Impact of Patient Size on Image Quality in Clinical PET with a Convergent Penalized-Likelihood Image Reconstruction Algorithm

2017 ◽  
Author(s):  
Sangtae Ahn ◽  
Kristen A. Wangerin ◽  
Scott D. Wollenweber ◽  
Steven G. Ross ◽  
Charles W. Stearns ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Penalized Likelihood ◽  
Reconstruction Algorithm ◽  
Image Reconstruction Algorithm ◽  
Likelihood Image ◽  
Patient Size

scholarly journals Improved image reconstruction of 89Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Julian Kirchner ◽  
Joseph A. O’Donoghue ◽  
Anton S. Becker ◽  
Gary A. Ulaner
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Penalized Likelihood ◽  
Reconstruction Algorithm ◽  
Normal Liver ◽  
Whole Body ◽  
List Mode ◽  
Improve Image Quality ◽  
Administered Activity

Abstract Purpose The aim of this study was to evaluate the use of a Bayesian penalized likelihood reconstruction algorithm (Q.Clear) for 89Zr-immunoPET image reconstruction and its potential to improve image quality and reduce the administered activity of 89Zr-immunoPET tracers. Methods Eight 89Zr-immunoPET whole-body PET/CT scans from three 89Zr-immunoPET clinical trials were selected for analysis. On average, patients were imaged 6.3 days (range 5.0–8.0 days) after administration of 69 MBq (range 65–76 MBq) of [89Zr]Zr-DFO-daratumumab, [89Zr]Zr-DFO-pertuzumab, or [89Zr]Zr-DFO-trastuzumab. List-mode PET data was retrospectively reconstructed using Q.Clear with incremental β-values from 150 to 7200, as well as standard ordered-subset expectation maximization (OSEM) reconstruction (2-iterations, 16-subsets, a 6.4-mm Gaussian transaxial filter, “heavy” z-axis filtering and all manufacturers’ corrections active). Reduced activities were simulated by discarding 50% and 75% of original counts in each list mode stream. All reconstructed PET images were scored for image quality and lesion detectability using a 5-point scale. SUVmax for normal liver and sites of disease and liver signal-to-noise ratio were measured. Results Q.Clear reconstructions with β = 3600 provided the highest scores for image quality. Images reconstructed with β-values of 3600 or 5200 using only 50% or 25% of the original counts provided comparable or better image quality scores than standard OSEM reconstruction images using 100% of counts. Conclusion The Bayesian penalized likelihood reconstruction algorithm Q.Clear improved the quality of 89Zr-immunoPET images. This could be used in future studies to improve image quality and/or decrease the administered activity of 89Zr-immunoPET tracers.

scholarly journals Deep learning versus iterative image reconstruction algorithm for head CT in trauma

2022 ◽  
Author(s):  
Zlatan Alagic ◽  
Jacqueline Diaz Cardenas ◽  
Kolbeinn Halldorsson ◽  
Vitali Grozman ◽  
Stig Wallgren ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Reconstruction Algorithm ◽  
Weighted Kappa ◽  
Trauma Patients ◽  
Reconstruction Algorithms ◽  
Head Ct ◽  
Image Reconstruction Algorithm ◽  
Experienced Reader

Abstract Purpose To compare the image quality between a deep learning–based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. Methods Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. Results DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. Conclusion The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

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