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The purpose of this study was to determine the capability of proton density with fat fraction (PD-FFQ) imaging to help assess hematopoietic ability and diagnose aplastic anemia in adults. Between January 2021 and March 2023, patients diagnosed with aplastic anemia (AA: n = 14) or myelodysplastic syndrome (MDS: n = 14) were examined by whole-body PD-FFQ imaging, and 14 of 126 age and gender matched patients who had undergone the same PD-FFQ imaging were selected as control group. All proton density fat fraction (PDFF) index evaluations were then performed by using regions of interest (ROIs). Pearson's correlation was used to determine the relationship between blood test results and each quantitative index, and ROC-based positive test and discrimination analyses to compare capability to differentiate the AA from the non-AA group. Finally, sensitivity, specificity and accuracy of all quantitative indexes were compared by means of McNemar's test. Mean PDFF, standard deviation (SD) and percentage of coefficient of variation (%CV) for vertebrae showed significant correlation with blood test results (−0.52 ≤ r ≤ −0.34, p < 0.05). Specificity (SP) and accuracy (AC) of %CV of PDFF in vertebrae were significantly higher than those of mean PDFF in vertebrae and the posterior superior iliac spine (SP: p = 0.0002, AC: p = 0.0001) and SD of PDFF in vertebrae (SP: p = 0.008, AC: p = 0.008). Moreover, AC of SD of PDFF in vertebrae was significantly higher than that of mean PDFF in vertebrae and the posterior superior iliac spine (p = 0.03). Whole-body PD-FFQ imaging is useful for hematopoietic ability assessment and diagnosis of aplastic anemia in adults. • PD-FF indexes had significantly difference between AA and non-AA groups (p < 0.05). • PD-FF indexes had significantly differences among AA, myelodysplastic syndrome or control groups (p < 0.05). • PD-FF within vertebra showed significantly higher accuracy than that in posterior superior iliac spine (p < 0.05). [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep learning-based reconstruction (dDLR) are more useful than 3D fat-saturated proton density multi-planar voxel images., Methods: Eighteen patients who underwent MRI of the shoulder joint at 3T were enrolled. The denoising effect of dDLR in 2D was evaluated using coefficient of variation (CV). Qualitative evaluation of anatomical structures, noise, and artifacts in 2D after dDLR and 3D was performed by two radiologists using a five-point Likert scale. All were analyzed statistically. Gwet's agreement coefficients were also calculated., Results: The CV of 2D after dDLR was significantly lower than that before dDLR (P < 0.05). Both radiologists rated 2D higher than 3D for all anatomical structures and noise (P < 0.05), except for artifacts. Both Gwet's agreement coefficients of anatomical structures, noise, and artifacts in 2D and 3D produced nearly perfect agreement between the two radiologists. The evaluation of 2D tended to be more reproducible than 3D., Conclusion: 2D with parallel imaging, partial Fourier technique, and dDLR was proved to be superior to 3D for depicting shoulder joint structures with lower noise.