Takuro Shiiba

OBJECTIVES: To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD). MATERIALS AND METHODS: This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups. RESULTS: A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002). CONCLUSION: Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD. CLINICAL RELEVANCE STATEMENT: The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD. KEY POINTS: Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.

In this study, we compared the repeatability and reproducibility of radiomic features obtained from positron emission tomography (PET) images according to the reconstruction algorithm used-advanced reconstruction algorithms, such as HYPER iterative (IT), HYPER deep learning reconstruction (DLR), and HYPER deep progressive reconstruction (DPR), or traditional Ordered Subset Expectation Maximization (OSEM)-to understand the potential variations and implications of using advanced reconstruction techniques in PET-based radiomics. We used a heterogeneous phantom with acrylic spherical beads (4- or 8-mm diameter) filled with 18F. PET images were acquired and reconstructed using OSEM, IT, DLR, and DPR. Original and wavelet radiomic features were calculated using SlicerRadiomics. Radiomic feature repeatability was assessed using the Coefficient of Variance (COV) and intraclass correlation coefficient (ICC), and inter-acquisition time reproducibility was assessed using the concordance correlation coefficient (CCC). For the 4- and 8-mm diameter beads phantom, the proportion of radiomic features with a COV < 10% was equivocal or higher for the advanced reconstruction algorithm than for OSEM. ICC indicated that advanced methods generally outperformed OSEM in repeatability, except for the original features of the 8-mm beads phantom. In the inter-acquisition time reproducibility analysis, the combinations of 3 and 5 min exhibited the highest reproducibility in both phantoms, with IT and DPR showing the highest proportion of radiomic features with CCC > 0.8. Advanced reconstruction methods provided enhanced stability of radiomic features compared with OSEM, suggesting their potential for optimal image reconstruction in PET-based radiomics, offering potential benefits in clinical diagnostics and prognostics.

Abstract Background SwiftScan single-photon emission computed tomography (SPECT) is a recently released scanning technique with data acquired when the detector is stationary and when it moves from one view to the next. The influence of scan time for using SwiftScan on quantitative bone SPECT remains unclear. This study aimed to clarify the effect of the scan time for SwiftScan SPECT on the image quality and quantification of bone SPECT compared to step and shoot mode (SSM) using ^99mTc-filled anthropomorphic phantom (SIM² bone phantom). Materials and methods Phantom SPECT/computed tomography (CT) images were acquired using Discovery NM/CT 860 (GE Healthcare) with a low-energy high-resolution sensitivity collimator. We used the fixed parameters (subsets 10 and iterations 5) for reconstruction. The coefficient of variation (CV), contrast-to-noise ratio (CNR), full width at half maximum (FWHM), and quantitative value of SwiftScan SPECT and SSM were compared at various acquisition times (5, 7, 17, and 32 min). Results In the short-time scan (&lt; 7 min), the CV and CNR of SwiftScan SPECT were better than those of SSM, whereas in the longtime scan (&gt; 17 min), the CV and CNR of SwiftScan SPECT were similar to those of SSM. The FWHMs for SwiftScan SPECT (13.6–14.8 mm) and SSM (13.5–14.4 mm) were similar. The mean absolute errors of quantitative values at 5, 7, 17, and 32 min were 38.8, 38.4, 48.8, and 48.1, respectively, for SwiftScan SPECT and 41.8, 40.8%, 47.2, and 49.8, respectively, for SSM. Conclusions SwiftScan on quantitative bone SPECT provides improved image quality in the short-time scan with quantification similar to or better than SSM. Therefore, in clinical settings, using SwiftScan SPECT instead of the SSM scan protocol in the short-time scan might provide higher-quality diagnostic images than SSM. Our results could provide vital information on the use of SwiftScan SPECT.