Evaluation of Diffusion-weighted MR Imaging as a Technique for Detecting Bone Marrow Edema in Patients with Osteitis Pubis

Edema-like marrow signal intensity: a narrative review with a pictorial essay

The term edema-like marrow signal intensity (ELMSI) represents a general term describing an area of abnormal signal intensity at MRI. Its appearance includes absence of clear margins and the possibility of exceeding well-defined anatomical borders (for example, physeal scars). We can define "ELMSI with unknown cause" an entity where the characteristic MR appearance is associated with the absence of specific signs of an underlying condition. However, it is more often an important finding indicating the presence of an underlying disease, and we describe this case as "ELMSI with known cause." It presents a dynamic behavior and its evolution can largely vary. It initially corresponds to an acute inflammatory response with edema, before being variably replaced by more permanent marrow remodeling changes such as fibrosis or myxomatous connective tissue that can occur over time. It is important to study ELMSI variations over time in order to evaluate the activity state and therapeutic response of an inflammatory chronic joint disease, the resolution of a trauma, and the severity of an osteoarthritis. We propose a narrative review of the literature dealing with various subjects about this challenging topic that is imaging, temporal evolution, etiology, differential diagnoses, and possible organization, together with a pictorial essay.

Musculoskeletal trauma imaging in the era of novel molecular methods and artificial intelligence

Over the past decade rapid advancements in molecular imaging (MI) and artificial intelligence (AI) have revolutionized traditional musculoskeletal radiology. Molecular imaging refers to the ability of various methods to in vivo characterize and quantify biological processes, at a molecular level. The extracted information provides the tools to understand the pathophysiology of diseases and thus to early detect, to accurately evaluate the extend and to apply and evaluate targeted treatments. At present, molecular imaging mainly involves CT, MRI, radionuclide, US, and optical imaging and has been reported in many clinical and preclinical studies. Although originally MI techniques targeted at central nervous system disorders, later on their value on musculoskeletal disorders was also studied in depth. Meaningful exploitation of the large volume of imaging data generated by molecular and conventional imaging techniques, requires state-of-the-art computational methods that enable rapid handling of large volumes of information. AI allows end-to-end training of computer algorithms to perform tasks encountered in everyday clinical practice including diagnosis, disease severity classification and image optimization. Notably, the development of deep learning algorithms has offered novel methods that enable intelligent processing of large imaging datasets in an attempt to automate decision-making in a wide variety of settings related to musculoskeletal trauma. Current applications of AI include the diagnosis of bone and soft tissue injuries, monitoring of the healing process and prediction of injuries in the professional sports setting. This review presents the current applications of novel MI techniques and methods and the emerging role of AI regarding the diagnosis and evaluation of musculoskeletal trauma.

Evaluation of Knees in Asymptomatic Amateur Ice Hockey Players Using 3.0-T Magnetic Resonance Imaging: A Case-Control Study

Background:

Research on the changes to knee structures in asymptomatic amateur ice hockey players (AAIHPs) has been limited. We aimed to assess the performance of the knees in AAIHPs using 3.0-T magnetic resonance imaging (MRI).

Methods:

A total of 71 asymptomatic knees (32 AAIHPs and 39 age- and sex-matched controls) were imaged using a 3.0-T MRI scanner at the Affiliated Zhongshan Hospital of Dalian University in April 2017. Two experienced musculoskeletal radiologists were blinded to assess all MRI findings, including bursae around the knee, bone marrow edema (BME), meniscal signal changes, and articular cartilage and ligament damage. Any disagreements were resolved by a third professor of musculoskeletal radiology. Categorical variables were compared using the Chi-square test and continuous variables using the Student's t-test or Mann-Whitney U-test.

Results:

The most common finding was fluid-filled bursae surrounding the knee. In the AAIHP group, which totaled 32 knees and 416 bursae, 155 (37%) fluid-filled bursae were present. In the control group, there were a total of 39 knees and 507 bursae, and 91 (18%) fluid-filled bursae were present. There was a significant difference in the number of fluid-filled bursae between the two groups (P < 0.05). However, in AAIHPs, the prevalence of meniscal signal changes (16 knees, 50%) was higher than in the control group (2 knees, 5%; P < 0.001). Importantly, 15 of the 19 were grade II signals. Other changes were only found in AAIHPs. Articular cartilage lesions were detected in 47% of their knees, predominantly at the patellofemoral joint, and BME was found in 34% of their knees.

Conclusion:

The MRI findings of knees in AAIHPs mainly manifested as self-protection reaction, and proper ice hockey exercise could be advocated.