Design and implementation of continuous bed motion (CBM) in Xtrim preclinical PET scanner for whole-body Imaging: MC simulation and experimental measurements.

PURPOSE: Preclinical PET scanners often have limited axial field-of-view for whole-body (WB) scanning of the small-animal. Step-and-shoot(S&S) acquisition mode requires multiple bed positions (BPs) to cover the scan length. Alternatively, in Continuous Bed Motion(CBM) mode, data acquisition is performed while the bed is continuously moving. In this study, to reduce acquisition time and enhance image quality, the CBM acquisition protocol was optimized and implemented on the Xtrim-PET preclinical scanner for WB imaging. METHODS: The over-scan percentage(OS%) in CBM mode was optimized by Monte Carlo simulation. Bed movement speed was optimized considering ranges from 0.1 to 2.0 mm s-1, and absolute system sensitivities with the optimal OS% were calculated. The performance of the scanner in CBM mode was measured, and compared with S&S mode based on the NEMA-NU4 standard. RESULTS: The optimal trade-off between absolute sensitivity and uniformity of sensitivity profile was achieved at OS-50 %. In comparison to S&S mode with maximum ring differences (MRD) of 9 and 23, the calculated equivalent speeds in CBM(OS-50 %) mode were 0.3 and 0.14 mm s-1, respectively. In terms of data acquisition with equal sensitivity in both CBM(OS-50 %) and S&S(MRD-9) modes, the total scan time in CBM mode decreased by 25.9 %, 47.7 %, 54.7 %, and 58.2 % for scan lengths of 1 to 4 BPs, respectively. CONCLUSION: The CBM mode enhances WB PET scans for small-animals, offering rapid data acquisition, high system sensitivity, and uniform axial sensitivity, leading to improved image quality. Its efficiency and customizable scan length and bed speed make it a superior alternative.

Synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET radiomic features in non-small cell lung cancer: Phantom and clinical studies.

OBJECTIVES: This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET image radiomic features in non-small cell lung cancer (NSCLC) patients, and investigating the robustness of features performance in differentiating NSCLC histopathology subtypes. METHODS: An in-house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full-width at half-maximum of post-reconstruction smoothing filter and acquisition parameters, including injected activity and test-retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi-automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions. RESULTS: Overall, 29% of radiomic features showed a COV ≤5% against motion. Forty-five percent and 76% of the features showed a COV ≤ 5% against the test-retest with and without motion in large lesions, respectively. Thirty-three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p-value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting). CONCLUSIONS: Motion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non-reproducibility.