arXiv subjects

Grant M. Stevens

Publications and source records attributed to Grant M. Stevens.

3 recordsLinked to original sources

arXiv2026

Evaluation of Silicon-Based Photon-Counting CT for Coronary Stenosis Quantification with Realistic Coronary Artery Phantoms

Objective: To quantify the impact of high-resolution deep silicon photon-counting CT (dSi-PCCT) on coronary stenosis quantification in anatomically realistic calcified coronary artery phantoms using Micro-CT as ground truth. Methods: Twelve vessel sections representing four calcification geometries (Type I-IV) and three luminal iodine concentrations (10, 15, and 20 mg/mL) were scanned under static conditions using energy-integrating detector CT (EID-CT), dSi-PCCT, and Micro-CT. Images were registered to Micro-CT and segmented using an automated threshold-based pipeline. The primary analysis compared longitudinal profiles of Micro-CT-referenced percent area stenosis and segmented vessel area. A secondary analysis evaluated ellipse-derived percent area stenosis and percent vessel-area deviation at the maximum-calcification cross-section. Results: dSi-PCCT reduced whole-profile mean absolute error in Micro-CT-referenced percent area stenosis from 3.10% with EID-CT to 1.62% (p=0.027) and reduced segmented vessel-area error from 0.55 to 0.31 mm2 (p=0.001). In the secondary analysis, absolute deviations in ellipse-derived percent area stenosis ranged from 0.1% to 6.5% for dSi-PCCT and from 0.8% to 24.2% for EID-CT (p<0.001). Mean absolute differences in percent vessel-area deviation from Micro-CT were also lower with dSi-PCCT than with EID-CT (15.0% vs 26.6%, p<0.001). Conclusion: Under static, resolution-optimized conditions, dSi-PCCT improved task-based coronary stenosis quantification and vessel delineation relative to EID-CT, supporting further evaluation in dynamic phantoms and clinical CCTA.

physics.med-ph↗

arXiv2026

Liver Metastasis Detection at Reduced Radiation Dose: Diagnostic Evaluation of a Novel Organ-Level Tube Current Modulation Method

Objective: To evaluate retroOpt, a novel organ-level tube current modulation (TCM) method that minimizes effective radiation dose while preserving diagnostic image quality for liver metastasis detection. Methods: In this retrospective, IRB-approved study, 22 patients with 68 liver lesions (38 malignant, 30 benign) underwent portal venous phase contrast-enhanced chest-abdomen-pelvis CT. Using projection-domain noise emulation, five series were generated per patient: original full dose (Orig-100), uniform dose reduction to 40% (UD-40) and 60% (UD-60) of the original effective dose, and organ-level TCM at the same levels (Opt-40, Opt-60). Three abdominal radiologists independently detected and classified lesions and rated image quality (5-point Likert). Per-lesion sensitivity was compared by McNemar test. Malignant lesion-size thresholds for 50% and 90% sensitivity (x50, x90) were estimated by logistic regression. Image quality was compared by Wilcoxon signed-rank test. Results: At the 60% level, Opt-60 achieved mean malignant lesion sensitivity comparable to full-dose CT (82% vs 80%) and exceeded uniform reduction (67%). At the 40% level, Opt-40 improved mean sensitivity over UD-40 from 52% to 68%. Mean all-lesion sensitivity rose from 54% (UD-40) to 64% (Opt-40) and from 65% (UD-60) to 77% (Opt-60), remaining comparable to full dose (76%). All optimized-versus-uniform differences were significant (p <= 0.003). Opt-60 lesion-size thresholds closely matched Orig-100 (x50 3.3 vs 3.7 mm; x90 15.7 vs 16.8 mm). Median Opt-60 image quality ranged from 3 to 4.5 across readers. Conclusions: Organ-level TCM preserved liver metastasis detection at 60% of the original effective dose, outperforming uniform dose reduction. Task-specific organ-level dose optimization may enable greater CT dose reduction than uniform strategies for patients requiring repeated metastasis surveillance.

physics.med-ph↗

arXiv2026

End-to-End Differentiable Photon Counting CT

Quantitative imaging is an important feature of spectral X-ray and CT systems, especially photon-counting CT (PCCT) imaging systems, which is achieved through material decomposition (MD) using spectral measurements. In this work, we present a novel framework that makes the PCCT imaging chain end-to-end differentiable (differentiable PCCT), with which we can leverage quantitative information in the image domain to enable cross-domain learning and optimization for upstream models. Specifically, the material decomposition from maximum-likelihood estimation (MLE) was made differentiable based on the Implicit Function Theorem and inserted as a layer into the imaging chain for end-to-end optimization. This framework allows for an automatic and adaptive solution of a wide range of imaging tasks, ultimately achieving quantitative imaging through computation rather than manual intervention. The end-to-end training mechanism effectively avoids the need for direct-domain training or supervision from intermediate references as models are trained using quantitative images. We demonstrate its applicability in two representative tasks: correcting detector energy bin drift and training an object scatter correction network using cross-domain reference from quantitative material images.

physics.med-ph↗