Medical physics最新文献

{"title":"Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer","authors":"Jue Jiang,&nbsp;Chloe Min Seo Choi,&nbsp;Maria Thor,&nbsp;Joseph O. Deasy,&nbsp;Harini Veeraraghavan","doi":"10.1002/mp.17536","DOIUrl":"10.1002/mp.17536","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We developed a <span>t</span>umo<span>r</span>-<span>a</span>ware re<span>c</span>urr<span>e</span>nt <span>r</span>egistration (TRACER) deep learning (DL) method and evaluated its suitability for VBA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF). Multiple CLSTM steps are used to compute a progressive sequence of deformations. Input conditioning was applied by including tumor segmentations with 3D image pairs as input channels. Bidirectional tumor rigidity, image similarity, and deformation smoothness losses were used to optimize the network in an unsupervised manner. TRACER and multiple DL methods were trained with 204 3D computed tomography (CT) image pairs from patients with lung cancers (LC) and evaluated using (a) Dataset I (<i>N</i> = 308 pairs) with DL segmented LCs, (b) Dataset II (<i>N</i> = 765 pairs) with manually delineated LCs, and (c) Dataset III with 42 LC patients treated with RT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>TRACER accurately aligned normal tissues. It best preserved tumors, indicated by the smallest tumor volume difference of 0.24%, 0.40%, and 0.13 % and mean square error in CT intensities of 0.005, 0.005, 0.004, computed between original and resampled moving image tumors, for Datasets I, II, and III, respectively. It resulted in the smallest planned RT tumor dose difference computed between original and resampled moving images of 0.01 and 0.013 Gy when using a female and a male reference.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>TRACER is a suitable method for inter-patient registration involving LC occurring in both fixed and moving images and applicable to voxel-based analysis methods.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"938-950"},"PeriodicalIF":3.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718139","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":"Evaluation of a deformable image registration algorithm for image-guided thermal ablation of liver tumors on clinically acquired MR-temperature maps","authors":"Valéry Ozenne,&nbsp;Pierre Bour,&nbsp;Thibaut Faller,&nbsp;Manon Desclides,&nbsp;Baudouin Denis de Senneville,&nbsp;Osman Öcal,&nbsp;Sergio Lentini,&nbsp;Max Seidensticker,&nbsp;Olaf Dietrich,&nbsp;Bruno Quesson","doi":"10.1002/mp.17526","DOIUrl":"10.1002/mp.17526","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;Quantitative real-time MRI-based temperature mapping techniques are hampered by abdominal motion. Intrascan motion can be reduced by rapid acquisition sequences such as 2D echo planar imaging (EPI), and inter-scan organ displacement can be compensated by image processing such as optical flow (OF) algorithms. However, motion field estimation can be seriously affected by local variation of signal intensity on magnitude images inherent to tissue heating, potentially leading to erroneous temperature estimates.&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;This study aims to characterize, in the context of clinical MRI-guided microwave ablation (MWA), a novel deformable image registration (DIR) algorithm that enhances the generation of thermal maps aligned to a reference position, a critical step for calculating cumulative thermal dose and, consequently, for the real-time evaluation of interventional procedure progress.&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;A retrospective image analysis was performed on 11 patients that underwent MWA of a liver tumor (primary or metastasis). Ablation duration was set to 9 ± 2 min with a 14-gauge large antenna. A stack of 13–20 contiguous slices was acquired dynamically (350 repetitions) at 1.5T using a single-shot EPI sequence. Evaluation was first performed on motion-free datasets (5 gated acquisitions using a cushion positioned in the patient abdomen) then with ones with motion (8 fixed-frequency acquisitions at 0.5 Hz). Temperature, thermal dose and lesion size were computed using three workflows: (i) standard phase subtraction (gold standard), (ii) conventional OF motion compensation, (iii) PCA-based OF motion compensation. The impact of flow field, temperature and lesion volume estimation were compared using averaged endpoint error (AEE), NRMSE and bland Altman plot, respectively.&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;Intensity signal decreases (close to 50%) were observed in the vicinity of the probe during MW energy delivery. Both motion correction algorithms reduce the NRMSE of magnitude images throughout the acquisition (&lt;i&gt;p&lt;/i&gt; &lt; 0.005) and achieve similar results between them. &lt;i&gt;Gated acquisition results&lt;/i&gt;. Conventional OF produced erroneous vector fields compared to the PCA-based OF, leading to higher maximal EE (3 mm vs. 1 mm) and temperature errors up to 15°C–20°C. PCA-based OF algorithm significantly reduces the NRMSE of temperature (&lt;i&gt;p&lt;/i&gt; &lt; 0.005). The conventional OF method underestimated the final size of lesions with a bias of 0.93 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"722-736"},"PeriodicalIF":3.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696319","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":"The TROG 15.01 stereotactic prostate adaptive radiotherapy utilizing kilovoltage intrafraction monitoring (SPARK) clinical trial database","authors":"Chandrima Sengupta,&nbsp;Doan Trang Nguyen,&nbsp;Yifan Li,&nbsp;Emily Hewson,&nbsp;Helen Ball,&nbsp;Ricky O'Brien,&nbsp;Jeremy Booth,&nbsp;John Kipritidis,&nbsp;Thomas Eade,&nbsp;Andrew Kneebone,&nbsp;George Hruby,&nbsp;Regina Bromley,&nbsp;Peter Greer,&nbsp;Jarad Martin,&nbsp;Perry Hunter,&nbsp;Lee Wilton,&nbsp;Trevor Moodie,&nbsp;Amy Hayden,&nbsp;Sandra Turner,&nbsp;Nicholas Hardcastle,&nbsp;Shankar Siva,&nbsp;Keen-Hun Tai,&nbsp;Sankar Arumugam,&nbsp;Mark Sidhom,&nbsp;Per Poulsen,&nbsp;Val Gebski,&nbsp;Alisha Moore,&nbsp;Paul Keall","doi":"10.1002/mp.17529","DOIUrl":"10.1002/mp.17529","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The US National Institutes of Health state that <i>Sharing of clinical trial data has great potential to accelerate scientific progress and ultimately improve public health by generating better evidence on the safety and effectiveness of therapies for patients</i> (https://www.ncbi.nlm.nih.gov/books/NBK285999/ accessed 2024-01-24.). Aligned with this initiative, the Trial Management Committee of the Trans-Tasman Radiation Oncology Group (TROG) 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing Kilovoltage intrafraction monitoring (KIM) (SPARK) clinical trial supported the public sharing of the clinical trial data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Acquisition and Validation Methods</h3>\u0000 \u0000 <p>The data originate from the TROG 15.01 SPARK clinical trial. The SPARK trial was a phase II prospective multi-institutional clinical trial (NCT02397317). The aim of the SPARK clinical trial was to measure the geometric and dosimetric cancer targeting accuracy achieved with a real-time image-guided radiotherapy technology named KIM for 48 prostate cancer patients treated in 5 treatment sessions. During treatment, real-time tumor translational and rotational motion were determined from x-ray images using the KIM technology. A dose reconstruction method was used to evaluate the dose delivered to the target and organs-at-risk. Patient-reported outcomes and toxicity data were monitored up to 2 years after the completion of the treatment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Format and Usage Notes</h3>\u0000 \u0000 <p>The dataset contains planning CT images, treatment plans, structure sets, planned and motion-included dose-volume histograms, intrafraction kilovoltage, and megavoltage projection images, tumor translational and rotational motion determined by KIM, tumor motion ground truth data, the linear accelerator trajectory traces, and patient treatment outcomes. The dataset is publicly hosted by the University of Sydney eScholarship Repository at https://doi.org/10.25910/qg5d-6058.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Potential Applications</h3>\u0000 \u0000 <p>The 3.6 Tb dataset, with approximately 1 million patient images, could be used for a variety of applications, including the development of real-time image-guided methods, adaptation strategies, tumor, and normal tissue control modeling, and prostate-specific antigen kinetics.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1941-1949"},"PeriodicalIF":3.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696320","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":"CT ventilation images produced by a 3D neural network show improvement over the Jacobian and HU DIR-based methods to predict quantized lung function","authors":"Daryl Wilding-McBride,&nbsp;Jeremy Lim,&nbsp;Hilary Byrne,&nbsp;Ricky O'Brien","doi":"10.1002/mp.17532","DOIUrl":"10.1002/mp.17532","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;Radiation-induced pneumonitis affects up to 33% of non-small cell lung cancer (NSCLC) patients, with fatal pneumonitis occurring in 2% of patients. Pneumonitis risk is related to the dose and volume of lung irradiated. Clinical radiotherapy plans assume lungs are functionally homogeneous, but evidence suggests that avoidance of high-functioning lung during radiotherapy can reduce the risk of radiation-induced pneumonitis. Radiotherapy avoidance structures can be constructed based on high-function regions indicated in a ventilation map, which can be produced from CT images.&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;Existing methods of deriving such a CT ventilation image (CTVI) require the use of deformable image registration (DIR) of peak-inhale and -exhale CT images, which is susceptible to inaccuracy for small or low-intensity regions, and sensitive to image artefacts. To overcome these problems, we use a neural network to predict a ventilation map from breath-hold CT (BHCT).&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 used the nnU-Net pipeline to train five-fold cross-validated ensemble models to predict a ventilation map (CTVI&lt;sub&gt;nnU-Net&lt;/sub&gt;). The training data were comprised of registered BHCT and Galligas PET images from 20 patients. Three training sets were created to ensure performance was averaged over different test patients. For each set, images from two randomly selected test patients were set aside, and models were trained on the remaining images. The ground truth was established by quantizing the Galligas PET images, assigning each voxel a label of high-function (&gt;70th percentile of intensity), medium-function (between 30th and 70th percentile), or low-function (&lt;30th percentile). For comparison, we created a CTVI with a 2D U-Net (CTVI&lt;sub&gt;nnU-Net-2D&lt;/sub&gt;), and with the Jacobian (CTVI&lt;sub&gt;Jac&lt;/sub&gt;) and Hounsfield Units (CTVI&lt;sub&gt;HU&lt;/sub&gt;) DIR-based methods which we quantized and labeled in the same way. The Dice similarity coefficient (DSC) and Hausdorff Distance 95th percentile (HD95) of each CTVI with the ground truth were measured separately for each lung function subregion.&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;CTVI&lt;sub&gt;nnU-Net&lt;/sub&gt; had the highest similarity to the quantized Galligas PET with a mean (range) DSC over all three categories of lung function at 0.68 (0.56 to 0.82), compared with 0.64 (0.47 to 0.75) for CTVI&lt;sub&gt;nnU-Net-2D&lt;/sub&gt;, 0.60 (0.38 to 0.73) for CTVI&lt;sub&gt;Jac&lt;/sub&gt;, and 0.56 (0.30 to 0.75) for CTVI&lt;sub&gt;HU&lt;/sub&gt;. CTVI&lt;sub&gt;nnU-Net&lt;/sub&gt; had the equal-lowest spatial distance to the quantized Galligas PET averaged over the three c","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"889-898"},"PeriodicalIF":3.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696318","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":"Generalizability of lesion detection and segmentation when ScaleNAS is trained on a large multi-organ dataset and validated in the liver","authors":"Jingchen Ma,&nbsp;Hao Yang,&nbsp;Yen Chou,&nbsp;Jin Yoon,&nbsp;Tavis Allison,&nbsp;Ravikumar Komandur,&nbsp;Jon McDunn,&nbsp;Asba Tasneem,&nbsp;Richard K. Do,&nbsp;Lawrence H Schwartz,&nbsp;Binsheng Zhao","doi":"10.1002/mp.17504","DOIUrl":"10.1002/mp.17504","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;Tumor assessment through imaging is crucial for diagnosing and treating cancer. Lesions in the liver, a common site for metastatic disease, are particularly challenging to accurately detect and segment. This labor-intensive task is subject to individual variation, which drives interest in automation using artificial intelligence (AI).&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;Evaluate AI for lesion detection and lesion segmentation using CT in the context of human performance on the same task. Use internal testing to determine how an AI-developed model (ScaleNAS) trained on lesions in multiple organs performs when tested specifically on liver lesions in a dataset integrating real-world and clinical trial data. Use external testing to evaluate whether ScaleNAS's performance generalizes to publicly available colorectal liver metastases (CRLM) from The Cancer Imaging Archive (TCIA).&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;The CUPA study dataset included patients whose CT scan of chest, abdomen, or pelvis at &lt;b&gt;C&lt;/b&gt;olumbia &lt;b&gt;U&lt;/b&gt;niversity between 2010–2020 indicated solid tumors (CUIMC, &lt;i&gt;n&lt;/i&gt; = 5011) and from two clinical trials in metastatic colorectal cancer, &lt;b&gt;P&lt;/b&gt;RIME (&lt;i&gt;n&lt;/i&gt; = 1183) and &lt;b&gt;A&lt;/b&gt;mgen (&lt;i&gt;n&lt;/i&gt; = 463). Inclusion required ≥1 measurable lesion; exclusion criteria eliminated 1566 patients. Data were divided at the patient level into training (&lt;i&gt;n&lt;/i&gt; = 3996), validation (&lt;i&gt;n&lt;/i&gt; = 570), and testing (&lt;i&gt;n&lt;/i&gt; = 1529) sets. To create the reference standard for training and validation, each case was annotated by one of six radiologists, randomly assigned, who marked the CUPA lesions without access to any previous annotations. For internal testing we refined the CUPA test set to contain only patients who had liver lesions (&lt;i&gt;n&lt;/i&gt; = 525) and formed an enhanced reference standard through expert consensus reviewing prior annotations. For external testing, TCIA-CRLM (&lt;i&gt;n&lt;/i&gt; = 197) formed the test set. The reference standard for TCIA-CRLM was formed by consensus review of the original annotation and contours by two new radiologists. Metrics for lesion detection were sensitivity and false positives. Lesion segmentation was assessed with median Dice coefficient, under-segmentation ratio (USR), and over-segmentation ratio (OSR). Subgroup analysis examined the influence of lesion size ≥ 10  mm (measurable by RECIST1.1) versus all lesions (important for early identification of disease progression).&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;ScaleNAS trained on all lesions achieved sensitivity of 71.4% and Dice of 70.2% for liver lesions in the CUPA internal test s","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1005-1018"},"PeriodicalIF":3.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690164","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":"The impact of tube voltage on the erosion of rotating x-ray anodes","authors":"Rolf Behling,&nbsp;Christopher Hulme,&nbsp;Panagiotis Tolias,&nbsp;Mats Danielsson","doi":"10.1002/mp.17528","DOIUrl":"10.1002/mp.17528","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 permitted input power density of rotating anode x-ray sources is limited by the performance of available target materials. The commonly used simplified formulas for the focal spot surface temperature ignore the tube voltage despite its variation in clinical practice. Improved modeling of electron transport and target erosion, as proposed in this work, improves the prediction of x-ray output degradation by target erosion, the absolute x-ray dose output and the quality of diagnostic imaging and orthovolt cancer therapy for a wide range of technique factors.&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;Improved modeling of electronic power absorption to include volume effects and surface erosion, to improve the understanding of x-ray output degradation, enhance the reliability of x-ray tubes, and safely widen their fields of use.&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 combine Monte Carlo electron transport simulations, coupled thermoelasticity finite element modelling, erosion-induced surface granularity, and the temperature dependence of thermophysical and thermomechanical target properties. A semi-empirical thermomechanical criterion is proposed to predict the target erosion. We simulate the absorbed electronic power of an eroded tungsten-rhenium target, mimicked by a flat target topped with a monolayer of spheres, and compare with a pristine target.&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;The absorbed electronic power and with it the conversion efficiency varies with tube voltage and the state of erosion. With reference to 80 kV (100%), the absorption of a severely eroded relative to a pristine target is 105% (30 kV), 99% (100 kV), 97% (120 kV), 96% (150 kV), 93% (200 kV), 87% (250 kV), and 79% (300 kV). We show that, although the simplistic Müller–Oosterkamp model of surface heating underestimates the benefit of higher tube voltages relative to operation at 80 kV, the error is limited to below −6% for 30 kV (reduction advised) and +13% for 300 kV (input power increase permitted).&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;Correcting the x-ray conversion efficiency of eroded target material, that is typically not accessible by measuring the tube current, may imply corrections to existing x-ray dose calculations. The relative increase of the allowable anode input power of rotating anode x-ray tubes with increasing tube voltage is substantially smaller than predicted by volume heatin","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"814-825"},"PeriodicalIF":3.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684061","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":"Synthesis of pseudo-PET/CT fusion images in radiotherapy based on a new transformer model","authors":"Hongfei Sun,&nbsp;Liting Chen,&nbsp;Jie Li,&nbsp;Zhi Yang,&nbsp;Jiarui Zhu,&nbsp;Zhongfei Wang,&nbsp;Ge Ren,&nbsp;Jing Cai,&nbsp;Lina Zhao","doi":"10.1002/mp.17512","DOIUrl":"10.1002/mp.17512","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>PET/CT and planning CT are commonly used medical images in radiotherapy for esophageal and nasopharyngeal cancer. However, repeated scans will expose patients to additional radiation doses and also introduce registration errors. This multimodal treatment approach is expected to be further improved.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>A new Transformer model is proposed to obtain pseudo-PET/CT fusion images for esophageal and nasopharyngeal cancer radiotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The data of 129 cases of esophageal cancer and 141 cases of nasopharyngeal cancer were retrospectively selected for training, validation, and testing. PET and CT images are used as input. Based on the Transformer model with a “focus-disperse” attention mechanism and multi-consistency loss constraints, the feature information in two images is effectively captured. This ultimately results in the synthesis of pseudo-PET/CT fusion images with enhanced tumor region imaging. During the testing phase, the accuracy of pseudo-PET/CT fusion images was verified in anatomy and dosimetry, and two prospective cases were selected for further dose verification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In terms of anatomical verification, the PET/CT fusion image obtained using the wavelet fusion algorithm was used as the ground truth image after correction by clinicians. The evaluation metrics, including peak signal-to-noise ratio, structural similarity index, mean absolute error, and normalized root mean square error, between the pseudo-fused images obtained based on the proposed model and ground truth, are represented by means (standard deviation). They are 37.82 (1.57), 95.23 (2.60), 29.70 (2.49), and 9.48 (0.32), respectively. These numerical values outperform those of the state-of-the-art deep learning comparative models. In terms of dosimetry validation, based on a 3%/2 mm gamma analysis, the average passing rates of global and tumor regions between the pseudo-fused images (with a PET/CT weight ratio of 2:8) and the planning CT images are 97.2% and 95.5%, respectively. These numerical outcomes are superior to those of pseudo-PET/CT fusion images with other weight ratios.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This pseudo-PET/CT fusion images obtained based on the proposed model hold promise as a new modality in the radiotherapy for esophageal and nasopharyngeal cancer.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1070-1085"},"PeriodicalIF":3.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684057","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":"An automated toolbox for microcalcification cluster modeling for mammographic imaging","authors":"Astrid Van Camp,&nbsp;Eva Punter,&nbsp;Katrien Houbrechts,&nbsp;Lesley Cockmartin,&nbsp;Renate Prevos,&nbsp;Nicholas W. Marshall,&nbsp;Henry C. Woodruff,&nbsp;Philippe Lambin,&nbsp;Hilde Bosmans","doi":"10.1002/mp.17521","DOIUrl":"10.1002/mp.17521","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;Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications.&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;Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks.&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;The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre-set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three-dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two-dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast-calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;s","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1335-1349"},"PeriodicalIF":3.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684047","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":"‘Dirty dose’-based proton variable RBE models - performance assessment on in vitro data","authors":"Fredrik Kalholm,&nbsp;Iuliana Toma-Dasu,&nbsp;Erik Traneus","doi":"10.1002/mp.17519","DOIUrl":"10.1002/mp.17519","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;In clinical proton radiotherapy, a constant relative biological effectiveness (RBE) of 1.1 is typically applied. Due to abundant evidence of variable RBE effects from in vitro data, multiple variable RBE models have been suggested, typically by describing the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 &lt;annotation&gt;$alpha$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 &lt;annotation&gt;$beta$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; parameters in the linear quadratic (LQ) model as a function of dose averaged linear energy transfer (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;LET&lt;/mtext&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{LET}_d$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;).&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;This work introduces a new variable RBE model based on the dirty dose concept, where dose deposited in voxels with a corresponding LET exceeding a specific threshold is considered “dirty” in the sense that it has a biological effect above the one predicted by a constant RBE of 1.1. As only one LET level, corresponding to a specific energy for a given particle in a given medium, needs to be monitored, this offers several advantages, such as simplified calculations by removing the need for intricate end of range LET calculations and averaging procedures, as well as opening up for more efficient experimental assessment of the cell specific model 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;Previously published in vitro data were utilized, where surviving fraction (SF), dose and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;LET&lt;/mtext&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{LET}_d$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; were reported for a pristine proton beam with varying physical PMMA thicknesses placed upstream of the cells. The setup was re-simulated to extract dirty dose metrics for the corresponding reported &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;LET&lt;/mtext&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{LET}_d$&lt;/annotation&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1311-1322"},"PeriodicalIF":3.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684052","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":"A hybrid network for fiber orientation distribution reconstruction employing multi-scale information","authors":"Hanyang Yu,&nbsp;Lingmei Ai,&nbsp;Ruoxia Yao,&nbsp;Jiahao Li","doi":"10.1002/mp.17505","DOIUrl":"10.1002/mp.17505","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Accurate fiber orientation distribution (FOD) is crucial for resolving complex neural fiber structures. However, existing reconstruction methods often fail to integrate both global and local FOD information, as well as the directional information of fixels, which limits reconstruction accuracy. Additionally, these methods overlook the spatial positional relationships between voxels, resulting in extracted features that lack continuity. In regions with signal distortion, many methods also exhibit issues with reconstruction artifacts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study addresses these challenges by introducing a new neural network called Fusion-Net.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Fusion-Net comprises both the FOD reconstruction network and the peak direction estimation network. The FOD reconstruction network efficiently fuses the global and local features of the FOD, providing these features with spatial positional information through a competitive coordinate attention mechanism and a progressive updating mechanism, thus ensuring feature continuity. The peak direction estimation network redefines the task of estimating fixel peak directions as a multi-class classification problem. It uses a direction-aware loss function to supply directional information to the FOD reconstruction network. Additionally, we introduce a larger input scale for Fusion-Net to compensate for local signal distortion by incorporating more global information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Experimental results demonstrate that the rich FOD features contribute to promising performance in Fusion-Net. The network effectively utilizes these features to enhance reconstruction accuracy while incorporating more global information, effectively mitigating the issue of local signal distortion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study demonstrates the feasibility of Fusion-Net for reconstructing FOD, providing reliable references for clinical applications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1019-1036"},"PeriodicalIF":3.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684043","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}