","doi":"10.1007/s00256-024-04786-1","DOIUrl":"https://doi.org/10.1007/s00256-024-04786-1","url":null,"abstract":"<p><strong>Objective: </strong>A fully automated laminar cartilage composition (MRI-based T2) analysis method was technically and clinically validated by comparing radiographically normal knees with (CL-JSN) and without contra-lateral joint space narrowing or other signs of radiographic osteoarthritis (OA, CL-noROA).</p><p><strong>Materials and methods: </strong>2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (All_E), or from the 1st echo only (1^st_E) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the All_E U-Net was then applied to knees from the OAI healthy reference cohort (n = 10), CL-JSN (n = 39), and (1:1) matched CL-noROA knees (n = 39) that all had manual expert segmentation, and to 982 non-matched CL-noROA knees without expert segmentation.</p><p><strong>Results: </strong>The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (All_E/1^st_E) and 0.88 ± 0.03/0.88 ± 0.03 (All_E/1^st_E) across femorotibial cartilage plates. The deviation between automated vs. manually derived laminar T2 reached up to - 2.2 ± 2.6 ms/ + 4.1 ± 10.2 ms (All_E/1^st_E). The All_E U-Net showed a similar sensitivity to cross-sectional laminar T2 differences between CL-JSN and CL-noROA knees in the matched (Cohen's D ≤ 0.54) and the non-matched (D ≤ 0.54) comparison as the matched manual analyses (D ≤ 0.48). Longitudinally, the All_E U-Net also showed a similar sensitivity to CL-JSN vs. CS-noROA differences in the matched (D ≤ 0.51) and the non-matched (D ≤ 0.43) comparison as matched manual analyses (D ≤ 0.41).</p><p><strong>Conclusion: </strong>The fully automated T2 analysis showed a high agreement, acceptable accuracy, and similar sensitivity to cross-sectional and longitudinal laminar T2 differences in an early OA model, compared with manual expert analysis.</p><p><strong>Trial registration: </strong>Clinicaltrials.gov identification: NCT00080171.</p>","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing lower back pain with right L4 radiculopathy: question.","authors":"R Meli, M Hussein, M Czyz, R Henderson, S Vaiyapuri, U Pohl, C Azzopardi, R Botchu","doi":"10.1007/s00256-024-04782-5","DOIUrl":"https://doi.org/10.1007/s00256-024-04782-5","url":null,"abstract":"","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}