Whole-body MRI (WB-MRI) has evolved over the past 2 decades as a noninvasive imaging technique for detecting distant metastases in prostate cancer. Since the introduction of diffusion-weighted imaging with background body signal suppression by Takahara et al. in 2004, its clinical use has expanded rapidly, particularly in the detection of bone metastases. WB-MRI offers whole-body coverage without radiation exposure and can be completed within approximately 30 minutes, making it suitable for repeated examinations. Consequently, it is now applied not only for metastasis detection but also for treatment response evaluation. Diffusion-weighted imaging further enables semi-quantitative assessment of tumor burden by measuring total tumor diffusion volume. Nevertheless, manual processing and interinstitutional standardization remain limitations that hinder widespread clinical adoption. Recent advances in deep learning and quantitative imaging are expected to overcome these issues through automated lesion extraction and volumetric analysis. Moreover, comparative studies have shown that WB-MRI and prostate-specific membrane antigen positron emission tomography can serve as complementary modalities. The integration of both techniques will enhance diagnostic accuracy, facilitate individualized treatment strategies, and contribute to establishing WB-MRI as a next-generation imaging standard in prostate cancer management.

Subclinical inflammation (SI) is clinically relevant in psoriasis (PsO) and psoriatic arthritis (PsA), but quantitative imaging biomarkers are lacking. We evaluated the feasibility of ultrashort echo time (UTE) bicomponent MRI for quantifying Achilles enthesis SI. Conventional MRI showed no group differences, whereas short T2* (T2*_s) values were significantly elevated in PsO/PsA compared with healthy volunteers. These findings support the feasibility of UTE bicomponent analysis, as a promising tool for evaluating SI and warrant further investigation.

Recent advances in musculoskeletal (MSK) radiology have markedly improved diagnostic accuracy through innovations in MRI, CT, and artificial intelligence (AI). This review summarizes 7 key domains shaping current MSK imaging: (1) CT-like contrast MRI techniques for bone visualization, (2) quantitative MRI approaches, (3) AI applications in image reconstruction and diagnostic support, (4) MR spectroscopy (MRS) for metabolic assessment, (5) whole-body MRI for systemic disease evaluation, (6) positron emission tomography (PET) for metabolic and inflammatory imaging, and (7) advanced CT techniques such as weight-bearing CT. Zero echo time and ultrashort echo time MRI sequences enable the visualization and quantitative assessment of short-T2 tissues such as cortical bone, tendons, and fibrocartilage. Deep learning–based image reconstruction improves SNR and shortens scan time, enhancing image quality and diagnostic confidence. In parallel, AI-driven diagnostic support systems, including convolutional neural networks for lesion detection and natural language processing for report generation, are transforming workflow efficiency and consistency. MRS offers metabolic insights into muscle disorders such as sarcopenia, and whole-body-MRI provides comprehensive, radiation-free evaluation of tumor burden and inflammatory joint or enthesis involvement, making it valuable in oncology and rheumatic diseases. PET complements MRI by identifying metabolically active or inflammatory lesions. CT-based innovations further contribute to evaluating joint biomechanics with high spatial resolution. Together, these technological developments are enabling earlier disease detection, more precise diagnosis, and improved treatment monitoring, representing a paradigm shift in MSK imaging and clinical practice.

Purpose: To evaluate the clinical utility of macromolecular proton fraction mapping for early osteoarthritis detection and compare its effectiveness with T₂* mapping in evaluating cartilage degeneration.

Methods: Eleven controls and 29 patients with osteoarthritis underwent 3.0T magnetic resonance imaging. Patients were classified based on the Kellgren–Lawrence grading system into mild osteoarthritis (KL 1–2, n = 9) and severe (KL 3–4, n = 20) osteoarthritis. Macromolecular proton fraction maps were generated from proton density-, T₁-, and magnetization transfer-weighted images using a single-point synthetic reference algorithm. T₂* maps were derived from multi-echo sequences. Macromolecular proton fractions and T₂* values were measured in 6 subregions of the femoral cartilage. Statistical analyses were conducted to compare values among the control, mild osteoarthritis, and severe osteoarthritis groups.

Results: Macromolecular proton fraction values decreased with increasing osteoarthritis severity. Compared to controls, both the mild and severe osteoarthritis groups exhibited significantly lower macromolecular proton fraction in the medial anterior and medial posterior regions. Additionally, the severe osteoarthritis group demonstrated significantly lower macromolecular proton fraction values in the medial central and lateral posterior regions compared to controls. In contrast, T₂* values generally increased with osteoarthritis severity. The mild and severe osteoarthritis groups had significantly elevated T₂* values in the medial anterior region compared to controls. Furthermore, the mild osteoarthritis group exhibited increased T₂* in the medial central region, whereas the severe osteoarthritis group had significantly higher T₂* in the medial anterior, medial posterior, and lateral posterior regions compared to controls.

Conclusion: Macromolecular proton fraction mapping demonstrated sensitivity to early-stage cartilage degeneration in osteoarthritis, suggesting its potential as a biomarker for early osteoarthritis detection. The combination of macromolecular proton fraction and T₂* mapping could enhance the biochemical assessment of cartilage integrity, providing valuable insights for early diagnosis and monitoring treatment effects in osteoarthritis.

Purpose: The primary objective of this study was to determine the relationship between Wingate test, athletic performance, and MRI parameters in athletes. Additionally, we examined whether there were significant differences in these parameters between athletes and non-athletes during dorsiflexion exercises.

Methods: Twenty-two male athletes and 9 non-athletes performed dorsiflexion exercises with a 4-kg load. MRI scans, including T2* mapping and diffusion tensor imaging, were conducted pre-exercise, immediately after exercise, and 30 minutes post-exercise. Quantitative parameters, including T2* values, fractional anisotropy, mean diffusivity, and eigenvalues (λ2, λ3), were analyzed. Wingate test results and athletics scoring based on the 2022 World Athletics Scoring Tables were used to evaluate anaerobic power and sprint performance.

Results: MRI parameters, particularly T2* changes and λ3, showed significant correlations with Wingate test results and athletic performance. Pre-exercise λ3, reflecting muscle fiber orientation and thickness, emerged as a key predictor of athletic performance alongside T2* changes and Wingate power. The integration of MRI-derived metrics with Wingate test results improved the prediction of athletic scores compared to Wingate power alone. Although differences between athletes and non-athletes in T2* and λ2 were observed, these findings serve as supplementary evidence supporting the role of MRI in identifying muscle characteristics critical for athletic performance.

Conclusion: MRI-derived parameters combined with performance tests can provide valuable insights into muscle recovery, structure, and athletic performance, with potential for predicting athlete scores and optimizing training strategies.

Purpose: This study aimed to compare the differences in the imaging findings for dedifferentiated liposarcoma (DDLS) and myxoid liposarcoma (MLS).

Methods: The study included 30 patients with histopathologically confirmed DDLS and 13 patients with MLS. All DDLSs and MLSs were diagnosed immunohistochemically using MDM2 and DDIT3 staining, respectively. Conventional MRI, CT, and ¹⁸F-fluorodeoxyglucose-positron emission tomography/CT findings were retrospectively evaluated and compared between the 2 pathologies.

Results: The median age of patients with DDLS was higher than that of patients with MLS (74 vs. 46 years, P < 0.01). In 10 DDLSs and 7 MLSs with fatty areas, the well-differentiated liposarcoma-like fatty areas were more common in DDLS than in MLS (70% vs. 14%), whereas septal/linear fatty areas were less common in DDLS than in MLS (30% vs. 86%, P < 0.05). The T2-hyperintense area of non-fatty area was less common in DDLS than in MLS (50% vs. 92%, P < 0.05), and the tumor-to-muscle signal intensity ratio of non-fatty areas on T2-weighted images was lower in DDLS than in MLS (3.18 vs. 5.92, P < 0.01). Apparent diffusion coefficient value was lower in DDLS than in MLS (1.29 vs. 2.10 × 10⁻³mm²/sec, P < 0.01). Unenhanced CT attenuation of non-fatty area was greater in DDLS than in MLS (33 vs. 19 Hounsfield unit, P < 0.01).

Conclusion: MRI features are valuable in differentiating MLS from DDLS. Younger age, septal/linear fatty areas, and high signal intensity of non-fatty areas on T2-weighted images were useful for diagnosing MLS.

Osteoarthritis (OA) is one of the most prevalent disorders in today’s society, resulting in significant socio-economic costs and morbidity. MRI is widely used as a non-invasive imaging tool for OA of the knee. However, conventional knee MRI has limitations to detect subtle early cartilage degeneration before morphological changes are visually apparent. Novel MRI pulse sequences for cartilage assessment have recently received increased attention due to newly developed compositional MRI techniques, including: T2 mapping, T1rho mapping, delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), sodium MRI, diffusion-weighted imaging (DWI)/ diffusion tensor imaging (DTI), ultrashort TE (uTE), and glycosaminoglycan specific chemical exchange saturation transfer (gagCEST) imaging. In this article, we will first review these quantitative assessments. Then, we will discuss the variations of quantitative values of knee articular cartilage with cartilage layer (depth)- and angle (regional)-dependent approaches. Multiple MRI sequence techniques can discern qualitative differences in knee cartilage. Normal articular hyaline cartilage has a zonal variation in T2 relaxation times with increasing T2 values from the subchondral bone to the articular surface. T1rho values were also higher in the superficial layer than in the deep layer in most locations in the medial and lateral femoral condyles, including the weight-bearing portion. Magic angle effect on T2 mapping is clearly observed in the both medial and lateral femoral condyles, especially within the deep layers. One of the limitations for clinical use of these compositional assessments is a long scan time. Recent new approaches with compressed sensing (CS) and MR fingerprinting (MRF) have potential to provide accurate and fast quantitative cartilage assessments.

Purpose: To compare apparent diffusion coefficients (ADCs) of bone marrow on diffusion-weighted imaging (DWI) between two fat-suppression techniques, and to evaluate the association between bone-marrow ADCs and the proton density fat fraction (PDFF).

Methods: Seventy-seven patients underwent whole-body DWI with short-inversion time inversion-recovery (STIR) (DWI_STIR) and/or STIR + selective water-excitation (spectral-spatial RF [SSRF]) (DWI_STIR+SSRF). ADCs of lumbar vertebrae (L3 and L4) were compared between DWI_STIR and DWI_STIR+SSRF, and correlated with the PDFF.

Results: Lumbar ADCs obtained by DWI_STIR and DWI_STIR+SSRF were significantly correlated (L3: r = 0.90, P < 0.0001, L4: r = 0.90, P < 0.0001). Lumbar ADCs (× 10^-6 mm²/s) obtained by DWI_STIR were significantly lower than those by DWI_STIR+SSRF (L3: 479 ± 137 and 490 ± 148, P < 0.05, L4: 456 ± 114 and 471 ± 118, P < 0.005). Residual fat signals were more clearly observed on DWI_STIR than on DWI_STIR+SSRF. The ADCs of L3 obtained by DWI_STIR and DWI_STIR+SSRF exhibited significant positive correlations with the PDFF (r = 0.51, P < 0.0001, and r = 0.45, P < 0.0001, respectively), and the ADCs of L4 obtained by DWI_STIR and DWI_STIR+SSRF exhibited significantly positive correlations with the PDFF (r = 0.40, P < 0.0005, and r = 0.40, P < 0.0005, respectively).

Conclusion: Irrespective of different fat-suppression methods, lumbar ADCs were positively correlated with the PDFF, being inconsistent with previous studies. Lumbar ADCs obtained by DWI_STIR were significantly lower than those obtained by DWI_STIR+SSRF, probably due to residual fat signals on DWI_STIR. However, this difference (< 4%) did not explain the positive correlation between lumbar ADC and PDFF.

Purpose: This study aimed to evaluate whether the image quality of 1.5T magnetic resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying deep learning reconstruction (DLR).

Methods: Proton density-weighted images of the right knee of 27 healthy volunteers were obtained by 3T and 1.5T MRI scanners using similar imaging parameters (21 for high resolution image and 6 for normal resolution image). Commercially available DLR was applied to the 1.5T images to obtain 1.5T/DLR images. The 3T and 1.5T/DLR images were compared subjectively for visibility of structures, image noise, artifacts, and overall diagnostic acceptability and objectively. One-way ANOVA and Friedman tests were used for the statistical analyses.

Results: For the high resolution images, all of the anatomical structures, except for bone, were depicted significantly better on the 1.5T/DLR compared with 3T images. Image noise scored statistically lower and overall diagnostic acceptability scored higher on the 1.5T/DLR images. The contrast between lateral meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.89 ± 1.30 vs. 4.34 ± 0.87, P < 0.001), and also the contrast between medial meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.12 ± 0.93 vs. 3.87 ± 0.56, P < 0.001). Similar image quality improvement by DLR was observed for the normal resolution images.

Conclusion: The 1.5T/DLR images can achieve less noise, more precise visualization of the meniscus and ligaments, and higher overall image quality compared with the 3T images acquired using a similar protocol.