Optimization of Q.Clear reconstruction for dynamic 18F PET imaging.

BACKGROUND: Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a ß penalization factor. This study aimed to determine the optimal ß-factor for accurate quantitation of dynamic PET scans. METHODS: A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different ß-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different ß-factors on the pharmacokinetic analysis of clinical PET brain data. RESULTS: In general, RC and CVRC decreased with increasing ß-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low ß-factors resulted in high variability and an overestimation of measured activity. Increasing the ß-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased ß-factor; a change in ß-factor from 300 to 1000 resulted in a 25.5% increase in the Ki. CONCLUSION: In a complex dynamic dataset with variable acquisition times, the optimal ß-factor provides a balance between accuracy and precision. Based on our results, we suggest a ß-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher ß-factors. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies.

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Ultra-low dose chest computed tomography: Effect of iterative reconstruction levels on image quality.

PURPOSE: To optimize image quality and radiation dose of chest CT with respect to various iterative reconstruction levels, detector collimations and body sizes. METHOD: A Kyoto Kagaku Lungman with and without extensions was scanned using fixed ultra-low doses of 0.25, 0.49 and 0.74 mGy CTDIvol, and collimations of 40 and 80 mm. Images were reconstructed with the lung kernel, filtered back projection (FBP) and different ASIR-V levels (10-100%). Contrast-to-noise ratios (CNR) were calculated for 12 mm simulated lesions of different densities in the lung. Image noise, signal-to-noise ratios (SNR), variations in Hounsfield units (HU), noise power spectrum (NPS) and noise texture deviations (NTD) were evaluated for all reconstructions. NTD was calculated as percentage of pixels outside 3 standard deviations to evaluate IR-specific artefacts. RESULTS: Compared to the FBP, image noise reduced (5-55%) with ASIR-V levels irrespective of dose or collimation. SNR correlated positively (r ≥ 0.925, p ≤ 0.001) with ASIR-V levels at all doses, collimations, and phantom sizes. ASIR-V enhanced the CNR of the lesion with the lowest contrast from 12.7-42.1 (0-100% ASIR-V) at 0.74 mGy with 40 mm collimation. As expected, higher SNR and CNR were measured in the smaller phantom than the bigger phantom. Uniform HU were observed between FBP and ASIR-V levels at all doses, collimations, and phantom sizes. NPS curves left-shifted towards lower frequencies at increasing levels of ASIR-V irrespective of collimation. A positive correlation (r ≥ 0.946, p ≥ 0.001) was observed between NTD and ASIR-V levels. NTD of the FBP was not significantly (p ≤ 0.087) different from NTD of ASIR-V ≤ 20%. The data from the NPS and NTD indicates a blotchier and coarser noise texture at higher levels of ASIR-V, especially at 100% ASIR-V. CONCLUSION: In comparison with the FBP technique, ASIR-V enhanced quantitative image quality parameters at all ultra-low doses tested. Moreover, the use of ASIR-V showed consistency with body size and collimation. Hence, ASIR-V may be useful for improving image quality of chest CT at ultra-low doses.