Medical physics最新文献

{"title":"List of Advertisers","authors":"","doi":"10.1002/mp.17199","DOIUrl":"https://doi.org/10.1002/mp.17199","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1350"},"PeriodicalIF":3.2,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating the optimization of proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning","authors":"Qingqing Wang, Chang Chang","doi":"10.1002/mp.17654","DOIUrl":"10.1002/mp.17654","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement learning (DRL) has recently been introduced to the planning processes of intensity-modulated radiation therapy (IMRT) and brachytherapy for prostate, lung, and cervical cancers. However, existing DRL planning models are built upon the Q-learning framework and rely on weighted linear combinations of clinical metrics for reward calculation. These approaches suffer from poor scalability and flexibility, that is, they are only capable of adjusting a limited number of planning objectives in discrete action spaces and therefore fail to generalize to more complex planning problems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Here we propose an automatic treatment planning model using the proximal policy optimization (PPO) algorithm in the policy gradient framework of DRL and a dose distribution-based reward function for proton PBS treatment planning of H&N cancers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The planning process is formulated as an optimization problem. A set of empirical rules is used to create auxiliary planning structures from target volumes and organs-at-risk (OARs), along with their associated planning objectives. Special attention is given to overlapping structures with potentially conflicting objectives. These planning objectives are fed into an in-house optimization engine to generate the spot monitor unit (MU) values. A decision-making policy network trained using PPO is developed to iteratively adjust the involved planning objective parameters. The policy network predicts actions in a continuous action space and guides the treatment planning system to refine the PBS treatment plans using a novel dose distribution-based reward function. A total of 34 H&N patients (30 for training and 4 for test) and 26 liver patients (20 for training, 6 for test) are included in this study to train and verify the effectiveness and generalizability of the proposed method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Proton H&N treatment plans generated by the model show improved OAR sparing with equal or superior target coverage when compared with human-generated plans. Moreover, additional experiments on liver cancer demonstrate that the proposed method can be successfully generalized to other treatment sites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"1997-2014"},"PeriodicalIF":3.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study","authors":"Aurélie Pauthe, Milan Milliner, Hugo Pasquier, Lucie Campagnolo, Sébastien Mulé, Alain Luciani","doi":"10.1002/mp.17651","DOIUrl":"10.1002/mp.17651","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To assess the impact of DLIR on image quality (IQ) and detectability of simulated hypervascular hepatocellular carcinoma (HCC) in fast kV-switching dual-energy CT (DECT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An anthropomorphic phantom of a standard patient morphology (body mass index of 23 kg m<sup>−2</sup>) with customized liver, including mimickers of hypervascular lesions in both late arterial phase (AP) and portal venous phase (PVP) enhancement, was scanned on a DECT. Virtual monoenergetic images were reconstructed from raw data at four energy levels (40/50/60/70 keV) using filtered back-projection (FBP), adaptive statistical iterative reconstruction-V 50% and 100% (ASIRV-50 and ASIRV-100), DLIR low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The contrast between the lesion and the liver parenchyma, the noise magnitude, the average and peak frequencies (f<sub>avg</sub> and f<sub>peak</sub>) of the noise power spectrum (NPS) reflecting noise texture, and the task-based measure of the modulation transfer function (MTF<sub>task</sub>) were measured to evaluate spatial resolution. A detectability index (d′) was computed to model the detection of hypervascular lesions in both AP and PVP. Metrics were compared between reconstructions and between energy levels using a Friedman test with follow-up post-hoc multiple comparison.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Lesion-to-liver contrast significantly increased with decreasing energy level in both AP and PVP (<i>p</i> ≤ 0.042) but was not affected by reconstruction algorithm (<i>p</i> ≥ 0.57). Overall, noise magnitude increased with decreasing energy levels and was the lowest with ASIRV-100 at all energy levels in both AP and PVP (<i>p</i> ≤ 0.01) and significantly lower with DLIR-M and DLIR-H reconstructions compared to ASIRV-50 and DLIR-L (<i>p</i> < 0.001). For all reconstructions, noise texture within the liver tended to get smoother with decreasing energy; f<sub>avg</sub> significantly shifted towards lower frequencies from 70 to 40 keV (<i>p</i> ≤ 0.01). Noise texture was the smoothest with ASIRV-100 (<i>p</i> < 0.001) while DLIR-L had the noise texture closer to the one of FBP. The spatial resolution was not significantly affected by the energy level, but it was degraded when increasing the level of ASIRV and ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2257-2268"},"PeriodicalIF":3.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implicit neural representation-based method for metal-induced beam hardening artifact reduction in X-ray CT imaging","authors":"Hyoung Suk Park,&nbsp;Jin Keun Seo,&nbsp;Kiwan Jeon","doi":"10.1002/mp.17649","DOIUrl":"10.1002/mp.17649","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In X-ray computed tomography (CT), metal-induced beam hardening artifacts arise from the complex interactions between polychromatic X-ray beams and metallic objects, leading to degraded image quality and impeding accurate diagnosis. A previously proposed metal-induced beam hardening correction (MBHC) method provides a theoretical framework for addressing nonlinear artifacts through mathematical analysis, with its effectiveness demonstrated by numerical simulations and phantom experiments. However, in practical applications, this method relies on precise segmentation of highly attenuating materials and parameter estimations, which limit its ability to fully correct artifacts caused by the intricate interactions between metals and other dense materials, such as bone or teeth.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to develop a parameter-free MBHC method that eliminates the need for accurate segmentation and parameter estimations, thereby addressing the limitations of the original MBHC approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed method employs implicit neural representations (INR) to generate two tomographic images: one representing the monochromatic attenuation distribution at a specific energy level, and another capturing the nonlinear beam hardening effects caused by the polychromatic nature of X-ray beams. A loss function drives the generation of these images, where the predicted projection data is nonlinearly modeled by the combination of the two images. This approach eliminates the need for geometric and parameter estimation of metals, providing a more generalized solution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Numerical and phantom experiments demonstrates that the proposed method effectively reduces beam hardening artifacts caused by interactions between highly attenuating materials such as metals, bone, and teeth. Additionally, the proposed INR-based method demonstrates potential in addressing challenges related to data insufficiencies, such as photon starvation and truncated fields of view in CT imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed generalized MBHC method provides high-quality image reconstructions without requiring parameter estimations and segmentations, offering a robust solution for reducing metal-induced beam hardening artifacts in CT imaging.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2201-2211"},"PeriodicalIF":3.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis","authors":"Jingtong Zhao,&nbsp;Eugene Vaios,&nbsp;Zhenyu Yang,&nbsp;Ke Lu,&nbsp;Scott Floyd,&nbsp;Deshan Yang,&nbsp;Hangjie Ji,&nbsp;Zachary J. Reitman,&nbsp;Kyle J. Lafata,&nbsp;Peter Fecci,&nbsp;John P. Kirkpatrick,&nbsp;Chunhao Wang","doi":"10.1002/mp.17635","DOIUrl":"10.1002/mp.17635","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence. However, their clinical adoption is hindered by a lack of explainability, limiting understanding and trust in their diagnostic decisions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To utilize a novel neural ordinary differential equation (NODE) model for discerning BM post-SRS radionecrosis from recurrence. This approach integrates image-deep features, genomic biomarkers, and non-image clinical parameters within a synthesized latent feature space. The trajectory of each data sample towards the diagnosis decision can be visualized within this feature space, offering a new angle on radiogenomic data analysis foundational for AI explainability.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we designed a novel model based on heavy ball NODE (HBNODE) in which deep feature extraction was governed by a second-order ODE. This approach enabled tracking of deep neural network (DNN) behavior by solving the HBNODE and observing the stepwise derivative evolution. Consequently, the trajectory of each sample within the Image-Genomic-Clinical (I-G-C) space became traceable. A decision-making field (&lt;b&gt;&lt;i&gt;F&lt;/i&gt;&lt;/b&gt;) was reconstructed within the feature space, with its gradient vectors directing the data samples’ trajectories and intensities showing the potential. The evolution of &lt;b&gt;&lt;i&gt;F&lt;/i&gt;&lt;/b&gt; reflected the cumulative feature contributions at intermediate states to the final diagnosis, enabling quantitative and dynamic comparisons of the relative contribution of each feature category over time. A velocity curve was designed to determine key intermediate states (locoregional ∇&lt;b&gt;&lt;i&gt;F &lt;/i&gt;&lt;/b&gt;=&lt;b&gt; &lt;/b&gt;0) that are most predictive. Subsequently, a non-parametric model aggregated the optimal solutions from these key states to predict outcomes.&lt;/p&gt;\u0000 \u0000 &lt;p&gt;Our dataset included 90 BMs from 62 NSCLC patients, and 3-month post-SRS T1+c MR image features, seven NSCLC genomic features, and seven clinical features were analyzed. An 8:2 train/test assignment was employed, and five independent models were trained to ensure robustness. Performance was benchmarked in sensitivity, specificity, accuracy, and ROC&lt;sub&gt;AUC","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2661-2674"},"PeriodicalIF":3.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MR imaging of proton beam-induced oxygen depletion","authors":"Juliane Schieferecke,&nbsp;Aswin Hoffmann,&nbsp;Jörg Pawelke","doi":"10.1002/mp.17622","DOIUrl":"10.1002/mp.17622","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Previous studies have shown that in-beam magnetic resonance imaging (MRI) can be used to visualize a proton beam during the irradiation of liquid-filled phantoms. The beam energy- and current-dependent local image contrast observed in water was identified to be predominantly caused by beam-induced buoyant convection and associated flow effects. Besides this flow dependency, the MR signal change was found to be characterized by a change in the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm T}_{1}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; relaxation time of water, hinting at a radiochemical contribution, which was hypothesized to lie in oxygen depletion-evoked &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm T}_{1}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; relaxation time lengthening. The elucidation of the underlying contrast mechanism is required to enable the further assessment of the application potential of MRI-based proton beam visualization in tissue.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The underlying radiochemical cause of the observed local beam-induced change in the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm T}_{1}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; relaxation time of water should be identified in beam visualization experiments testing the hypothesis of beam-induced oxygen depletion-evoked &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${rm T}_{1}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; relaxation time lengthening.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Combined irradiation and imaging experiments were performed using static proton pencil beam irradiation, background-nulled inversion recovery (IR) MRI and a range of flow-restricted phantoms differing in initial oxygen concentration and homogeneity. The similarity of the irradiation-induced MRI contrast to the proton pencil beam dose distribution ac","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2454-2464"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photon mini-GRID therapy for preoperative breast cancer tumor treatment: A treatment plan study","authors":"Angela Corvino,&nbsp;Tim Schneider,&nbsp;Jeremi Vu-Bezin,&nbsp;Pierre Loap,&nbsp;Youlia Kirova,&nbsp;Yolanda Prezado","doi":"10.1002/mp.17634","DOIUrl":"10.1002/mp.17634","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Breast cancer is the leading cause of female cancer mortality worldwide, accounting for 1 in 6 cancer deaths. Surgery, radiation, and systemic therapy are the three pillars of breast cancer treatment, with several strategies developed to combine them. The association of preoperative radiotherapy with immunotherapy may improve breast cancer tumor control by exploiting the tumor radio-induced immune priming. However, this requires the use of hypofractionated radiotherapy (3 × 8 Gy), increasing the risk of toxicity. Mini-GRID therapy (mini-GRT) is an innovative form of spatially fractionated radiation therapy (SFRT) characterized by narrow beam widths between 1 to 2 mm that promises a significant increase in normal tissue dose tolerances and could thereby represent a new alternative for preoperative breast cancer treatment. Mini-GRT has been successfully implemented at the Hospital de Santiago de Compostela (Spain) with a flattening filter-free LINAC (megavoltage x-rays).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In this dosimetry proof-of-concept study, we evaluate the feasibility of photon mini-GRT for preoperative breast cancer treatment. We also assess the clinical potential of mini-GRT and compare it with the current treatment standard of intensity-modulated radiotherapy (IMRT).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Seven unbiased breast cancer dosimetries of patients treated with stereotactic body radiotherapy (SBRT) (3 × 8 Gy, IMRT) were selected for the study. Photon mini-GRT was compared with SBRT using three main criteria: (i) the dose to organs at risk (OARs), (ii) the dose constraints dictated by normal tissue tolerance, and (iii) the lateral penumbra in OARs. Tumor coverage was evaluated in terms of normalized total dose at 8 Gy-fractions. The optimized SBRT by IMRT was realized at the Institut Curie, Paris, France. The dose in mini-GRT was calculated by means of Monte Carlo simulations based on the mini-GRT implementation realized at the University Hospital in Santiago de Compostela.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Compared to SBRT plans, mini-GRT resulted in a reduction of the mean dose to the lungs, heart, chest wall, and lymph nodes in the studied cases by a factor ranging from 50% to 100%. Additionally, valley, mean, and peak doses to normal tissues meet the dose tolerance limits for the considered OARs, the most challenging of all being the skin. The mean dose to the skin was reduced (20%–60% less) for most of the studied cases. Mini-GRT also yielded sharper lateral p","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2493-2506"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17634","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scanning K-edge subtraction (SKES) imaging with laser-compton x-ray sources","authors":"Trevor Reutershan,&nbsp;Christine V. Nguyen,&nbsp;Haytham H. Effarah,&nbsp;Eric C. Nelson,&nbsp;Kyle D. Chesnut,&nbsp;Christopher P. J. Barty","doi":"10.1002/mp.17638","DOIUrl":"10.1002/mp.17638","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;K-edge subtraction (KES) imaging is a dual-energy imaging technique that enhances contrast by subtracting images taken with x-rays that are above and below the K-edge energy of a specified contrast agent. The resulting reconstruction spatially identifies where the contrast agent accumulates, even when obscured by complex and heterogeneous distributions of human tissue. This method is most successful when x-ray sources are quasimonoenergetic and tunable, conditions that have traditionally only been met at synchrotrons. Laser-Compton x-ray sources (LCSs) are a compact alternative to synchrotron radiation with a quasimonoenergetic x-ray spectrum. One limitation in the clinical application of KES imaging with LCSs has been the extensive time required to tune the x-ray spectrum to two different energies.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We introduce an imaging technique called scanning K-edge subtraction (SKES) that leverages the angle-correlated laser-Compton x-ray spectrum in the setting of mammography. The feasibility and utility of this technique will be evaluated through a series of simulation studies. The goal of SKES imaging is to enable rapid K-edge subtraction imaging using a laser-Compton x-ray source. The technique does not rely on the time-consuming process of tuning laser-Compton interaction parameters.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Laser-Compton interaction physics are modeled using conditions based on an X-band linear electron accelerator architecture currently under development using a combination of 3D particle tracking software and Mathematica. The resulting angle-correlated laser-Compton x-ray beam is propagated through digitally compressed breast phantoms containing iodine contrast-enhanced inserts and then to a digital flat-panel detector using a Matlab Monte Carlo propagation software. This scanning acquisition technique is compared to the direct energy tuning method (DET), as well as to a clinically available dual-energy contrast-enhanced mammography (CEM) system.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;KES imaging in a scanning configuration using an LCS was able to generate a KES image of comparable quality to the direct energy tuning method. SKES was able to detect tumors with iodine contrast concentrations lower than what is clinically available today including lesions that are typically obscured by dense fibroglandular tissue. After normalizing to mean glandular dose, SKES is able to generate a KES image with equal contrast to CEM using only 3% of the ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2475-2492"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-based perfusion reconstruction with time separation technique in cone-beam CT dynamic liver perfusion imaging","authors":"Hana Haseljić,&nbsp;Robert Frysch,&nbsp;Vojtěch Kulvait,&nbsp;Thomas Werncke,&nbsp;Inga Brüsch,&nbsp;Oliver Speck,&nbsp;Jessica Schulz,&nbsp;Michael Manhart,&nbsp;Georg Rose","doi":"10.1002/mp.17652","DOIUrl":"10.1002/mp.17652","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The success of embolization, a minimally invasive treatment of liver cancer, could be evaluated in the operational room with cone-beam CT by acquiring a dynamic perfusion scan to inspect the contrast agent flow.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The reconstruction algorithm must address the issues of low temporal sampling and higher noise levels inherent in cone-beam CT systems, compared to conventional CT.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Therefore, a model-based perfusion reconstruction based on the time separation technique (TST) was applied. TST uses basis functions to model time attenuation curves. These functions are either analytical or based on prior knowledge (PK), extracted using singular value decomposition of the classical CT perfusion data of animal subjects. To explore how well the PK can model perfusion dynamics and what the potential limitations are, the dynamic cone-beam CT (CBCT) perfusion scan was simulated from a dynamic CT perfusion scan under different noise levels. The TST method was compared to static reconstruction.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;It was demonstrated on this simulated dynamic CBCT perfusion scan that a set consisting of only four basis functions results in perfusion maps that preserve relevant information, denoise the data, and outperform static reconstruction under higher noise levels. TST with PK would not only outperform static reconstruction but also the TST with analytical basis functions. Furthermore, it has been shown that only eight CBCT rotations, unlike previously assumed ten, are sufficient to obtain the perfusion maps comparable to the reference CT perfusion maps. This contributes to saving dose and reconstruction time. The real dynamic CBCT perfusion scan, reconstructed under the same conditions as the simulated scan, shows potential for maintaining the accuracy of the perfusion maps. By visual inspection, the embolized region was matching to that in corresponding CT perfusion maps.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;CBCT reconstruction of perfusion scan data using the TST method has shown promising potential, outperforming static reconstructions and potentially saving dose by reducing the necessary number of acquisition sweeps. Further analysis of a larger cohort of patient data is needed to draw final conclusions regarding the expected advantages of the TST.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 &lt;/d","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2074-2088"},"PeriodicalIF":3.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling absorbed alpha particle dose from diffusing alpha-emitters radiation therapy in changing tissue volumes","authors":"Irene P. Zhang,&nbsp;Guy Heger,&nbsp;Gil'ad N. Cohen,&nbsp;Lior Arazi,&nbsp;Antonio L. Damato","doi":"10.1002/mp.17646","DOIUrl":"10.1002/mp.17646","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Diffusing alpha-emitters Radiation Therapy (“Alpha DaRT”) is a promising new radiation therapy modality for treating bulky tumors. &lt;sup&gt;224&lt;/sup&gt;Ra-carrying sources are inserted intratumorally, producing a therapeutic alpha-dose region with a total size of a few millimeter via the diffusive motion of &lt;sup&gt;224&lt;/sup&gt;Ra's alpha-emitting daughters. Clinical studies of Alpha DaRT have reported 100% positive response (30%–100% shrinkage within several weeks), with post-insertion swelling in close to half of the cases. While dosimetry recommendations informed by the effects of edema are standard in some radiation therapy modalities, the effect of edema and tumor shrinkage on the absorbed dose delivered by Alpha DaRT is still unknown.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The aim of this work is to develop a simple model for Alpha-DaRT dose deposition in a time-dependent tissue volume in order to study the effect of geometrical changes in source location due to edema and tumor shrinkage on the delivered alpha particle dose.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We perform FEM-based dose deposition modeling for a single Alpha-DaRT source inside shrinking and swelling tissues. Gradual volume change models were used for shrinkage and swelling, and an additional immediate volume gain model was also used for “worst case” swelling. Volume change rates were estimated from source location data from serial scans acquired at time of insertion and removal for seven patients treated using Alpha DaRT. We calculate absorbed dose profiles under both the high- and low-diffusion regimes described by the Diffusion-Leakage model.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Changes in tissue volume can lead to over- or underestimation of the calculated absorbed dose. In the low-diffusion regime, gradual tissue shrinkage can result in an increase of 100% and gradual swelling can result in a 35% decrease in absorbed dose compared to a calculation in static tissue. Although immediate post-insertion swelling can reduce the absorbed dose by close to 65% for very closely spaced sources, in all cases analyzed the final absorbed dose continues to exceed the 10 Gy target. These effects are less severe in the high-diffusion regime.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;These results indicate that tissue swelling and shrinkage can have a non-negligible effect on the tumor absorbed dose. Further study of tissue dynamics during Alpha-DaRT treatment will be necessary for improvements in dosimetry practice.&lt;/p&gt;\u0000 &lt;","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2618-2631"},"PeriodicalIF":3.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}