Medical physics最新文献

{"title":"Dosimetric and biological impact of activity extravasation of radiopharmaceuticals in PET imaging","authors":"Ashok Tiwari, Matthew Andriotty, Greeshma Agasthya, John J. Sunderland, Dustin R. Osborne, Anuj J. Kapadia","doi":"10.1002/mp.17520","DOIUrl":"10.1002/mp.17520","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The increasing use of nuclear medicine and PET imaging has intensified scrutiny of radiotracer extravasation. To our knowledge, this topic is understudied but holds great potential for enhancing our understanding of extravasation in clinical PET imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This work aims to (1) quantify the absorbed doses from radiotracer extravasation in PET imaging, both locally at the site of extravasation and with the extravasation location as a source of exposure to bodily organs and (2) assess the biological ramifications within the injection site at the cellular level.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A radiation dosimetry simulation was performed using a whole-body 4D Extended Cardiac-Torso (XCAT) phantom embedded in the GATE Monte Carlo platform. A 10-mCi dose of <sup>18</sup>F-FDG was chosen to simulate a typical clinical PET scan scenario, with 10% of the activity extravasated in the antecubital fossa of the right arm of the phantom. The extravasation volume was modeled as a 5.5 mL rectangle in the hypodermal layer of skin. Absorbed dose contributions were calculated for the first two half-lives, assuming biological clearance thereafter. Dose calculations were performed as absorbed doses at the organ and skin levels. Energy deposition was simulated both at the local extravasation site and in multiple organs of interest and converted to absorbed doses based on their respective masses. Each simulation was repeated ten times to estimate Monte Carlo uncertainties. Biological impacts on cells within the extravasated volume were evaluated by randomizing cells and exposing them to a uniform radiation source of <sup>18</sup>F and <sup>68</sup>Ga. Particle types, their energies, and direction cosines were recorded in phase space files using a separate Geant4 simulation to characterize their entry into the nucleus of the cellular volume. Subsequently, the phase space files were imported into the TOPAS-nBio simulation to assess the extent of DNA damage, including double-strand breaks (DSBs) and single-strand breaks (SSBs).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Organ-level dosimetric estimations are presented for <sup>18</sup>F and <sup>68</sup>Ga radionuclides in various organs of interest. With 10% extravasation, the hypodermal layer of the skin received the highest absorbed dose of 1.32 ± 0.01 Gy for <sup>18</sup>F and 0.99 ± 0.01 Gy for <sup>68</sup>Ga. The epidermal and dermal layers received absorbed doses of 0.07 ± 0.01 Gy and 0.13 ± 0.01 Gy for <sup>18</sup>F, and 0.14 ± 0.01 Gy and 0.29 ± 0.01 Gy for <sup>68</sup>Ga, respectively. In th","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"801-813"},"PeriodicalIF":3.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684054","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":"RapidBrachyIVBT: A dosimetry software for patient-specific intravascular brachytherapy dose calculations on optical coherence tomography images","authors":"Maryam Rahbaran, Jonathan Kalinowski, Joseph M. DeCunha, Kevin J. Croce, Brian A. Bergmark, James M. G. Tsui, Phillip M. Devlin, Shirin A. Enger","doi":"10.1002/mp.17525","DOIUrl":"10.1002/mp.17525","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Coronary artery disease is the most common form of cardiovascular disease. It is caused by excess plaque along the arterial wall, blocking blood flow to the heart (stenosis). A percutaneous coronary intervention widens the arterial wall with the inflation of a balloon inside the lesion area and leaves behind a metal stent to prevent re-narrowing of the artery (restenosis). However, in-stent restenosis may occur due to damage to the arterial wall tissue, triggering neointimal hyperplasia, producing fibrotic and calcified plaques and narrowing the artery again. Drug-eluting stents, which slowly release medication to inhibit neointimal hyperplasia, are used to prevent in-stent restenosis but fail up to 20% of cases. Coronary intravascular brachytherapy (IVBT), which uses <span></span><math>\u0000 <semantics>\u0000 <mi>β</mi>\u0000 <annotation>$beta$</annotation>\u0000 </semantics></math>-emitting radionuclides to prevent in-stent restenosis, is used in these failed cases to prevent in-stent restenosis. However, current clinical dosimetry for IVBT is water-based, and heterogeneities such as the guidewire of the IVBT device, fibrotic and calcified plaques and stents are not considered.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aimed to develop a Monte Carlo-based dose calculation software, accounting for patient-specific geometry from Optical Coherence Tomography (OCT) images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>RapidBrachyIVBT, a Monte Carlo dose calculation software based on the Geant4 toolkit v. 10.02.p02, was developed and integrated into RapidBrachyMCTPS, a treatment planning system for brachytherapy applications. The only commercially available IVBT delivery system, the Novoste Beta-Cath 3.5F, with a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow></mrow>\u0000 <mn>90</mn>\u0000 </msup>\u0000 <msup>\u0000 <mi>Sr</mi>\u0000 <mn>90</mn>\u0000 </msup>\u0000 <mi>Y</mi>\u0000 </mrow>\u0000 <annotation>$^{90}{rm Sr}^{90}{rm Y}$</annotation>\u0000 </semantics></math> source train, was modeled with 30, 40, and 60 mm source train lengths. The software was validated with published TG-149 parameters compared to Monte Carlo simulations in water. The dose calculation engine was tested with OCT images from a patient undergoin","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1256-1267"},"PeriodicalIF":3.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678137","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 radiation therapy platform to enable upright cone beam computed tomography and future upright treatment on existing photon therapy machines","authors":"James C. Korte, Mark Wright, Prem G. Krishnan, Nicholas Winterling, Sulman Rahim, Katrina Woodford, Elizabeth Pearson, Susan Harden, Fiona Hegi-Johnson, Nikki Plumridge, Tsien Fua, Kate Moodie, Andrew Fielding, Sarah Hegarty, Tomas Kron, Nicholas Hardcastle","doi":"10.1002/mp.17523","DOIUrl":"10.1002/mp.17523","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The conventional lying down position for radiation therapy can be challenging for patients due to pain, swallowing or breathing issues. To provide an alternative upright treatment position for these patients, we have developed a portable rotating radiation therapy platform which integrates with conventional photon treatment machines. The device enables cone-beam computed tomography (CBCT) imaging of patients in an upright position, and the future delivery of therapeutic radiation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To design, manufacture, and test a device for upright radiation therapy. A collaborative partnership between physicists, engineers, radiation therapists, radiation oncologists, implementation researchers and consumers was established, to create a device that meets both the clinical and technical requirements of upright radiation therapy. The device is central to a clinical trial (ACTRN12623000498695) which will evaluate upright image quality in the context of future image guided radiation therapy for patients with lung cancer or head and neck cancer.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The weight and physical constraints of the device were assessed with respect to the American civilian population. The final design was evaluated with a series of tests to characterize the angular accuracy of the platform rotation and the reproducibility of the platform setup position in a radiation treatment room. To acquire an upright CBCT, the platform movement system was synchronized to the kilo-voltage fluoroscopic imaging on an existing treatment machine. The accuracy of the synchronization was evaluated by assessing the positional reproducibility of upright CBCT imaging of a chest phantom.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The platform has a weight limit of up to 125 kg which is suitable for approximately 90% of males and 95% of females. The platform has physical constraints that accommodate approximately 95.6% of males and 99.6% of females: a maximum seated height of 97.5 cm, a maximum hip breadth of 63.0 cm, and maximum elbow to knuckle length of 46.5 cm. The angular accuracy of the motion system is within ±0.15° over a full rotation, which is within the guidelines for machine movement accuracy in radiation therapy (1 mm/1°). The platform is a portable device and can be reproducibly positioned in a radiation therapy treatment room with a translational range within ±0.04 mm and a rotational range within ±0.025°. The CBCT imaging can reproducibly detect the position of a chest p","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1133-1145"},"PeriodicalIF":3.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670175","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":"Proof-of-principle of 3D-printed track-end detectors for dosimetry in proton therapy","authors":"Vicky Bietenbeck, Claus Maximilian Bäcker, Jörg Wulff, Beate Timmermann, Christian Bäumer","doi":"10.1002/mp.17515","DOIUrl":"10.1002/mp.17515","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Dosimetric equipment in particle therapy (PT) is associated with high costs. There is a lack of versatile, tissue-equivalent detectors suitable for in-vivo dosimetry. Faraday-cup (FC) type detectors are sensitive to stopped protons, that is, to track-ends (TEs). They experience a renaissance in PT as they can cope with high dose rates. Owing to their simple functional principle, production of FC could benefit from the dynamic technological developments in additive manufacturing of sensors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To build FC-type detectors for PT by standard 3D-printing. This study seeks to build an integrating, single-channel (SC) FC for replacement of a traditional FC and a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>×</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$2times 2$</annotation>\u0000 </semantics></math> array of FC elements indicating the feasibility of a spatially resolving detector.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Samples of FCs were produced with a dual-extruder 3D-printer with polylactic-acid filaments, which contained graphite in the conductive parts of the detector. Production was optimized in terms of materials and printing temperature. Samples were characterized by electrical tests and non-destructive 3D x-ray imaging. Beam tests were conducted at a clinical PT machine.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Operational FC-type detectors for proton fields were printed. The detected charge of the SC FC corresponded qualitatively to the one of a traditional FC. A <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>×</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$2times 2$</annotation>\u0000 </semantics></math> FC array was fabricated in a single run. There was a linear relationship between the response of the individual FC elements and the machine output.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>3D-printing is a viable method for producing low-cost, tissue-equivalent, FC-type detectors for PT. They could potentially be used as TE detectors in anthropomorphic phantoms.</p>\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"737-741"},"PeriodicalIF":3.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670177","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":"MMD-Net: Image domain multi-material decomposition network for dual-energy CT imaging","authors":"Jiongtao Zhu,&nbsp;Xin Zhang,&nbsp;Ting Su,&nbsp;Han Cui,&nbsp;Yuhang Tan,&nbsp;Hao Huang,&nbsp;Jinchuan Guo,&nbsp;Hairong Zheng,&nbsp;Dong Liang,&nbsp;Guangyao Wu,&nbsp;Yongshuai Ge","doi":"10.1002/mp.17500","DOIUrl":"10.1002/mp.17500","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>In this work, a novel multi-material decomposition network (MMD-Net) is proposed to improve the multi-material decomposition performance of DECT imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To achieve dual-energy multi-material decomposition, a deep neural network, named as MMD-Net, is proposed in this work. In MMD-Net, two specific convolutional neural network modules, Net-I and Net-II, are developed. Specifically, Net-I is used to distinguish the material triangles, while Net-II predicts the effective attenuation coefficients corresponding to the vertices of the material triangles. Subsequently, the material-specific density maps are calculated analytically through matrix inversion. The new method is validated using in-house benchtop DECT imaging experiments with a solution phantom and a pig leg specimen, as well as commercial medical DECT imaging experiments with a human patient. The decomposition accuracy, edge spreading function, and noise power spectrum are quantitatively evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Compared to the conventional multiple material decomposition (MMD) algorithm, the proposed MMD-Net method is more effective at suppressing image noise. Additionally, MMD-Net outperforms the iterative MMD approach in maintaining decomposition accuracy, image sharpness, and high-frequency content. Consequently, MMD-Net is capable of generating high-quality material decomposition images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A high performance multi-material decomposition network is developed for dual-energy CT imaging.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"771-786"},"PeriodicalIF":3.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670176","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":"Markerless tracking of tumor and tissues: A motion model approach","authors":"Ling Fung Cheung,&nbsp;Shinichirou Fujitaka,&nbsp;Takaaki Fujii,&nbsp;Naoki Miyamoto,&nbsp;Seishin Takao","doi":"10.1002/mp.17524","DOIUrl":"10.1002/mp.17524","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;Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking.&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 present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images.&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 proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition.&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;In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3 mm to less than 1 mm after correction. For the shallow breathing motion trace with a 1.7 mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5 mm to less than 1 mm after correction.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The method combines the detailed structural informat","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1193-1206"},"PeriodicalIF":3.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640262","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 energy-conserving dose summation method for dose accumulation in radiotherapy","authors":"Hualiang Zhong","doi":"10.1002/mp.17514","DOIUrl":"10.1002/mp.17514","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Radiation therapy often requires the accumulation of doses from multiple treatment fractions or courses for plan evaluation and treatment response assessment. However, due to underlying mass changes, the accumulated dose may not accurately reflect the total deposited energy, leading to potential inaccuracies in characterizing the treatment input.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study introduces an energy-conserving dose summation method to calculate the total dose in scenarios where patients experience changes in body mass during treatment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and materials</h3>\u0000 \u0000 <p>The proposed method transfers dose and mass data from dosimetry images, where the delivered doses were calculated, to a reference image using an energy and mass-conserving dose reconstruction technique. The reconstructed dose assumes the same resolution and dimension as the reference image. The transferred masses are averaged at each image voxel in the reference image to generate an average mass. The transferred doses are then adjusted by multiplying by the ratio of their transferred mass to the average mass, and subsequently summed to calculate a mass-weighted (MW) total dose at each voxel. This method is demonstrated with a case of lung cancer retreatment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The MW total dose was shown to be equivalent to the total deposited energy divided by the average mass. In the lung cancer retreatment case, the energy derived from the MW total dose was consistent with the sum of energy transferred from two treatments across all evaluated organs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The MW dose summation method can produce a total dose that accurately reflects the total energy deposited in each organ. The consistency may provide a robust foundation for verifying dose accumulations in adaptive radiotherapy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"1305-1310"},"PeriodicalIF":3.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640301","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":"Efficient labeling for fine-tuning chest X-ray bone-suppression networks for pediatric patients","authors":"Weijie Xie,&nbsp;Mengkun Gan,&nbsp;Xiaocong Tan,&nbsp;Mujiao Li,&nbsp;Wei Yang,&nbsp;Wenhui Wang","doi":"10.1002/mp.17516","DOIUrl":"10.1002/mp.17516","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;Pneumonia, a major infectious cause of morbidity and mortality among children worldwide, is typically diagnosed using low-dose pediatric chest X-ray [CXR (chest radiography)]. In pediatric CXR images, bone occlusion leads to a risk of missed diagnosis. Deep learning–based bone-suppression networks relying on training data have enabled considerable progress to be achieved in bone suppression in adult CXR images; however, these networks have poor generalizability to pediatric CXR images because of the lack of labeled pediatric CXR images (i.e., bone images vs. soft-tissue images). Dual-energy subtraction imaging approaches are capable of producing labeled adult CXR images; however, their application is limited because they require specialized equipment, and they are infrequently employed in pediatric settings. Traditional image processing–based models can be used to label pediatric CXR images, but they are semiautomatic and have suboptimal performance.&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 developed an efficient labeling approach for fine-tuning pediatric CXR bone-suppression networks capable of automatically suppressing bone structures in CXR images for pediatric patients without the need for specialized equipment and technologist training.&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;Three steps were employed to label pediatric CXR images and fine-tune pediatric bone-suppression networks: distance transform–based bone-edge detection, traditional image processing–based bone suppression, and fully automated pediatric bone suppression. In distance transform–based bone-edge detection, bone edges were automatically detected by predicting bone-edge distance-transform images, which were then used as inputs in traditional image processing. In this processing, pediatric CXR images were labeled by obtaining bone images through a series of traditional image processing techniques. Finally, the pediatric bone-suppression network was fine-tuned using the labeled pediatric CXR images. This network was initially pretrained on a public adult dataset comprising 240 adult CXR images (A240) and then fine-tuned and validated on 40 pediatric CXR images (P260_40labeled) from our customized dataset (named P260) through five-fold cross-validation; finally, the network was tested on 220 pediatric CXR images (P260_220unlabeled dataset).&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 distance transform–based bone-edge detection network achieved a mean boundary distance of 1.029. Moreover, the traditional image processing–","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 2","pages":"978-992"},"PeriodicalIF":3.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640260","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":"AAPM task group report 314: Fault recovery in external beam radiation therapy","authors":"Steven Sutlief,&nbsp;Courtney Buckey,&nbsp;Geoffrey Ibbott,&nbsp;Scott Hadley,&nbsp;Bruce Curran,&nbsp;Ramon Alfredo Siochi,&nbsp;Sugata Tripathi,&nbsp;David Perrin,&nbsp;Cheryl Young,&nbsp;Cristina Negrut,&nbsp;Ian Robertson,&nbsp;Susan Koehl,&nbsp;Jie Shi","doi":"10.1002/mp.17502","DOIUrl":"10.1002/mp.17502","url":null,"abstract":"<p>Task Group (TG) 314 of the American Association of Physicists in Medicine (AAPM) was charged to develop guidance for recovering from fault states in radiation therapy, specifically regarding the delivery of photon or electron beams using a linear accelerator (linac) including ancillary systems. The fault conditions addressed may involve software, hardware, or a combination of causes. The report provides detailed recommendations for the proactive steps to be taken before a fault, the actions to be taken at the time of a fault, and the safety steps before returning a linac to clinical service, as well as the activities that device manufacturers and standard organizations can do to prevent and resolve the faults. A user-maintained log of prior faults; establishment of remote access by the vendor; and user training in emergency gantry, couch, and door motions are all useful proactive steps. At the moment of downtime and after ensuring the safety of the patient, the report stresses the importance of capturing fault information, prompt contact with the service engineer after the initial assessment, and considerations for communicating the estimated duration before the linac is returned to service. The medical physicist has a critical responsibility to assess the impact of the fault on patient care. Before resuming clinical use, the medical physicist must both determine the level of testing required to ensure safe operation of the linac and ensure any partially or totally delivered treatments have been correctly saved for accurate completion of the treatment fraction. The report stresses the roles of the radiation therapist, medical physicist, and service engineer to efficiently and safely address linac downtime. The appendices contain a description of the efforts of several organizations regarding linac safety: Integrating the Healthcare Enterprise—Radiation Oncology, International Standards Organization/International Electrotechnical Commission, Radiation Oncology Safety Stakeholder Initiative, and the AAPM Vendor Relations and Product Usability Subcommittee.</p><p>Disclaimer: The recommendations of this TG should not be used to establish regulations. These recommendations are guidelines for Qualified Medical Physicists and others to use and appropriately interpret for their institution and clinical setting. Each institution may have site-specific or state-mandated needs and requirements which may modify their usage of these recommendations.</p>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 1","pages":"21-44"},"PeriodicalIF":3.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640300","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":"Optimizing spot-scanning proton arc therapy with a novel spot sparsity approach","authors":"Qingkun Fan,&nbsp;Lewei Zhao,&nbsp;Xiaoqiang Li,&nbsp;Yujia Qian,&nbsp;Riao Dao,&nbsp;Jie Hu,&nbsp;Sheng Zhang,&nbsp;Kunyu Yang,&nbsp;Xiliang Lu,&nbsp;Zhijian Yang,&nbsp;Xuanfeng Ding,&nbsp;Shuyang Dai,&nbsp;Gang Liu","doi":"10.1002/mp.17517","DOIUrl":"10.1002/mp.17517","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;One of the main challenges of utilizing spot-scanning proton arc therapy (SPArc) in routine clinics is treatment delivery efficiency. Spot reduction, which relies on spot sparsity optimization (SSO), is crucial for achieving high delivery efficiency in SPArc.&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 develop a novel SSO approach based on the alternating directions method of multipliers (ADMM) for SPArc to achieve high treatment delivery efficiency and maintain optimal dosimetric plan quality.&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;In this study, SSO for SPArc is based on the least-square dose fidelity term with L0-norm regularization. The novel optimization approach is based on the ADMM framework, in which the minimum monitor unit constraint was considered to improve the plan quality. A state-of-the-art SSO method, the primal-dual active set with continuation (PDASC) algorithm published previously, was utilized as a benchmark. Two SPArc plan groups with the same beam assignment and clinical constraint were generated, in which the former group was SPArc plan with SSO utilizing ADMM, denoted as &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;SPArc&lt;/mtext&gt;\u0000 &lt;mtext&gt;ADMM&lt;/mtext&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{SPArc}_{text{ADMM}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and the later group was SPArc with SSO utilizing PDASC, denoted as &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;SPArc&lt;/mtext&gt;\u0000 &lt;mtext&gt;PDASC&lt;/mtext&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{SPArc}_{text{PDASC}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. Nine clinical cases included five different cancer sites (brain, lung, liver, prostate, and head&amp;neck cancer) were used. The SSO method's performance was evaluated in terms of spot sparsity level (the number of zero-valued elements divided by the total number of elements), beam delivery time, dosimetric plan quality, and plan robustness.&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 the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;SPArc&lt;/mtext&gt;\u0000 &lt;mtext&gt;PDASC&lt;/mtext&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{SPArc}_{text{PDASC}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; plan, the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1789-1797"},"PeriodicalIF":3.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640281","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}