Journal of Medical Imaging最新文献

{"title":"Automatic detection of main pancreatic duct dilation and pancreatic parenchymal atrophy based on a shape feature in abdominal contrast-enhanced CT images.","authors":"Shintaro Ambo, Ryo Hirano, Chihiro Hattori","doi":"10.1117/1.JMI.12.1.014504","DOIUrl":"10.1117/1.JMI.12.1.014504","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to develop and evaluate an algorithm for calculating a shape feature to automatically detect both main pancreatic duct dilation (MPDD) and pancreatic parenchymal atrophy (PPA) in abdominal contrast-enhanced CT (CE-CT) images.</p><p><strong>Approach: </strong>The proposed algorithm for the automatic detection of MPDD and PPA is composed of five processes: coarse pancreas segmentation, fine pancreas segmentation, main pancreatic duct (MPD) segmentation, centerline estimation, and shape feature calculation. First, the pancreas region is segmented by a deep learning convolutional neural network (CNN). Then, the MPD region is segmented inside the pancreatic region by the deep learning CNN. Next, centerline estimation is performed using Dijkstra's rooting algorithm. Finally, in shape feature calculation, the cross-sectional area ratio of the pancreatic duct to the pancreatic parenchyma (DP ratio) is calculated in all cross sections perpendicular to the identified centerline, and the 90th percentile value of the DP ratio for all cross sections (90th DP ratio) is calculated. The detection performance of the 90th DP ratio for MPDD and PPA was evaluated using 56 abdominal CE-CT images available as public data.</p><p><strong>Results: </strong>The average of the 90th DP ratio was 0.059 in 48 cases with MPDD and 0.007 in eight cases without MPDD ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) and 0.074 in 31 cases with PPA and 0.023 in 25 cases without PPA ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ).</p><p><strong>Conclusions: </strong>We have developed an algorithm for calculating an automatically measurable shape feature called the 90th DP ratio for the detection of MPDD and PPA.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014504"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate volume image reconstruction for digital breast tomosynthesis with directional-gradient and pixel sparsity regularization.","authors":"Emil Y Sidky, Xiangyi Wu, Xiaoyu Duan, Hailiang Huang, Wei Zhao, Leo Y Zhang, John Paul Phillips, Zheng Zhang, Buxin Chen, Dan Xia, Ingrid S Reiser, Xiaochuan Pan","doi":"10.1117/1.JMI.12.S1.S13013","DOIUrl":"10.1117/1.JMI.12.S1.S13013","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to develop accurate volumetric quantitative imaging of iodinated contrast agent (ICA) in contrast-enhanced digital breast tomosynthesis (DBT).</p><p><strong>Approach: </strong>The two main components of the approach are the use of a dual-energy DBT (DE-DBT) scan and the development of an optimization-based algorithm that can yield accurate images with isotropic resolution. The image reconstruction algorithm exploits sparsity in the subject's directional derivative magnitudes, and it also performs direct sparsity regularization to help confine the reconstruction to the true support of the subject. The algorithm is demonstrated with three sets of simulations in 2D and 3D, and a physical DE-DBT scan. The last of the three simulations employs an anthropomorphic phantom derived from the VICTRE project, testing quantitative tumor imaging with ICA.</p><p><strong>Results: </strong>The 2D simulations of the algorithm demonstrate accurate and stable image reconstruction. With the first 3D simulation, the proposed algorithm shows the ability to resolve overlapping objects, and with the anthropomorphic phantom, accurate recovery of the irregular ICA distribution in the shape of a tumor model is demonstrated. Applying the algorithm to DE-DBT transmission data of the CIRS BR3D phantom with solid ICA inserts yields images in which the depth-blurring is greatly reduced and the ICA distribution is accurately reconstructed.</p><p><strong>Conclusion: </strong>The results for the sparsity regularization algorithm applied to DE-DBT show promise, but as the algorithm performance is necessarily subject-dependent, further investigation using subjects with varying complexity in the ICA distribution is required.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13013"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In-silico</i> study of the impact of system design parameters on microcalcification detection in wide-angle digital breast tomosynthesis.","authors":"Xiaoyu Duan, Hailiang Huang, Wei Zhao","doi":"10.1117/1.JMI.12.S1.S13002","DOIUrl":"10.1117/1.JMI.12.S1.S13002","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate detection of microcalcifications ( <math><mrow><mi>μ</mi> <mi>Calcs</mi></mrow> </math> ) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving <math><mrow><mi>μ</mi> <mi>Calcs</mi></mrow> </math> detectability and (2) prioritize key optimization factors.</p><p><strong>Approach: </strong>An <i>in-silico</i> DBT pipeline was constructed to evaluate <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with <math><mrow><mn>120</mn> <mtext> </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis.</p><p><strong>Results: </strong>Results showed that FSM degraded <math><mrow><mi>μ</mi> <mi>Calcs</mi></mrow> </math> sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to <math><mrow><mn>50</mn> <mtext>  </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a <math><mrow><mn>50</mn> <mtext> </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC.</p><p><strong>Conclusions: </strong>Based on the magnitude of impact, the priority for enhancing <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13002"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Respiratory-gated micro-computed tomography imaging to measure radiation-induced lung injuries in mice following ultra-high dose-rate and conventional dose-rate radiation therapy.","authors":"Nancy Lee Ford, Xi Ren, Luca Egoriti, Nolan Esplen, Stephanie Radel, Brandon Humphries, Hui-Wen Koay, Thomas Planche, Cornelia Hoehr, Alexander Gottberg, Magdalena Bazalova-Carter","doi":"10.1117/1.JMI.12.1.014002","DOIUrl":"10.1117/1.JMI.12.1.014002","url":null,"abstract":"<p><strong>Purpose: </strong>Ultra-high dose-rate radiotherapy (FLASH-RT) shows the potential to eliminate tumors while sparing healthy tissues. To investigate radiation-induced lung damage, we used <i>in vivo</i> respiratory-gated micro-computed tomography (micro-CT) to monitor mice that received photon FLASH-RT or conventional RT on the FLASH irradiation research station at TRIUMF.</p><p><strong>Approach: </strong>Thirty healthy male C57BL/6 mice received baseline micro-CT scans followed by radiation therapy targeting the thorax. Treatments administered included no irradiation, 10-MV photon FLASH-RT, and 10-MV conventional RT with either 15 or 30 Gy prescribed dose. Follow-up micro-CT scans were obtained up to 24 weeks post-irradiation, and histology was obtained at the experimental endpoint. Lung volume and CT number were measured during peak inspiration and end-expiration and used to calculate the functional residual capacity (FRC) and tidal volume ( <math> <mrow><msub><mi>V</mi> <mi>T</mi></msub> </mrow> </math> ).</p><p><strong>Results: </strong>Radiation pneumonitis was observed sporadically in micro-CT images at 9 and 12 weeks post-irradiation. Fibrosis was observed in the endpoint images and confirmed with histology. Compared with the 15-Gy treatment groups and unirradiated controls, the micro-CT images for 30-Gy FLASH-RT showed differences during peak inspiration, with a significant reduction in <math> <mrow><msub><mi>V</mi> <mi>T</mi></msub> </mrow> </math> , whereas the 30-Gy conventional RT showed differences during end-expiration, with a significant difference in FRC from 15 Gy. Between 12 weeks and the endpoint, the 30-Gy conventional RT group exhibited the largest reduction in lung volume.</p><p><strong>Conclusions: </strong>Respiratory-gated micro-CT imaging was sensitive to radiation pneumonitis and fibrosis. Significant differences were seen in functional metrics measured at the endpoint for FRC (both 30-Gy groups) and <math> <mrow><msub><mi>V</mi> <mi>T</mi></msub> </mrow> </math> (30-Gy FLASH-RT) compared with the control.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014002"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound elastic modulus reconstruction using a deep learning model trained with simulated data.","authors":"Utsav Ratna Tuladhar, Richard A Simon, Cristian A Linte, Michael S Richards","doi":"10.1117/1.JMI.12.1.017001","DOIUrl":"10.1117/1.JMI.12.1.017001","url":null,"abstract":"<p><strong>Purpose: </strong>Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by solving the inverse problem on the measured displacement field from the ultrasound images. The limitations of traditional inverse problem techniques in US elastography are either slow and computationally intensive (iterative techniques) or sensitive to measurement noise and dependent on full displacement field data (direct techniques). Thus, we develop and validate a deep learning approach for solving the inverse problem in US elastography. This involves recovering the spatial modulus distribution of the elastic modulus from one component of the US-measured displacement field.</p><p><strong>Approach: </strong>We present a U-Net-based deep learning neural network to address the inverse problem in ultrasound elastography. This approach diverges from traditional methods by focusing on a data-driven model. The neural network is trained using data generated from a forward finite element model. This simulation incorporates variations in the displacement fields that correspond to the elastic modulus distribution, allowing the network to learn without the need for extensive real-world measurement data. The inverse problem of predicting the modulus spatial distribution from ultrasound-measured displacement fields is addressed using a trained neural network. The neural network is evaluated with mean squared error (MSE) and mean absolute percentage error (MAPE) metrics. To extend our model to practical purposes, we conduct phantom experiments and also apply our model to clinical data.</p><p><strong>Results: </strong>Our simulated results indicate that our deep learning (DL) model effectively reconstructs modulus distributions, as evidenced by low MSE and MAPE evaluation metrics. We obtain a mean MAPE of 0.32% for a hard inclusion and 0.39% for a soft inclusion. Similarly, in our phantom studies, the predicted modulus ratio aligns with the expected range, affirming the model's accuracy. These findings, alongside evaluations using the modulus ratio and contrast-to-noise ratio, confirm our DL model's robust generalization capabilities across diverse datasets.</p><p><strong>Conclusions: </strong>The presented work demonstrated that provided the simulated data are sufficiently diverse and representative of a wide variability, the algorithm trained on simulated data would generalize well to both phantom, as well as real-world clinical data.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"017001"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11796470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital breast tomosynthesis system concept addressing the needs in breast cancer screening and diagnosis.","authors":"Marcus Radicke, Marcel Beister, Stephan Dwars, Joerg Freudenberger, Pilar B Garcia-Allende, Bernhard Geiger, Katrin Hall, WenMan He, Axel Hebecker, Carina Heimann, Daan Hellingman, Magdalena Herbst, Mathias Hoernig, Thomas Klinnert, Ferdinand Lueck, Ralf Nanke, Ludwig Ritschl, Stefan Schaffert, Sabine Schneider, Daniel Stein, Julia Wicklein, Steffen Kappler","doi":"10.1117/1.JMI.12.S1.S13010","DOIUrl":"10.1117/1.JMI.12.S1.S13010","url":null,"abstract":"<p><strong>Purpose: </strong>Digital breast tomosynthesis (DBT) has been introduced more than a decade ago. Studies have shown higher breast cancer detection rates and lower recall rates, and it has become an established imaging method in diagnostic settings. However, full-field digital mammography (FFDM) remains the most common imaging modality for screening in many countries, as it delivers high-resolution planar images of the breast. To combine the advantages of DBT with the faster acquisition and the unique in-plane resolution capabilities known from FFDM, a system concept was developed for application in screening and diagnosis.</p><p><strong>Approach: </strong>The concept comprises an X-ray tube with adaptive focal spot position based on the flying focal spot (FFS) technology and optimized X-ray spectra. This is combined with innovative algorithmic concepts for tomosynthesis reconstruction and synthetic mammograms (SMs).</p><p><strong>Results: </strong>An X-ray tube with FFS was incorporated into a DBT system that performs 50-deg wide tomosynthesis scans with 25 projections in 4.85 s. Laboratory evaluations demonstrated significant improvements in the effective modular transfer function (eMTF). The improved eMTF as well as the effectiveness of the algorithmic concepts is shown in images from a clinical evaluation study.</p><p><strong>Conclusions: </strong>The DBT system concept enables high spatial resolution at short acquisition times. This leads to improved microcalcification visibility, reduced risk of motion artifacts, and shorter breast compression times. It shifts the in-plane resolution of DBT into the high-resolution range of FFDM. The presented technology leap might be a key contributor to facilitating the paradigm shift of replacing FFDM with DBT plus SM.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13010"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of patient habitus and acquisition protocol on iodine quantification in dual-source photon-counting computed tomography.","authors":"Leening P Liu, Rizza Pua, Michael Dieckmeyer, Nadav Shapira, Pooyan Sahbaee, Grace J Gang, Harold I Litt, Peter B Noël","doi":"10.1117/1.JMI.11.S1.S12806","DOIUrl":"10.1117/1.JMI.11.S1.S12806","url":null,"abstract":"<p><strong>Purpose: </strong>Evaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system.</p><p><strong>Approach: </strong>A multi-energy CT phantom with and without its extension ring equipped with various iodine inserts (0.2 to 15.0 mg/ml) was scanned over a range of radiation dose levels ( <math> <mrow> <msub><mrow><mi>CTDI</mi></mrow> <mrow><mi>vol</mi></mrow> </msub> </mrow> </math> 0.5 to 15.0 mGy) using two tube voltages (120, 140 kVp) and two different source modes (single-, dual-source). To assess the agreement between nominal and measured iodine concentrations, iodine density maps at different iterative reconstruction levels were utilized to calculate root mean square error (RMSE) and generate Bland-Altman plots by grouping radiation dose levels (ultra-low: <math><mrow><mo><</mo> <mn>1.5</mn></mrow> </math> ; low: 1.5 to 5; medium: 5 to 15 mGy) and iodine concentrations (low: <math><mrow><mo><</mo> <mn>5</mn></mrow> </math> ; high: 5 to 15 mg/mL).</p><p><strong>Results: </strong>Overall, quantification of iodine concentrations was accurate and reliable even at ultra-low radiation dose levels. RMSE ranged from 0.25 to 0.37, 0.20 to 0.38, and 0.25 to 0.37 mg/ml for ultra-low, low, and medium radiation dose levels, respectively. Similarly, RMSE was stable at 0.31, 0.28, 0.33, and 0.30 mg/ml for tube voltage and source mode combinations. Ultimately, the accuracy of iodine quantification was higher for the phantom without an extension ring (RMSE 0.21 mg/mL) and did not vary across different levels of iterative reconstruction.</p><p><strong>Conclusions: </strong>The first-generation PCCT allows for accurate iodine quantification over a wide range of iodine concentrations and radiation dose levels. Stable accuracy across iterative reconstruction levels may allow further radiation exposure reductions without affecting quantitative results.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 Suppl 1","pages":"S12806"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11278921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral optimization using fast kV switching and filtration for photon counting CT with realistic detector responses: a simulation study.","authors":"Sen Wang, Yirong Yang, Debashish Pal, Zhye Yin, Jonathan S Maltz, Norbert J Pelc, Adam S Wang","doi":"10.1117/1.JMI.11.S1.S12805","DOIUrl":"10.1117/1.JMI.11.S1.S12805","url":null,"abstract":"<p><strong>Purpose: </strong>Photon counting CT (PCCT) provides spectral measurements for material decomposition. However, the image noise (at a fixed dose) depends on the source spectrum. Our study investigates the potential benefits from spectral optimization using fast kV switching and filtration to reduce noise in material decomposition.</p><p><strong>Approach: </strong>The effect of the input spectra on noise performance in both two-basis material decomposition and three-basis material decomposition was compared using Cramer-Rao lower bound analysis in the projection domain and in a digital phantom study in the image domain. The fluences of different spectra were normalized using the CT dose index to maintain constant dose levels. Four detector response models based on Si or CdTe were included in the analysis.</p><p><strong>Results: </strong>For single kV scans, kV selection can be optimized based on the imaging task and object size. Furthermore, our results suggest that noise in material decomposition can be substantially reduced with fast kV switching. For two-material decomposition, fast kV switching reduces the standard deviation (SD) by <math><mrow><mo>∼</mo> <mn>10</mn> <mo>%</mo></mrow> </math> . For three-material decomposition, greater noise reduction in material images was found with fast kV switching (26.2% for calcium and 25.8% for iodine, in terms of SD), which suggests that challenging tasks benefit more from the richer spectral information provided by fast kV switching.</p><p><strong>Conclusions: </strong>The performance of PCCT in material decomposition can be improved by optimizing source spectrum settings. Task-specific tube voltages can be selected for single kV scans. Also, our results demonstrate that utilizing fast kV switching can substantially reduce the noise in material decomposition for both two- and three-material decompositions, and a fixed Gd filter can further enhance such improvements for two-material decomposition.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 Suppl 1","pages":"S12805"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11272100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Number of energy windows for photon counting detectors: is more actually more?","authors":"Katsuyuki Taguchi","doi":"10.1117/1.JMI.11.S1.S12807","DOIUrl":"10.1117/1.JMI.11.S1.S12807","url":null,"abstract":"<p><strong>Purpose: </strong>It has been debated whether photon counting detectors (PCDs) with moderate numbers of energy windows ( <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> ) perform better than PCDs with higher <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> . A higher <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> results in fewer photons in each energy window, which degrades the signal-to-noise ratio of each datum. Unlike energy-integrating detectors, PCDs add very little electronic noise to measured counts; however, there exists electronic noise on the pulse train, to which multiple energy thresholds are applied to count photons. The noise may increase the uncertainty of counts within energy windows; however, this effect has not been studied in the context of spectral imaging tasks. We aim to investigate the effect of <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> on the quality of the spectral information in the presence of electronic noise.</p><p><strong>Approach: </strong>We obtained the following three types of PCD data with various <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> (= 2 to 24) and noise levels using a Monte Carlo simulation: (A) A PCD with no electronic noise; (B) realistic PCDs with electronic noise added to the pulse train; and (C) hypothetical PCDs with electronic noise added to each energy window's output, similar to energy-integrating detectors. We evaluated the Cramér-Rao lower bound (CRLB) of estimation for the following two spectral imaging tasks: (a) water-bone material decomposition and (b) K-edge imaging.</p><p><strong>Results: </strong>For both the e-noise-free and realistic PCDs, the CRLB improved monotonically with increasing <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> for both tasks. In contrast, a moderate <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> provided the best CRLB for the hypothetical PCDs, and the optimal <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> was smaller when electronic noise was larger. Adding one energy window to the minimum necessary <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> for a given task gained 66.2% to 68.7% of the improvement <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> <mo>=</mo> <mn>24</mn></mrow> </math> provided.</p><p><strong>Conclusion: </strong>For realistic PCDs, the quality of the spectral information monotonically improves with increasing <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> .</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 Suppl 1","pages":"S12807"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning estimation of proton stopping power with photon-counting computed tomography: a virtual study.","authors":"Karin Larsson, Dennis Hein, Ruihan Huang, Daniel Collin, Andrea Scotti, Erik Fredenberg, Jonas Andersson, Mats Persson","doi":"10.1117/1.JMI.11.S1.S12809","DOIUrl":"10.1117/1.JMI.11.S1.S12809","url":null,"abstract":"<p><strong>Purpose: </strong>Proton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps.</p><p><strong>Approach: </strong>The XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net.</p><p><strong>Results: </strong>Prediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data.</p><p><strong>Conclusions: </strong>These early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 Suppl 1","pages":"S12809"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}