Performance of SwiftScan planar and SPECT technology using low-energy high-resolution and sensitivity collimator compared with Siemens SPECT system

The SwiftScan step-and-shoot continuous mode improves SPECT scanning efficiency: a preliminary phantom and clinical test

PURPOSE

The aim of the study was to investigate the value of SwiftScan Step-and-Shoot Continuous (SSC) scanning mode in enhancing image quality and to explore appropriate scanning parameters for reducing scan time.

METHODS

This study was composed of a phantom study and two clinical tests. The differences in visual image quality scores, coefficient of variance (COV) of the background, image signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and recovery coefficient (RC) of the sphere were compared between SSC mode and traditional Step-and-Shoot (SS) mode in the phantom study. Various "shoot" acquisition times (5s, 10s, 15s) and "step" angles (3-degree, 6-degree, 9-degree) were evaluated and verified. In the clinical tests, bone tomography and parathyroid tomography were performed on 30 patients each. Differences in visual image quality scores, background COV, image SNR, CNR, and standardized uptake value (SUV) of lesions were compared between the two modes.

RESULTS

In the phantom study, SSC mode demonstrated higher visual scores and significantly reduced background COV (P < 0.05), and significantly increased SNR and CNR (P < 0.05) compared to SS mode. No significant alteration in RC was observed (P > 0.05). In the clinical tests, no significant differences were found between the optimal SSC scan combination (10s "shoot" and 6-degree "step")/ (10s "shoot" and 3-degree "step") and the traditional SS scan combination (15s "shoot" and 6-degree "step")/ (15s "shoot" and 3-degree "step") in visual image quality scores, background COV, image SNR, CNR, and SUV of bone and parathyroid high uptake lesions (P > 0.05).

CONCLUSION

The SwiftScan SSC mode can reduce acquisition time by 33% while maintaining similar image quality and quantification accuracy compared to SS mode. An SSC scanning protocol with a 10s "shoot" acquisition and 6-degree "step" or with a 10s "shoot" acquisition and 3-degree "step" over a 360-degree rotation, is recommended for clinical use.

Verification of the effect of acquisition time for SwiftScan on quantitative bone single-photon emission computed tomography using an anthropomorphic phantom

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 (< 7 min), the CV and CNR of SwiftScan SPECT were better than those of SSM, whereas in the longtime scan (> 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.

Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients

OBJECTIVE

We aimed to compare the deep learning-based (VSBONE BSI) and atlas-based (BONENAVI) segmentation accuracy that have been developed to measure the bone scan index based on skeletal segmentation.

METHODS

We retrospectively conducted bone scans for 383 patients with prostate cancer. These patients were divided into two groups: 208 patients were injected with ^99mTc-hydroxymethylene diphosphonate processed by VSBONE BSI, and 175 patients were injected with ^99mTc-methylene diphosphonate processed by BONENAVI. Three observers classified the skeletal segmentations as either a "Match" or "Mismatch" in the following regions: the skull, cervical vertebrae, thoracic vertebrae, lumbar vertebrae, pelvis, sacrum, humerus, rib, sternum, clavicle, scapula, and femur. Segmentation error was defined if two or more observers selected "Mismatch" in the same region. We calculated the segmentation error rate according to each administration group and evaluated the presence of hot spots suspected bone metastases in "Mismatch" regions. Multivariate logistic regression analysis was used to determine the association between segmentation error and variables like age, uptake time, total counts, extent of disease, and gamma cameras.

RESULTS

The regions of "Mismatch" were more common in the long tube bones for VSBONE BSI and in the pelvis and axial skeletons for BONENAVI. Segmentation error was observed in 49 cases (23.6%) with VSBONE BSI and 58 cases (33.1%) with BONENAVI. VSBONE BSI tended that "Mismatch" regions contained hot spots suspected of bone metastases in patients with multiple bone metastases and showed that patients with higher extent of disease (odds ratio = 8.34) were associated with segmentation error in multivariate logistic regression analysis.

CONCLUSIONS

VSBONE BSI has a potential to be higher segmentation accuracy compared with BONENAVI. However, the segmentation error in VSBONE BSI occurred dependent on bone metastases burden. We need to be careful when evaluating multiple bone metastases using VSBONE BSI.