Opportunistic Screening of Bone Fragility Using Computed Tomography.

Opportunistic screening uses existing imaging studies for additional diagnostic insights without imposing further burden on patients. We explore the potential of opportunistic computed tomography (CT) screening for osteoporosis, a condition affecting 500 million people globally and leading to significant health care costs and fragility fractures. Although dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosing osteoporosis, > 50% of fractures occur in individuals not screened previously with DXA. With recent advancements in technology, CT has emerged as the most promising tool for opportunistic screening due to its wide use and the ability to provide quantitative measurements of bone attenuation, a surrogate of bone mineral density. This article discusses the technical considerations, calibration methods, and potential benefits of CT for osteoporosis screening. It also explores the role of automation, supervised and unsupervised, in streamlining the diagnostic process, improving accuracy, and potentially developing new biomarkers of bone health. The potential addition of radiomics and genomics is also highlighted, showcasing the synergy between genetic and imaging data for a more comprehensive understanding of osteoporosis pathophysiology and with it possible novel osteoporosis therapies. The future of opportunistic CT screening holds significant promise, with automation and advanced image processing ultimately enhancing patient care, reducing rates of osteoporotic fractures, and improving patient outcomes.

Merlin: A Vision Language Foundation Model for 3D Computed Tomography.

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

Myositis and Its Mimics: Guideline Updates, MRI Characteristics, and New Horizons.

Myositis is defined as inflammation within skeletal muscle and is a subcategory of myopathy, which is more broadly defined as any disorder affecting skeletal muscle. Myositis may be encountered as a component of autoimmune and connective tissue diseases, where it is described as idiopathic inflammatory myopathy (IIM). Myositis can also be caused by infections as well as toxins and drugs, including newer classes of medications. MRI plays an important role in the diagnosis and evaluation of patients with suspected myositis, but many entities may have imaging features similar to those of myositis and can be considered myositis mimics. These include muscular dystrophies, denervation, deep venous thrombosis, diabetic myonecrosis, muscle injury, heterotopic ossification, and even neoplasms. In patients with suspected myositis, definitive diagnosis may require integrated analysis of imaging findings with clinical, laboratory, and pathology data. The objectives of this article are to review the fundamental features of myositis, including recent updates in terminology and consensus guidelines for IIMs; the most important MRI differential diagnostic considerations for myositis (i.e., myositis mimics); and new horizons, including the potential importance of artificial intelligence and multimodal integrated diagnostics in the evaluation of patients with muscle disorders.

MRI of Soft-Tissue Tumors: What to Include in the Report.

MRI serves as a critical step in the workup, local staging, and treatment planning of extremity soft-tissue masses. For the radiologist to meaningfully contribute to the management of soft-tissue masses, they need to provide a detailed list of descriptors of the lesion outlined in an organized report. While it is occasionally possible to use MRI to provide a diagnosis for patients with a mass, it is more often used to help with determining the differential diagnosis and planning of biopsies, surgery, radiation treatment, and chemotherapy (when provided). Each descriptor on the list outlined in this article is specifically aimed to assist in one or more facets of the overall approach to soft-tissue masses. This applies to all masses, but in particular sarcomas. Those descriptors are useful to help narrow the differential diagnosis and ensure concordance with a pathologic diagnosis and its accompanying grade assignment of soft-tissue sarcomas. These include a lesion's borders and shape, signal characteristics, and contrast enhancement pattern; the presence of peritumoral edema and peritumoral enhancement; and the presence of lymph nodes. The items most helpful in assisting surgical planning include a lesion's anatomic location, site of origin, size, location relative to a landmark, relationship to adjacent structures, and vascularity including feeding and draining vessels. The authors provide some background information on soft-tissue sarcomas, including their diagnosis and treatment, for the general radiologist and as a refresher for radiologists who are more experienced in tumor imaging. ©RSNA, 2024 See the invited commentary by Murphey in this issue.

Chronic Nonspinal Osteomyelitis in Adults: Consensus Recommendations on Percutaneous Bone Biopsies from the Society of Academic Bone Radiologists.

The diagnosis and management of chronic nonspinal osteomyelitis can be challenging, and guidelines regarding the appropriateness of performing percutaneous image-guided biopsies to acquire bone samples for microbiological analysis remain limited. An expert panel convened by the Society of Academic Bone Radiologists developed and endorsed consensus statements on the various indications for percutaneous image-guided biopsies to standardize care and eliminate inconsistencies across institutions. The issued statements pertain to several commonly encountered clinical presentations of chronic osteomyelitis and were supported by a literature review. For most patients, MRI can help guide management and effectively rule out osteomyelitis when performed soon after presentation. Additionally, in the appropriate clinical setting, open wounds such as sinus tracts and ulcers, as well as joint fluid aspirates, can be used for microbiological culture to determine the causative microorganism. If MRI findings are positive, surgery is not needed, and alternative sites for microbiological culture are not available, then percutaneous image-guided biopsies can be performed. The expert panel recommends that antibiotics be avoided or discontinued for an optimal period of 2 weeks prior to a biopsy whenever possible. Patients with extensive necrotic decubitus ulcers or other surgical emergencies should not undergo percutaneous image-guided biopsies but rather should be admitted for surgical debridement and intraoperative cultures. Multidisciplinary discussion and approach are crucial to ensure optimal diagnosis and care of patients diagnosed with chronic osteomyelitis.

Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study.

BACKGROUND: Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort. METHODS: An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222). FINDINGS: 17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001). INTERPRETATION: PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes. FUNDING: National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.

Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm.

OBJECTIVES: To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans. MATERIALS AND METHODS: Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT". RESULTS: The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification. CONCLUSION: An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability. CLINICAL RELEVANCE STATEMENT: Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging. KEY POINTS: Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.

Body composition measurements and clinical outcomes in patients with resectable pancreatic adenocarcinoma - analysis from SWOG S1505.

BACKGROUND: Sarcopenic obesity and muscle attenuation have been associated with survival in patients with borderline resectable and advanced pancreatic ductal adenocarcinoma (PDA); however, these relationships are unknown for patients with resectable PDA. This study examined the associations between skeletal muscle and adipose tissue as measured on baseline computed tomography (CT) and the overall survival (OS) of participants with resectable PDA in a secondary analysis of the Southwest Oncology Group S1505 clinical trial (identifier: NCT02562716). METHODS: The S1505 phase II clinical trial enrolled patients with resectable PDA who were randomized to receive modified FOLFIRINOX or gemcitabine and nab-paclitaxel as perioperative chemotherapy, followed by surgical resection. Baseline axial CT images at the L3 level were analyzed with externally validated software, and measurements were recorded for skeletal muscle area and skeletal muscle density, visceral adipose tissue area (VATA) and density, and subcutaneous adipose tissue area and density. The relationships between CT metrics and OS were analyzed using Cox regression models, with adjustment for baseline participant characteristics. RESULTS: Of 98 eligible participants with available baseline abdominal CT, 8 were excluded because of imaging quality (eg, orthopedic hardware), resulting in 90 evaluable cases: 51 men (57.0%; mean age, 63.2 years [SD, 8.5]; mean body mass index [BMI], 29.3 kg/m2 [SD, 6.4]), 80 White (89.0%), 6 Black (7.0%), and 4 unknown race (4.0%). Sarcopenia was present in 32 participants (35.9%), and sarcopenic obesity was present in 10 participants (11.2%). Univariable analyses for the 6 variables of interest indicated that the standardized mean difference (hazard ratio [HR], 0.75; 95% CI, 0.57-0.98; P = .04) was statistically significantly associated with OS. In models adjusted for sex, race, age, BMI, performance score, contrast use, sarcopenia, and sarcopenic obesity, VATA was statistically significantly associated with OS (HR, 1.58; 95% CI, 1.00-2.51; P = .05). No difference was observed in OS between participants according to sarcopenic obesity or sarcopenia categories. The median OS estimates were 25.1 months for participants without sarcopenic obesity, 18.6 months for participants with sarcopenic obesity, 23.6 months for participants without sarcopenia, and 27.9 months for participants with sarcopenia. CONCLUSION: This was the first study to systematically evaluate body composition parameters in a prospective multicenter trial of patients with resectable PDA who received perioperative chemotherapy. Visceral adipose tissue was associated with survival; however, there was no association between OS and sarcopenia or sarcopenic obesity. Further studies should evaluate these findings in more detail.

Biomarkers of Body Composition.

The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.

Clinical, functional, and opportunistic CT metrics of sarcopenia at the point of imaging care: analysis of all-cause mortality.

PURPOSE: This study examines clinical, functional, and CT metrics of sarcopenia and all-cause mortality in older adults undergoing outpatient imaging. METHODS: The study included outpatients ≥ 65 years of age undergoing CT or PET/CT at a tertiary care institution. Assessments included screening questionnaires for sarcopenia (SARC-F) and frailty (FRAIL scale), and measurements of grip strength and usual gait speed (6 m course). Skeletal muscle area (SMA), index (SMI, area/height2) and density (SMD) were measured on CT at T12 and L3. A modified SMI was also examined (SMI-m, area/height). Mortality risk was studied with Cox proportional hazard analysis. RESULTS: The study included 416 patients; mean age 73.8 years [sd 6.2]; mean follow-up 2.9 years (sd 1.34). Abnormal grip, SARC-F, and FRAIL scale assessments were associated with higher mortality risk (HR [95%CI] = 2.0 [1.4-2.9], 1.6 [1.1-2.3], 2.0 [1.4-2.8]). Adjusting for age, higher L3-SMA, T12-SMA, T12-SMI and T12-SMI-m were associated with lower mortality risk (HR [95%CI] = 0.80 [0.65-0.90], 0.76 [0.64-0.90], 0.84 [0.70-1.00], and 0.80 [0.67-0.90], respectively). T12-SMD and L3-SMD were not predictive of mortality. After adjusting for abnormal grip strength and FRAIL scale assessments, T12-SMA and T12-SMI-m remained predictive of mortality risk (HR [95%CI] = 0.83 [0.70-1.00] and 0.80 [0.67-0.97], respectively). CONCLUSION: CT areal metrics were weaker predictors of all-cause mortality than clinical and functional metrics of sarcopenia in our older patient cohort; a CT density metric (SMD) was not predictive. Of areal CT metrics, SMI (area/height2) appeared to be less effective than non-normalized SMA or SMA normalized by height1.

Skeletal Muscle Area on CT: Determination of an Optimal Height Scaling Power and Testing for Mortality Risk Prediction.

BACKGROUND. Sarcopenia is commonly assessed on CT by use of the skeletal muscle index (SMI), which is calculated as the skeletal muscle area (SMA) at L3 divided by patient height squared (i.e., a height scaling power of 2). OBJECTIVE. The purpose of this study was to determine the optimal height scaling power for SMA measurements on CT and to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. METHODS. This retrospective study included 16,575 patients (6985 men, 9590 women; mean age, 56.4 years) who underwent abdominal CT from December 2012 through October 2018. The SMA at L3 was determined using automated software. The sample was stratified into two groups: 5459 patients without major medical conditions (based on ICD-9 and ICD-10 codes) who were included in the analysis for determining the optimal height scaling power and 11,116 patients with major medical conditions who were included for the purpose of testing this power. The optimal scaling power was determined by allometric analysis (whereby regression coefficients were fitted to log-linear sex-specific models relating height to SMA) and by analysis of statistical independence of SMI from height across scaling powers. Cox proportional hazards models were used to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. RESULTS. In allometric analysis, the regression coefficient of log(height) in patients 40 years old and younger was 1.02 in men and 1.08 in women, and in patients older than 40 years old, it was 1.07 in men and 1.10 in women (all p < .05 vs regression coefficient of 2). In analyses for statistical independence of SMI from height, the optimal height scaling power (i.e., those yielding correlations closest to 0) was, in patients 40 years old and younger, 0.97 in men and 1.08 in women, whereas in patients older than 40 years old, it was 1.03 in men and 1.09 in women. In the Cox model used for testing, SMI predicted all-cause mortality with a higher concordance index using of a height scaling power of 1 rather than 2 in men (0.675 vs 0.663, p < .001) and in women (0.664 vs 0.653, p < .001). CONCLUSION. The findings support a height scaling power of 1, rather than a conventional power of 2, for SMI computation. CLINICAL IMPACT. A revised height scaling power for SMI could impact the utility of CT-based sarcopenia diagnoses in risk assessment.

Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults.

RATIONALE AND OBJECTIVES: Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT)-derived measurements of muscle and adipose tissue are associated with frailty. MATERIALS AND METHODS: Outpatients aged ≥ 55 years consecutively imaged with contrast-enhanced abdominopelvic CT over a 3-month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT-derived variables were compared between sexes. Sex-specific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters. RESULTS: The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for pre-frailty (ie, 0.10 < eFI ≤ 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (ß = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (ß = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT-derived muscle or adipose tissue metrics were associated with frailty. CONCLUSION: We observed a positive association between frailty and CT-derived biomarkers of myosteatosis and visceral adiposity in a sex-dependent manner.

MRI of patellar stabilizers: Anatomic visibility, inter-reader reliability, and intra-reader reproducibility of primary and secondary ligament anatomy.

OBJECTIVE: To compare MRI features of medial and lateral patellar stabilizers in patients with and without patellar instability. METHODS: Retrospective study of 196 patients (mean age, 33.1 ± 18.5 years; 119 women) after diagnosis of patellar instability (cohort-1, acute patellar dislocation; cohort-2, chronic patellar maltracking) or no patellar instability (cohort-3, acute ACL rupture; cohort-4, chronic medial meniscus tear). On MRI, four medial and four lateral stabilizers were evaluated for visibility and injury by three readers independently. Inter- and intra-reader agreement was determined. RESULTS: Medial and lateral patellofemoral ligaments (MPFL and LPFL) were mostly or fully visualized in all cases (100%). Of the secondary patellar stabilizers, the medial patellotibial ligament was mostly or fully visualized in 166 cases (84.7%). Other secondary stabilizers were mostly or fully visualized in only a minority of cases (range, 0.5-32.1%). Injury scores for all four medial stabilizers were higher in patients with acute patellar dislocation than the other 3 cohorts (p < .05). Visibility inter- and intra-reader agreement was good for medial stabilizers (κ 0.61-0.78) and moderate-to-good for lateral stabilizers (κ 0.40-0.72). Injury inter- and intra-reader agreement was moderate-to-excellent for medial stabilizers (κ 0.43-0.90) and poor-to-moderate for lateral stabilizers (κ 0-0.50). CONCLUSION: The MPFL and LPFL were well visualized on MRI while the secondary stabilizers were less frequently visualized. The secondary stabilizers were more frequently visualized medially than laterally, and patellotibial ligaments were more frequently visualized compared to the other secondary stabilizers. Injury to the medial stabilizers was more common with acute patellar dislocation than with chronic patellar maltracking or other knee injuries.

Relationships between diagnostic imaging of first carpometacarpal osteoarthritis and pain, functional status, and disease progression: A systematic review.

OBJECTIVE: To systematically review the association of pain, function, and progression in first carpometacarpal (CMC) osteoarthritis (OA) with imaging biomarkers and radiography-based staging. DESIGN: Database searches in PubMed, Embase, and the Cochrane Library, along with citation searching were conducted in accordance with published guidance. Data on the association of imaging with pain, functional status, and disease progression were extracted and synthesized, along with key information on study methodology such as sample sizes, use of control subjects, study design, number of image raters, and blinding. Methodological quality was assessed using National Heart, Lung, and Blood Institute tools. RESULTS: After duplicate removal, a total of 1969 records were screened. Forty-six articles are included in this review, covering a total of 28,202 study participants, 7263 with first CMC OA. Osteophytes were found to be one of the strongest biomarkers for pain across imaging modalities. Radiographic findings alone showed conflicting relationships with pain. However, Kellgren-Lawrence staging showed consistent associations with pain in various studies. Radiographic, sonographic, and MRI findings and staging showed little association to tools evaluating functional status across imaging modalities. The same imaging methods showed limited ability to predict progression of first CMC OA. A major limitation was the heterogeneity in the study base, limiting synthesis of results. CONCLUSION: Imaging findings and radiography-based staging systems generally showed strong associations with pain, but not with functional status or disease progression. More research and improved imaging techniques are needed to help physicians better manage patients with first CMC OA.

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach.

Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient's electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.

Real-time MRI of the moving wrist at 0.55 tesla.

OBJECTIVES: Magnetic resonance imaging (MRI) using 1.5T or 3.0T systems is routinely employed for assessing wrist pathology; however, due to off-resonance artifacts and high power deposition, these high-field systems have drawbacks for real-time (RT) imaging of the moving wrist. Recently, high-performance 0.55T MRI systems have become available. In this proof-of-concept study, we tested the hypothesis that RT-MRI during continuous, active, and uninterrupted wrist motion is feasible with a high-performance 0.55T system at temporal resolutions below 100 ms and that the resulting images provide visualization of tissues commonly interrogated for assessing dynamic wrist instability. METHODS: Participants were scanned during uninterrupted wrist radial-ulnar deviation and clenched fist maneuvers. Resulting images (nominal temporal resolution of 12.7-164.6 ms per image) were assessed for image quality. Feasibility of static MRI to supplement RT-MRI acquisition was also tested. RESULTS: The RT images with temporal resolutions < 100 ms demonstrated low distortion and image artifacts, and higher reader assessment scores. Static MRI scans showed the ability to assess anatomical structures of interest in the wrist. CONCLUSION: RT-MRI of the wrist at a high temporal resolution, coupled with static MRI, is feasible with a high-performance 0.55T system, and may enable improved assessment of wrist dynamic dysfunction and instability. ADVANCES IN KNOWLEDGE: Real-time MRI of the moving wrist is feasible with high-performance 0.55T and may improve the evaluation of dynamic dysfunction of the wrist.

Muscle Steatosis and Fibrosis in Older Adults, From the AJR Special Series on Imaging of Fibrosis.

The purpose of this article is to review steatosis and fibrosis of skeletal muscle, focusing on older adults. Although CT, MRI, and ultrasound are commonly used to image skeletal muscle and provide diagnoses for a variety of medical conditions, quantitative assessment of muscle steatosis and fibrosis is uncommon. This review provides radiologists with a broad perspective on muscle steatosis and fibrosis in older adults by considering their public health impact, biologic mechanisms, and evaluation using CT, MRI, and ultrasound. Promising directions in clinical research that employ artificial intelligence algorithms and the imaging assessment of biologic age are also reviewed. The presented imaging methods hold promise for improving the evaluation of common conditions affecting older adults including sarcopenia, frailty, and cachexia.

Knee MRI: Meniscus Roots, Ramps, Repairs, and Repercussions.

Menisci play an essential role in maintaining normal pain-free function of the knee. While there are decades of MRI literature on the tears involving the meniscus body and horns, there is now a surge in knowledge regarding injuries at the meniscus roots and periphery. The authors briefly highlight new insights into meniscus anatomy and then summarize recent developments in the understanding of meniscus injuries that matter, emphasizing meniscus injuries at the root and peripheral (eg, ramp) regions that may be missed easily at MRI and arthroscopy. Root and ramp tears are important to diagnose because they may be amenable to repair. However, if these tears are left untreated, ongoing pain and accelerated cartilage degeneration may ensue. The posterior roots of the medial and lateral menisci are most commonly affected by injury, and each of these injuries is associated with distinctive clinical profiles, MRI findings, and tear patterns. Specific diagnostic pitfalls can make the roots challenging to evaluate, including MRI artifacts and anatomic variations. As with root tears, MRI interpretation and orthopedic treatment have important differences for injuries at the medial versus lateral meniscus (LM) periphery (located at or near the meniscocapsular junction). Medially, ramp lesions typically occur in the setting of an anterior cruciate ligament rupture and are generally classified into five patterns. Laterally, the meniscocapsular junction may be injured in association with tibial plateau fractures, but disruption of the popliteomeniscal fascicles may also result in a hypermobile LM. Updated knowledge of the meniscus root and ramp tears is crucial in optimizing diagnostic imaging before repair and understanding the clinical repercussions. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available in the Online Learning Center.

Patellar Tracking: An Old Problem with New Insights.

Patellofemoral pain and instability are common indications for imaging that are encountered in everyday practice. The authors comprehensively review key aspects of patellofemoral instability pertinent to radiologists that can be seen before the onset of osteoarthritis, highlighting the anatomy, clinical evaluation, diagnostic imaging, and treatment. Regarding the anatomy, the medial patellofemoral ligament (MPFL) is the primary static soft-tissue restraint to lateral patellar displacement and is commonly reconstructed surgically in patients with MPFL dysfunction and patellar instability. Osteoarticular abnormalities that predispose individuals to patellar instability include patellar malalignment, trochlear dysplasia, and tibial tubercle lateralization. Clinically, patients with patellar instability may be divided into two broad groups with imaging findings that sometimes overlap: patients with a history of overt patellar instability after a traumatic event (eg, dislocation, subluxation) and patients without such a history. In terms of imaging, radiography is generally the initial examination of choice, and MRI is the most common cross-sectional examination performed preoperatively. For all imaging techniques, there has been a proliferation of published radiologic measurement methods. The authors summarize the most common validated measurements for patellar malalignment, trochlear dysplasia, and tibial tubercle lateralization. Given that static imaging is inherently limited in the evaluation of patellar motion, dynamic imaging with US, CT, or MRI may be requested by some surgeons. The primary treatment strategy for patellofemoral pain is conservative. Surgical treatment options include MPFL reconstruction with or without osseous corrections such as trochleoplasty and tibial tubercle osteotomy. Postoperative complications evaluated at imaging include patellar fracture, graft failure, graft malposition, and medial patellar subluxation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.