arXiv subjects

Maria Jose Medrano

Publications and source records attributed to Maria Jose Medrano.

4 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↗

arXiv2025

Scout-Dose-TCM: Direct and Prospective Scout-Based Estimation of Personalized Organ Doses from Tube Current Modulated CT Exams

This study proposes Scout-Dose-TCM for direct, prospective estimation of organ-level doses under tube current modulation (TCM) and compares its performance to two established methods. We analyzed contrast-enhanced chest-abdomen-pelvis CT scans from 130 adults (120 kVp, TCM). Reference doses for six organs (lungs, kidneys, liver, pancreas, bladder, spleen) were calculated using MC-GPU and TotalSegmentator. Based on these, we trained Scout-Dose-TCM, a deep learning model that predicts organ doses corresponding to discrete cosine transform (DCT) basis functions, enabling real-time estimates for any TCM profile. The model combines a feature learning module that extracts contextual information from lateral and frontal scouts and scan range with a dose learning module that output DCT-based dose estimates. A customized loss function incorporated the DCT formulation during training. For comparison, we implemented size-specific dose estimation per AAPM TG 204 (Global CTDIvol) and its organ-level TCM-adapted version (Organ CTDIvol). A 5-fold cross-validation assessed generalizability by comparing mean absolute percentage dose errors and r-squared correlations with benchmark doses. Average absolute percentage errors were 13% (Global CTDIvol), 9% (Organ CTDIvol), and 7% (Scout-Dose-TCM), with bladder showing the largest discrepancies (15%, 13%, and 9%). Statistical tests confirmed Scout-Dose-TCM significantly reduced errors vs. Global CTDIvol across most organs and improved over Organ CTDIvol for the liver, bladder, and pancreas. It also achieved higher r-squared values, indicating stronger agreement with Monte Carlo benchmarks. Scout-Dose-TCM outperformed Global CTDIvol and was comparable to or better than Organ CTDIvol, without requiring organ segmentations at inference, demonstrating its promise as a tool for prospective organ-level dose estimation in CT.

physics.med-ph↗

arXiv2025

Derivation of Tissue Properties from Basis-Vector Model Weights for Dual-Energy CT-Based Monte Carlo Proton Beam Dose Calculations

We propose a novel method, basis vector model material indexing (BVM-MI), for predicting atomic composition and mass density from two independent basis vector model weights derived from dual-energy CT (DECT) for Monte Carlo (MC) dose planning. BVM-MI employs multiple linear regression on BVM weights and their quotient to predict elemental composition and mass density for 70 representative tissues. Predicted values were imported into the TOPAS MC code to simulate proton dose deposition to a uniform cylinder phantom composed of each tissue type. The performance of BVM-MI was compared to the conventional Hounsfield Unit material indexing method (HU-MI), which estimates elemental composition and density based on CT numbers (HU). Evaluation metrics included absolute errors in predicted elemental compositions and relative percent errors in calculated mass density and mean excitation energy. Dose distributions were assessed by quantifying absolute error in the depth of 80% maximum scored dose (R80) and relative percent errors in stopping power (SP) between MC simulations using HU-MI, BVM-MI, and benchmark compositions. Lateral dose profiles were analyzed at R80 and Bragg Peak (RBP) depths for three tissues showing the largest discrepancies in R80 depth. BVM-MI outperformed HU-MI in elemental composition predictions, with mean RMSEs of 1.30% (soft tissue) and 0.1% (bony tissue), compared to 4.20% and 1.9% for HU-MI. R80 depth RMSEs were 0.2 mm (soft) and 0.1 mm (bony) for BVM-MI, vs. 1.8 mm and 0.7 mm for HU-MI. Lateral dose profile analysis showed overall smaller dose errors for BVM-MI across core, halo, and proximal aura regions. In conclusion, fully utilizing the two-parameter BVM space for material indexing led to significantly improved TOPAS MC dose calculations over the conventional HU-MI method, demonstrating BVM-MI's potential to enhance proton therapy planning.

physics.med-ph↗