Background: Lumbar disk herniation is a common cause of low back pain and radiculopathy, significantly impacting patients’ life quality and functional capacity. Magnetic Resonance Imaging (MRI) remains the gold standard for its assessment due to its superior soft tissue contrast and multiplanar imaging capabilities. However, recent advances in spectral computed tomography (CT), particularly dual-energy CT (DECT), have introduced new diagnostic opportunities, offering improved soft tissue characterization. Objective: To evaluate the diagnostic performance of DECT in detecting and grading lumbar disk herniations using dedicated color-coded fat maps. Materials and Methods: A total of 205 intervertebral levels from 41 consecutive patients with lumbar symptoms were prospectively analyzed. All patients underwent both DECT and MRI within 3 days. Three radiologists with varying years of experience independently assessed DECT images using color-coded reconstructions. A five-point grading score was attributed to each lumbar level: 1 = normal disk, 2 = bulging/protrusion, 3 = focal herniation, 4 = extruded herniation, and 5 = migrated fragment. The statistical analysis included Pearson’s correlation for score consistency, Cohen’s Kappa for interobserver agreement, generalized estimating equations for a cluster-robust analysis, and an ROC curve analysis. The DECT diagnostic accuracy was assessed in a dichotomized model (grades 1–2 = no herniation; 3–5 = herniation), using MRI as reference. Results: A strong correlation was observed between DECT and MRI scores across all readers (mean Pearson’s r = 0.826, p < 0.001). The average exact agreement between DECT and MRI was 79.4%, with the highest concordance at L1–L2 (86.7%) and L5–S1 (80.4%). The interobserver agreement was substantial (mean Cohen’s κ = 0.765), with a near-perfect agreement between the two most experienced readers (κ = 0.822). The intraclass correlation coefficient was 0.906 (95% CI: 0.893–0.918). The ROC analysis showed excellent performance (AUC range: 0.953–0.986). In the dichotomous model, DECT demonstrated a markedly higher sensitivity than conventional CT (95.1% vs. 57.2%), with a comparable specificity (DECT: 99.0%; CT: 96.5%) and improved overall accuracy (98.4% vs. 90.0%). Subgroup analyses by age and disk location revealed no statistically significant differences. Conclusions: The use of DECT dedicated color-coded fat map reconstructions showed high diagnostic performance in the assessment of lumbar disk herniations compared to MRI. These findings support the development of dedicated post-processing tools, facilitating the broader clinical adoption of spectral CT, especially in cases where MRI is contraindicated or less accessible.

Background/Objectives: Photon-counting CT (PCCT) spectral fat maps allow fat quantification on routine scans. We quantified the fat fraction (FF) in the liver, spleen, pancreas, and psoas, assessed determinants and regional heterogeneity, and compared spectral FF with an attenuation-derived PDFF estimate (HU-PDFF). Methods: In total, 270 consecutive patients underwent abdominal PCCT. On the unenhanced acquisition, 14 ROIs per patient were placed, recording spectral FF and 70 keV attenuation; no external reference standard was available. Agreement was assessed via ICC and Bland–Altman analysis. Results: Mean FF was 4.2% (liver), 12.3% (spleen), 23.1% (pancreas), and 15.2% (psoas); 34.8% had hepatic steatosis (FF ≥ 5%). In exploratory analyses, BMI was the main independent correlate of hepatic and pancreatic FF, and psoas FF was associated with BMI, age, and male sex; splenic FF was unrelated to BMI and age. Hepatic FF showed no systematic supra-/infra-portal difference (ICC 0.97); the pancreatic tail was leaner than the body and head. Spectral FF and attenuation correlated strongly in the liver, pancreas, and psoas (r = −0.82 to −0.92), but spectral FF was 4.6 points lower than HU-PDFF in the liver and 2.7–3.4 points higher in the pancreas and psoas; liver attenuation ≤ 58.7 HU corresponded to FF ≥ 5% (AUC 0.93; cross-validated sensitivity and specificity 86%). Conclusions: PCCT fat maps are feasible for multi-organ fat measurement. Spectral FF and HU-PDFF are highly correlated but not interchangeable because of organ-specific offsets, whose source and absolute accuracy must be established against MRI-PDFF.

Background/Objectives: Magnetic resonance arthrography is the reference standard for evaluating glenoid labral lesions. Deep learning (DL) reconstruction algorithms may accelerate 3D acquisitions while maintaining image quality. This study assesses the diagnostic accuracy of DL-based isotropic 3D MR imaging for detecting glenoid labral lesions. Methods: This prospective study included 128 consecutive patients (79 men, 49 women; mean age 38.4 years) undergoing shoulder MR arthrography between June 2023 and April 2025. DL-based 3D sequences (acquisition time: 3:26) were compared with conventional multiplanar TSE and PD-FS sequences (acquisition time: 24–28 min). Two independent radiologists assessed glenoid labral lesions, bone marrow edema, and rotator cuff abnormalities using a four-point Likert scale. Sensitivity, specificity, and interobserver agreement were calculated. Results: DL-based 3D sequences demonstrated 94.7–95.1% sensitivity and 100% specificity for glenoid labral lesions, with excellent interobserver agreement (κ = 0.812). The area under the ROC curve was 0.894. Combined 3D protocols (T1 + PD-FS) showed superior accuracy (97.8%) compared to single sequences (90.5%, p = 0.012). For bone marrow edema, sensitivity was 82.9% with 100% specificity. Rotator cuff evaluation achieved 75% sensitivity with 100% specificity. Conclusions: DL-based isotropic 3D sequences provide high diagnostic accuracy for glenoid labral pathology while reducing scan time by 75%. Combined T1 and PD-FS protocols optimize performance. These findings support selective implementation of DL-accelerated 3D protocols in shoulder MR arthrography, particularly for labral assessment, while acknowledging that conventional protocols may remain preferable in specific clinical scenarios.