Desmoid tumors are rare, locally invasive soft-tissue tumors with unpredictable clinical behavior. Imaging plays a crucial role in their diagnosis, measurement of disease burden, and assessment of treatment response. However, desmoid tumors’ unique imaging features present challenges to conventional imaging metrics. The heterogeneous nature of these tumors, with a variable composition (fibrous, myxoid, or cellular), complicates accurate delineation of tumor boundaries and volumetric assessment. Furthermore, desmoid tumors can demonstrate prolonged stability or spontaneous regression, and biologic quiescence is often manifested by collagenization rather than bulk size reduction, making traditional size-based response criteria, such as Response Evaluation Criteria in Solid Tumors (RECIST), suboptimal. To overcome these limitations, advanced imaging techniques offer promising opportunities. Functional and parametric imaging methods, such as diffusion-weighted MRI, dynamic contrast-enhanced MRI, and T2 relaxometry, can provide insights into tumor cellularity and maturation. Radiomics and artificial intelligence approaches may enhance quantitative analysis by extracting and correlating complex imaging features with biological behavior. Moreover, imaging biomarkers could facilitate earlier detection of treatment efficacy or resistance, enabling tailored therapy. By integrating advanced imaging into clinical practice, it may be possible to refine the evaluation of disease burden and treatment response, ultimately improving the management and outcomes of patients with desmoid tumors.

Background: Knee MRI is one of the most performed MR exams in outpatient settings and has a crucial role in detecting pathology. To increase access to advanced imaging and provide valuable diagnostic information to the clinician, reducing imaging acquisition time while preserving good image qualities is crucial. Deep learning-based reconstruction (DLR) algorithm (SubtleMR™) may be an alternative to conventional MRI with reduced scanning time while preserving image quality and diagnostic value. To compare image quality and diagnostic performance among conventional sequences and an automated accelerated deep learning-based reconstruction (DLR) knee algorithm method in patients with knee pain. Material and Methods: A total of 50 consecutive patients (26 men, mean age of 43.5 ± 10 years) with knee pain who underwent conventional (acquisition time: 13 min and 27 s) and accelerated DLR (acquisition time: 6 min and 45 s) knee MRI between November 2020 and December 2020 on a 1.5 T MRI system were prospectively enrolled. Two independent musculoskeletal radiologists compared the image quality artifacts and pathology using a 3-point Likert scale (1–3). Data were compared using paired Wilcoxon signed rank tests. Results: Forty-three patients with 86 knee MRI examinations were included. A comparison of conventional sequences and accelerated DLR by both readers revealed that there were no significant differences in overall diagnosis, image quality, and artifacts. Accelerated DLR knee MRI is comparable with conventional acquisitions, with an average time difference of 6 min and 42 s per patient (49.81%). Conclusions: Accelerated DLR knee MRI substantially reduces acquisition time (~49.81%) while maintaining equivalent diagnosis and image quality.