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.

MR Imaging of Benign Soft Tissue Tumors: Highlights for the Practicing Radiologist.

The overwhelming majority of soft tissue masses encountered on routine imaging are incidental and benign. When incidental, the radiologist is usually limited to routine MR imaging sequences, often without contrast. In these situations, there are typical imaging features pointing to a single diagnosis or limited differential diagnosis. Although these imaging features can be helpful, many lesions are nonspecific and may require contrast administration, evaluation with other imaging modalities, follow-up imaging, or biopsy for diagnosis. This article will provide an overview of the most commonly encountered benign soft tissue masses along with some of their characteristic MR imaging features.

Society of Skeletal Radiology- white paper. Guidelines for the diagnostic management of incidental solitary bone lesions on CT and MRI in adults: bone reporting and data system (Bone-RADS).

The purpose of this article is to present algorithms for the diagnostic management of solitary bone lesions incidentally encountered on computed tomography (CT) and magnetic resonance (MRI) in adults. Based on review of the current literature and expert opinion, the Practice Guidelines and Technical Standards Committee of the Society of Skeletal Radiology (SSR) proposes a bone reporting and data system (Bone-RADS) for incidentally encountered solitary bone lesions on CT and MRI with four possible diagnostic management recommendations (Bone-RADS1, leave alone; Bone-RADS2, perform different imaging modality; Bone-RADS3, perform follow-up imaging; Bone-RADS4, biopsy and/or oncologic referral). Two algorithms for CT based on lesion density (lucent or sclerotic/mixed) and two for MRI allow the user to arrive at a specific Bone-RADS management recommendation. Representative cases are provided to illustrate the usability of the algorithms.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

Soft tissue pathology for the radiologist: a tumor board primer with 2020 WHO classification update.

Radiologists serve an important role in the diagnosis and staging of soft tissue tumors, often through participation in multidisciplinary tumor board teams. While an important function of the radiologist is to review pertinent imaging and assist in the differential diagnosis, a critical role is to ensure that there is concordance between the imaging and the pathologic diagnosis. This requires a basic understanding of the pathology of soft tissue tumors, particularly in the case of diagnostic dilemmas or incongruent imaging and histologic features. This work is intended to provide an overview of soft tissue pathology for the radiologist to optimize participation in multidisciplinary orthopedic oncology tumor boards, allowing for contribution to management decisions with expertise beyond image interpretation.

Capsular thinning on magnetic resonance arthrography is associated with intra-operative hip joint laxity in women.

Hip microinstability is a recognized cause of hip pain in young patients. Intra-operative evaluation is used to confirm the diagnosis, but limited data exist associating magnetic resonance arthrography (MRA) findings with hip microinstability. To determine if a difference exists in the thickness of the anterior joint capsule and/or the width of the anterior joint recess on MRA in hip arthroscopy patients with and without an intra-operative diagnosis of hip laxity. Sixty-two hip arthroscopy patients were included in the study. Two musculoskeletal radiologists blinded to surgical results reviewed the MRAs for two previously described findings: (i) anterior joint capsule thinning; (ii) widening of the anterior joint recess distal to the zona orbicularis. Operative reports were reviewed for the diagnosis of joint laxity. In all patients with and without intra-operative laxity, there were no significant differences with either MRA measurement. However, twenty-six of 27 patients with intra-operative laxity were women compared with 11 of 35 patients without laxity (P < 0.001). In subgroup analysis of women, the intra-operative laxity group had a higher rate of capsular thinning compared with the non-laxity group (85% versus 45%; P = 0.01). A 82% of women with capsular thinning also had intra-operative laxity, compared with 40% without capsular thinning (P = 0.01). There were no differences regarding the width of the anterior joint recess. In this study, there was an association between capsular thinning and intra-operative laxity in female patients. Measuring anterior capsule thickness on a pre-operative MRA may be useful for the diagnosis of hip microinstability.

Erratum: Author Correction: Human-machine partnership with artificial intelligence for chest radiograph diagnosis.

[This corrects the article DOI: 10.1038/s41746-019-0189-7.].

Human-machine partnership with artificial intelligence for chest radiograph diagnosis.

Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models. Our work demonstrates that both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy than the human experts alone. Our work further demonstrates that when used in combination, the swarm-based technology and deep-learning technology outperformed either method alone. The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice.

The Cliff Sign: A New Radiographic Sign of Hip Instability.

BACKGROUND: The preoperative diagnosis of hip microinstability is challenging. Although physical examination maneuvers and magnetic resonance imaging findings associated with microinstability have been described, there are limited reports of radiographic features. In patients with microinstability, we observed a high incidence of a steep drop-off on the lateral edge of the femoral head, which we have named the "cliff sign." PURPOSE: (1) To determine the relationship of the cliff sign and associated measurements with intraoperative microinstability and (2) to determine the interobserver reliability of these measurements. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: A total of 115 consecutive patients who underwent hip arthroscopy were identified. Patients with prior hip surgery, Legg-Calve-Perthes disease, fractures, pigmented villonodular synovitis, or synovial chondromatosis were excluded, resulting in the inclusion of 96 patients in the study. A perfect circle around the femoral head was created on anteroposterior pelvis radiographs. If the lateral femoral head did not completely fill the perfect circle, it was considered a positive cliff sign. Five additional measurements relating to the cliff sign were calculated. The diagnosis of microinstability was made intraoperatively by the (1) amount of traction required to distract the hip, (2) lack of hip reduction after initial traction release following joint venting, or (3) intraoperative findings consistent with hip microinstability. Continuous variables were analyzed through use of unpaired t tests and discrete variables with Fisher exact tests. Interobserver reliability (n = 3) was determined for each measurement. RESULTS: Overall, 89% (39/44) of patients with microinstability had a cliff sign, compared with 27% of patients (14/52) without instability (P < .0001). Conversely, 74% of patients with a cliff sign had microinstability, while only 12% of patients without a cliff sign had instability (P < .0001). In women younger than 32 years with a cliff sign, 100% (20/20) were diagnosed with instability. No differences were found in any of the 5 additional measurements. Excellent interobserver reliability was found for the presence of a cliff sign and the cliff angle measurement. CONCLUSION: We have identified a radiographic finding, the cliff sign, that is associated with the intraoperative diagnosis of hip microinstability and has excellent interobserver reliability. Results showed that 100% of young women with a cliff sign had intraoperative microinstability. The cliff sign may be useful in the preoperative diagnosis of hip microinstability.

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.

BACKGROUND: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. METHODS AND FINDINGS: Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. CONCLUSIONS: Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

Extreme Sports Injuries to the Pelvis and Lower Extremity.

Extreme sports are growing in popularity, and physicians are becoming increasingly aware of injuries related to these activities. Imaging plays a key role in diagnosing and determining clinical management of many of these injuries. This article describes general imaging techniques and findings in various injuries specific to multiple extreme sports.

Extraskeletal osteosarcoma of the hand: the role of marginal excision and adjuvant radiation therapy.

BACKGROUND: Extraskeletal osteosarcoma of the hand is rare, and its optimal modality of local control is not currently known. METHODS: A literature search was performed to identify studies that describe the treatment and outcomes of extraskeletal osteosarcoma. A second literature search was performed to identify studies that describe the treatment and outcomes of extraskeletal osteosarcoma of the hand specifically. RESULTS: The role of adjuvant radiation for extraskeletal osteosarcoma is not well defined. All cases in the literature describing treatment of extraskeletal osteosarcoma of the hand utilized amputation, and none of the patients described received radiation therapy. However, there are multiple reports showing excellent local control, minimal toxicity, and superior functional outcome with limb conservation and radiation rather than amputation of the hand in pediatric and adult soft tissue sarcoma. CONCLUSION: For extraskeletal osteosarcoma of the hand, we recommend a treatment approach with the goal of preservation of form and function using limb-sparing surgery and planned postoperative radiation.

Approach to MR Imaging of the Elbow and Wrist: Technical Aspects and Innovation.

Wrist and elbow MR imaging technology is advancing at a dramatic rate. Wrist and elbow MR imaging is performed at medium and higher field strengths with more specialized surface coils and more variable pulse sequences and postprocessing techniques. High field imaging and improved coils lead to an increased signal-to-noise ratio and increased variety of soft tissue contrast options. Three-dimensional imaging is improving in terms of usability and artifacts. Some of these advances have challenges in wrist and elbow imaging, such as postoperative patient imaging, cartilage mapping, and molecular imaging. This review considers technical advances in hardware and software and their clinical applications.

Medical student selection criteria and socio-demographic factors as predictors of ultimately working rurally after graduation.

BACKGROUND: We have previously demonstrated that both coming from a rural background and spending a year-long clinical rotation in our Rural Clinical School (RCS) have independent and additive effects to increase the likelihood of medical students practicing rurally following graduation. The current study assesses the extent to which medical school selection criteria and/or the socio-demographic profile of medical students may further facilitate or hamper the selection of students ultimately destined for the rural medical workforce. METHODS: The study comprised 729 students, admitted from secondary school since 1999 and having graduated by 2011, whose actual workplace location in 2014 was classified as either urban or rural using the Australian Health Practitioner Regulation Agency database. Selection factors on entry (score from a standardised interview, percentile scores for the 3 components of the Undergraduate Medicine and Health Sciences Admission Test (UMAT) and prior academic performance as assessed by the Australian Tertiary Admissions Rank) together with socio-demographic factors (age, gender, decile for the Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD)), were examined in relation to ultimate rural destination of practice. RESULTS: In logistic regression, those practicing in a rural location in 2014 were more likely to have come from the lower 6 IRSAD deciles (OR 2.75, 95% CI 1.44, 5.23, P = 0.002), to be older (OR 1.86, 95% CI 1.09, 3.18, p = 0.023) and to have a lower UMAT-3 (Non-verbal communication) score (OR 0.98, 95% CI 0.97, 0.99, P = 0.005). After further controlling for either rural background or RCS participation, only age and UMAT-3 remained as independent predictors of current rural practice. CONCLUSIONS: In terms of the socio-demographic profiles of those selected for medical school entry from secondary school, only older age weakly augmented the selection of graduates likely to ultimately work in a rural destination. Among the selection factors, having achieved higher scores in UMAT-3 tended to mitigate this outcome. The major focus in attempts to grow the rural medical workforce should therefore remain on recruiting medical students from a rural background together with providing maximal opportunity for prolonged immersion in rural clinical environments during their training.

Longitudinal rural clerkships: increased likelihood of more remote rural medical practice following graduation.

BACKGROUND: Extended rural clerkships clearly increase the likelihood of rural practice post-graduation. What has not been determined is whether such rural interventions increase the likelihood of graduates practicing in more remote, versus inner regional, locations. METHODS: The Australian Health Practitioner Regulation Agency database was used to identify the current workplace of every graduate of the Medical School of Western Australia, 1980 to 2011. There were 324 graduates working in a primary practice location defined by the Australian Standard Geographical Classification as inner regional to very remote. They were divided into 3 groups - 200 graduates who entered medical school before commencement of the Rural Clinical School of Western Australia (RCSWA), 63 who entered after the RCSWA had started, but not participated in RCSWA, and 61 who participated in the RCSWA. The RCSWA offers a longitudinal rural clinical clerkship throughout level 5 of the MBBS course. RESULTS: The two groups not participating in the RCSWA had 45.5% and 52.4% of subjects in outer regional/very remote locations, respectively. In comparison, 78.7% of those who had participated in the RCSWA were currently practicing in outer regional/very remote locations. When the 3 groups were compared, the significant predictors of working in a more remote practice compared to working in an inner regional area were being female (OR 1.75 95% CI 1.13, 2.72, P = 0.013) and participating in the RCSWA (OR 4.42, 95% CI 2.26, 8.67, P < 0.001). In multivariate logistic regression that corrected for gender and remoteness of rural address before entry to medical school, participation in the RCSWA still predicted a more than 4-fold increase in the odds of practicing in a more remote area (OR 4.11, 95% CI 2.04, 8.30, P < 0.001). CONCLUSION: Extended rural clinical clerkship during an undergraduate MBBS course is related to a much greater likelihood of practicing in more remote, under-serviced rural locations.

MRI of the hip for the evaluation of femoroacetabular impingement; past, present, and future.

The concept of femoroacetabular impingement (FAI) has, in a relatively short time, come to the forefront of orthopedic imaging. In just a few short years MRI findings that were in the past ascribed to degenerative change, normal variation, or other pathologies must now be described and included in radiology reports, as they have been shown, or are suspected to be related to, FAI. Crucial questions have come up in this time, including: what is the relationship of bony morphology to subsequent cartilage and labral damage, and most importantly, how is this morphology related to the development of osteoarthritis? In this review, we attempt to place a historical perspective on the controversy, provide guidelines for interpretation of MRI examinations of patients with suspected FAI, and offer a glimpse into the future of MRI of this complex condition.

Medical student selection criteria as predictors of intended rural practice following graduation.

BACKGROUND: Recruiting medical students from a rural background, together with offering them opportunities for prolonged immersion in rural clinical training environments, both lead to increased participation in the rural workforce after graduation. We have now assessed the extent to which medical students' intentions to practice rurally may also be predicted by either medical school selection criteria and/or student socio-demographic profiles. METHODS: The study cohort included 538 secondary school-leaver entrants to The University of Western Australia Medical School from 2006 to 2011. On entry they completed a questionnaire indicating intention for either urban or rural practice following graduation. Selection factors (standardised interview score, percentile score from the Undergraduate Medicine and Health Sciences Admission Test (UMAT) and prior academic performance (Australian Tertiary Admissions Rank), together with socio-demographic factors (age, gender, decile for the Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) and an index of rurality) were examined in relation to intended rural or urban destination of practice. RESULTS: In multivariate logistic regression, students from a rural background had a nearly 8-fold increase in the odds of intention to practice rurally after graduation compared to those from urban backgrounds (OR 7.84, 95% CI 4.10, 14.99, P < 0.001). Those intending to be generalists rather than specialists had a more than 4-fold increase in the odds of intention to practice rurally (OR 4.36, 95% CI 1.69, 11.22, P < 0.001). After controlling for these 2 factors, those with rural intent had significantly lower academic entry scores (P = 0.002) and marginally lower interview scores (P = 0.045). UMAT percentile scores were no different. Those intending to work in a rural location were also more likely to be female (OR 1.93, 95% CI 1.08, 3.48, P = 0.027), to come from the lower eight IRSAD deciles (OR 2.52, 95% CI 1.47, 4.32, P = 0.001) and to come from Government vs independent schools (OR 2.02, 95% CI 1.15, 3.55, P = 0.015). CONCLUSIONS: Very high academic scores generally required for medical school entry may have the unintended consequence of selecting fewer graduates interested in a rural practice destination. Increased efforts to recruit students from lower socioeconomic backgrounds may be beneficial in terms of an ultimate intended rural practice destination.

Impact of the Rural Clinical School of Western Australia on work location of medical graduates.

OBJECTIVE: To determine whether completing a year of the Rural Clinical School of Western Australia (RCSWA) program is associated with entering the rural medical workforce. DESIGN AND SETTING: Cohort study of graduates from the University of Western Australia who completed Year 5 of medical school between 2002 and 2009, comparing work location (identified from the Australian Health Practitioner Regulation Agency database in March-June 2013) between those who participated in the RCSWA (RCSWA graduates) and those who did not (controls). MAIN OUTCOME MEASURE: Rural or urban work location of graduates. RESULTS: Of 1116 eligible graduates, 1017 (91.1%) could be traced and were included in the study. Of 258 RCSWA graduates, 42 (16.3%) were working rurally compared with 36 of 759 controls (4.7%). Of 195 RCSWA graduates from urban backgrounds, 29 (14.9%) were working rurally compared with 26 of 691 urban-background controls (3.8%). Of 63 rural-background RCSWA graduates, 13 (20.6%) were working rurally, compared with 10 of 68 rural-background controls (14.7%). Using logistic regression, RCSWA participation had a strong relationship with working rurally (rural-background RCSWA graduates: odds ratio [OR], 7.5; 95% CI, 3.5-15.8; urban-background RCSWA graduates: OR, 5.1; 95% CI, 2.9-9.1). Rural background without RCSWA participation (OR, 4.2; 95% CI, 1.8-9.2) and older age (age in 2012, 30-39 years: OR, 2.2; 95% CI, 1.3-3.7 v ≥ 40 years: OR, 6.6; 95% CI, 2.8-15.0) were also significant factors for working rurally. CONCLUSIONS: Participation in the RCSWA is strongly associated with greater likelihood of working rurally. Graduates from urban backgrounds who participated in the RCSWA were much more likely to work in rural areas than those who did not. These data substantiate the RCSWA as an effective rural workforce strategy.

Online mental health resources in rural Australia: clinician perceptions of acceptability.

BACKGROUND: Online mental health resources have been proposed as an innovative means of overcoming barriers to accessing rural mental health services. However, clinicians tend to express lower satisfaction with online mental health resources than do clients. OBJECTIVE: To understand rural clinicians' attitudes towards the acceptability of online mental health resources as a treatment option in the rural context. METHODS: In-depth interviews were conducted with 21 rural clinicians (general practitioners, psychologists, psychiatrists, and clinical social workers). Interviews were supplemented with rural-specific vignettes, which described clinical scenarios in which referral to online mental health resources might be considered. Symbolic interactionism was used as the theoretical framework for the study, and interview transcripts were thematically analyzed using a constant comparative method. RESULTS: Clinicians were optimistic about the use of online mental health resources into the future, showing a preference for integration alongside existing services, and use as an adjunct rather than an alternative to traditional approaches. Key themes identified included perceptions of resources, clinician factors, client factors, and the rural and remote context. Clinicians favored resources that were user-friendly and could be integrated into their clinical practice. Barriers to use included a lack of time to explore resources, difficulty accessing training in the rural environment, and concerns about the lack of feedback from clients. Social pressure exerted within professional clinical networks contributed to a cautious approach to referring clients to online resources. CONCLUSIONS: Successful implementation of online mental health resources in the rural context requires attention to clinician perceptions of acceptability. Promotion of online mental health resources to rural clinicians should include information about resource effectiveness, enable integration with existing services, and provide opportunities for renegotiating the socially defined role of the clinician in the eHealth era.

Hip-femoral acetabular impingement.

Magnetic resonance imaging (MRI) has become a valuable technology for the diagnosis and treatment of femoroacetabular impingement (FAI). This article reviews the basic pathophysiology of FAI, as well as the techniques and indications for MRI and magnetic resonance arthrography. Normal MRI anatomy of the hip and pathologic MRI anatomy associated with FAI are also discussed. Several case examples are presented demonstrating the diagnosis and treatment of FAI.