Medical physics最新文献

{"title":"Rapid CNN-based needle localization for automatic slice alignment in MR-guided interventions using 3D undersampled radial white-marker imaging","authors":"Jonas Frederik Faust, Axel Joachim Krafft, Daniel Polak, Peter Speier, Nicolas Gerhard Roland Behl, Nathan Ooms, Jesse Roll, Joshua Krieger, Mark Edward Ladd, Florian Maier","doi":"10.1002/mp.17376","DOIUrl":"10.1002/mp.17376","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In MR-guided in-bore percutaneous needle interventions, typically 2D interactive real-time imaging is used for navigating the needle into the target. Misaligned 2D imaging planes can result in losing visibility of the needle in the 2D images, which impedes successful targeting. Necessary iterative manual slice adjustment can prolong interventional workflows. Therefore, rapid automatic alignment of the imaging planes with the needle would be preferable to improve such workflows.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To investigate rapid 3D localization of needles in MR-guided interventions via a convolutional neural network (CNN)-based localization algorithm using an undersampled white-marker contrast acquisition for the purpose of automatic imaging slice alignment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A radial 3D rf-spoiled gradient echo MR pulse sequence with white-marker encoding was implemented and a CNN-based localization algorithm was employed to extract position and orientation of an aspiration needle from the undersampled white-marker images. The CNN was trained using porcine tissue phantoms (257 needle trajectories, four-fold data augmentation, 90%/10% split into training and validation dataset). Achievable localization times and accuracy were evaluated retrospectively in an ex vivo study (109 needle trajectories) for a range of needle orientations between 78° and 90° relative to the B<sub>0</sub> field. A proof-of-concept in vivo experiment was performed in two porcine animal models and feasibility of automatic imaging slice alignment was evaluated retrospectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Ex vivo needle localization was achieved with a median localization accuracy of 1.9 mm (distance needle tip to detected needle axis) and a median angular deviation of 2.6° for needle orientations between 86° and 90° to the B<sub>0</sub> field from fully sampled WM images (resolution of (4 mm)<sup>3</sup>, 6434 acquired radial k-space spokes, acquisition time of 80.4 s) in a field-of-view of (256 mm)<sup>3</sup>. Localization accuracy decreased with increasing undersampling and needle trajectory increasingly aligned with B<sub>0</sub>. For needle orientations between 86° and 90° to the B<sub>0</sub> field, a highly accelerated acquisition of only 32 k-space spokes (acquisition time of 0.4 s) yielded a median localization accuracy of 3.1 mm and a median angular deviation of 4.7°. For needle orientations between 78° and 82° to the B<sub>0</sub> field, a median accuracy and angular","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8018-8033"},"PeriodicalIF":3.2,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251217","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":"Optimization of reconstruction in quantitative brain PET images: Benefits from PSF modeling and correction of edge artifacts","authors":"Emilie Verrecchia‐Ramos, Merwan Ginet, Olivier Morel, Marc Engels‐Deutsch, Sinan Ben Mahmoud, Paul Retif","doi":"10.1002/mp.17419","DOIUrl":"https://doi.org/10.1002/mp.17419","url":null,"abstract":"BackgroundModern PET reconstruction algorithms incorporate point‐spread‐function (PSF) correction to mitigate partial volume effect. However, PSF correction can introduce edge artifacts that lead to quantification errors. Consequently, current international guidelines advise against using PSF correction in brain PET reconstruction.PurposeWe aimed to investigate PSF‐induced quantification errors in recent digital PET systems and identify conditions that mitigate them. This study utilized brain PET imaging with alginate‐based realistic phantoms, simulating lesion‐to‐background activity ratios of 10:1 and 2:1, with eleven reconstruction parameter sets.MethodsPhantoms were prepared using a commercial anthropomorphic head phantom and two homemade inserts. Each insert contained a homogeneous <jats:sup>18</jats:sup>F‐FDG alginate background with hot spheres of varying diameter (3, 4, 6, 8, 10, 12, and 15 mm). PET imaging was conducted on a digital PET‐CT system Biograph Vision 600 (Siemens), with a 10 min scan duration. Imaging was performed with and without PSF correction, with 2, 4, 6, 12, 18, or 24 iterations in reconstruction, and with or without additional Gaussian postfiltering. We assessed the recovery coefficient (RC), contrast recovery coefficient (CRC), variability, and CRC‐to‐variability ratios for each sphere size and reconstruction parameter set.ResultsPSF‐corrected images of the 10:1 spheres exhibited a nonmonotonic CRC‐to‐sphere diameter relationship due to edge artifacts overshoot in the 10 mm‐diameter sphere. In contrast, PSF images of the 2:1 spheres showed a monotonically increasing relationship. Non‐PSF images of both phantoms showed an expected monotonically increasing CRC‐to‐sphere diameter relationship but with lower CRC values compared to PSF images. The nonmonotonic relationship observed with 10:1 spheres was mitigated by applying a 3‐mm FWHM Gaussian postfiltering. For both phantoms, reconstructions with 6 iterations, PSF correction, and additional 3‐mm FWHM Gaussian postfiltering demonstrated the highest CRC‐to‐variability ratios.ConclusionsOur findings indicate that Gaussian postfiltering suppresses PSF artifacts. This parameter set corrected the nonmonotonic CRC‐to‐sphere diameter relationship and improved the CRC‐to‐variability ratio compared to non‐PSF reconstructions. Therefore, to enhance lesion detectability without compromising quantification accuracy, PSF correction coupled with Gaussian postfiltering should be used in brain PET.","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251220","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":"Deep learning-based prediction of the dose–volume histograms for volumetric modulated arc therapy of left-sided breast cancer","authors":"Akseli Leino,&nbsp;Janne Heikkilä,&nbsp;Tuomas Virén,&nbsp;Juuso T. J. Honkanen,&nbsp;Jan Seppälä,&nbsp;Henri Korkalainen","doi":"10.1002/mp.17410","DOIUrl":"10.1002/mp.17410","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 advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose–volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient-specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning.&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 aimed to develop a deep learning-based framework for the prediction of organ-specific dose–volume histograms (DVH) based on structures delineated for left-sided breast cancer treatment.&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 a dataset of 249 left-sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice-by-slice DVHs for planning target volume (PTV) and organs-at-risk. The patients were divided into training (70%, &lt;i&gt;n &lt;/i&gt;= 174), validation (10%, &lt;i&gt;n &lt;/i&gt;= 24), and test (20%, &lt;i&gt;n &lt;/i&gt;= 51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network.&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 the independent test set (&lt;i&gt;n&lt;/i&gt; = 51), the point-wise differences in the slice-by-slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10&lt;sup&gt;−4&lt;/sup&gt;] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference ± standard deviation of mean doses between the clinical and the predicted DVH were 0.08 ± 0.04 Gy, 0.24 ± 0.22 Gy, 0.73 ± 0.46 Gy, 0.07 ± 0.06 Gy, and 0.14 ± 0.14 Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"7986-7997"},"PeriodicalIF":3.2,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251221","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":"Biophysical modeling of low‐energy ion irradiations with NanOx","authors":"Mario Alcocer‐Ávila, Victor Levrague, Rachel Delorme, Étienne Testa, Michaël Beuve","doi":"10.1002/mp.17407","DOIUrl":"https://doi.org/10.1002/mp.17407","url":null,"abstract":"BackgroundTargeted radiotherapies with low‐energy ions show interesting possibilities for the selective irradiation of tumor cells, a strategy particularly appropriate for the treatment of disseminated cancer. Two promising examples are boron neutron capture therapy (BNCT) and targeted radionuclide therapy with ‐particle emitters (TAT). The successful clinical translation of these radiotherapies requires the implementation of accurate radiation dosimetry approaches able to take into account the impact on treatments of the biological effectiveness of ions and the heterogeneity in the therapeutic agent distribution inside the tumor cells. To this end, biophysical models can be applied to translate the interactions of radiations with matter into biological endpoints, such as cell survival.PurposeThe NanOx model was initially developed for predicting the cell survival fractions resulting from irradiations with the high‐energy ion beams encountered in hadrontherapy. We present in this work a new implementation of the model that extends its application to irradiations with low‐energy ions, as the ones found in TAT and BNCT.MethodsThe NanOx model was adapted to consider the energy loss of primary ions within the sensitive volume (i.e., the cell nucleus). Additional assumptions were introduced to simplify the practical implementation of the model and reduce computation time. In particular, for low‐energy ions the narrow‐track approximation allowed to neglect the energy deposited by secondary electrons outside the sensitive volume, increasing significantly the performance of simulations. Calculations were performed to compare the original hadrontherapy implementation of the NanOx model with the present one in terms of the inactivation cross sections of human salivary gland cells as a function of the kinetic energy of incident ‐particles.ResultsThe predictions of the previous and current versions of NanOx agreed for incident energies higher than 1 MeV/n. For lower energies, the new NanOx implementation predicted a decrease in the inactivation cross sections that depended on the length of the sensitive volume.ConclusionsWe reported in this work an extension of the NanOx biophysical model to consider irradiations with low‐energy ions, such as the ones found in TAT and BNCT. The excellent agreement observed at intermediate and high energies between the original hadrontherapy implementation and the present one showed that NanOx offers a consistent, self‐integrated framework for describing the biological effects induced by ion irradiations. Future work will focus on the application of the latest version of NanOx to cases closer to the clinical setting.","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"169 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251219","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":"Existence, uniqueness, and efficiency of numerically unbiased attenuation pathlength estimators for photon counting detectors at low count rates","authors":"Scott S. Hsieh, Paurakh L. Rajbhandary","doi":"10.1002/mp.17406","DOIUrl":"https://doi.org/10.1002/mp.17406","url":null,"abstract":"BackgroundThe first step in computed tomography (CT) reconstruction is to estimate attenuation pathlength. Usually, this is done with a logarithm transformation, which is the direct solution to the Beer‐Lambert Law. At low signals, however, the logarithm estimator is biased. Bias arises both from the curvature of the logarithm and from the possibility of detecting zero counts, so a data substitution strategy may be employed to avoid the singularity of the logarithm. Recent progress has been made by Li et al. [&lt;jats:italic&gt;IEEE Trans Med Img&lt;/jats:italic&gt; 42:6, 2023] to modify the logarithm estimator to eliminate curvature bias, but the optimal strategy for mitigating bias from the singularity remains unknown.PurposeThe purpose of this study was to use numerical techniques to construct unbiased attenuation pathlength estimators that are alternatives to the logarithm estimator, and to study the uniqueness and optimality of possible solutions, assuming a photon counting detector.MethodsFormally, an attenuation pathlength estimator is a mapping from integer detector counts to real pathlength values. We constrain our focus to only the small signal inputs that are problematic for the logarithm estimator, which we define as inputs of &lt;100 counts, and we consider estimators that use only a single input and that are not informed by adjacent measurements (e.g., adaptive smoothing). The set of all possible pathlength estimators can then be represented as points in a 100‐dimensional vector space. Within this vector space, we use optimization to select the estimator that (1) minimizes mean squared error and (2) is unbiased. We define “unbiased” as satisfying the numerical condition that the maximum bias be less than 0.001 across a continuum of 1000 object thicknesses that span the desired operating range. Because the objective function is convex and the constraints are affine, optimization is tractable and guaranteed to converge to the global minimum. We further examine the nullspace of the constraint matrix to understand the uniqueness of possible solutions, and we compare the results to the Cramér‐Rao bound of the variance.ResultsWe first show that an unbiased attenuation pathlength estimator does not exist if very low mean detector signals (equivalently, very thick objects) are permitted. It is necessary to select a minimum mean detector signal for which unbiased behavior is desired. If we select two counts, the optimal estimator is similar to Li's estimator. If we select one count, the optimal estimator becomes non‐monotonic. The oscillations cause the unbiased estimator to be noise amplifying. The nullspace of the constraint matrix is high‐dimensional, so that unbiased solutions are not unique. The Cramér‐Rao bound of the variance matches well with the expected scaling law and cannot be attained.ConclusionIf arbitrarily thick objects are permitted, an unbiased attenuation pathlength estimator does not exist. If the maximum thickness is restricted, an ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"63 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250884","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":"Benchmarking deep learning‐based low‐dose CT image denoising algorithms","authors":"Elias Eulig, Björn Ommer, Marc Kachelrieß","doi":"10.1002/mp.17379","DOIUrl":"https://doi.org/10.1002/mp.17379","url":null,"abstract":"BackgroundLong‐lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reduction algorithms.PurposeRecently, deep learning‐based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reported results.MethodsIn this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state‐of‐the‐art methods using this standardized setup.ResultsOur evaluation reveals that most deep learning‐based methods show statistically similar performance, and improvements over the past years have been marginal at best.ConclusionsThis study highlights the need for a more rigorous and fair evaluation of novel deep learning‐based methods for low‐dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate their algorithms.","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"14 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250893","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":"Structural tensor and frequency guided semi‐supervised segmentation for medical images","authors":"Xuesong Leng, Xiaxia Wang, Wenbo Yue, Jianxiu Jin, Guoping Xu","doi":"10.1002/mp.17399","DOIUrl":"https://doi.org/10.1002/mp.17399","url":null,"abstract":"BackgroundThe method of semi‐supervised semantic segmentation entails training with a limited number of labeled samples alongside many unlabeled samples, aiming to reduce dependence on pixel‐level annotations. Most semi‐supervised semantic segmentation methods primarily focus on sample augmentation in spatial dimensions to reduce the shortage of labeled samples. These methods tend to ignore the structural information of objects. In addition, frequency‐domain information also supplies another perspective to evaluate information from images, which includes different properties compared to the spatial domain.PurposeIn this study, we attempt to answer these two questions: (1) is it helpful to provide structural information of objects in semi‐supervised semantic segmentation tasks for medical images? (2) is it more effective to evaluate the segmentation performance in the frequency domain compared to the spatial domain for semi‐supervised medical image segmentation? Therefore, we seek to introduce structural and frequency information to improve the performance of semi‐supervised semantic segmentation for medical images.MethodsWe present a novel structural tensor loss (STL) to guide feature learning on the spatial domain for semi‐supervised semantic segmentation. Specifically, STL utilizes the structural information encoded in the tensors to enforce the consistency of objects across spatial regions, thereby promoting more robust and accurate feature extraction. Additionally, we proposed a frequency‐domain alignment loss (FAL) to enable the neural networks to learn frequency‐domain information across different augmented samples. It leverages the inherent patterns present in frequency‐domain representations to guide the network in capturing and aligning features across diverse augmentation variations, thereby enhancing the model's robustness for the inputting variations.ResultsWe conduct our experiments on three benchmark datasets, which include MRI (ACDC) for cardiac, CT (Synapse) for abdomen organs, and ultrasound image (BUSI) for breast lesion segmentation. The experimental results demonstrate that our method outperforms state‐of‐the‐art semi‐supervised approaches regarding the Dice similarity coefficient.ConclusionsWe find the proposed approach could improve the final performance of the semi‐supervised medical image segmentation task. It will help reduce the need for medical image labels. Our code will are available at <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://github.com/apple1986/STLFAL\">https://github.com/apple1986/STLFAL</jats:ext-link>.","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"65 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250886","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":"Reducing treatment time for robotic radiosurgery by considering path efficiency during node selection","authors":"Theodor Hagström,&nbsp;Björn Andersson,&nbsp;Albin Fredriksson","doi":"10.1002/mp.17380","DOIUrl":"10.1002/mp.17380","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Robotic radiosurgery treatments allow for precise non-coplanar beam delivery by utilizing a robot equipped with a linac that traverses through a set of predetermined nodes. High quality treatment plans can be produced but treatment times can grow large, with one substantial component being the robot traversal time.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The aim of this study is to reduce the treatment time for robotic radiosurgery treatments by introducing algorithms for reducing the robot traversal time. The algorithms are integrated into a commercial treatment planning system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>First, an optimization framework for robotic radiosurgery planning is detailed, including a heuristic optimization method for node selection. Second, two methods aimed at reducing the traversal time are introduced. One utilizes a centrality measure focusing on the structure of the node network, while the other is based on the direct computation of traversal times during optimization. A comparison between plans with and without the time-reducing algorithms is made for three brain cases and one liver case with basis in treatment time, plan quality, monitor units, and network structure of the selected nodes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Large decreases in traversal times are obtained by the traversal time reducing algorithms, with reductions of up to 49 % in the brain cases and 31 % in the liver case. The resulting reductions in treatment times are up to 30 % and 13 %, respectively. Small differences in plan quality are observed, with similar dose-volume histograms, dose distributions, and conformity/gradient indices.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The total treatment time of the robotic radiosurgery treatments can be reduced by selecting nodes with more efficient robot traversal paths, while maintaining plan quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8010-8017"},"PeriodicalIF":3.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250885","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":"Assessment of thermochromic phantoms for characterizing microwave ablation devices","authors":"Ghina Zia,&nbsp;Amber Lintz,&nbsp;Clay Hardin,&nbsp;Anna Bottiglieri,&nbsp;Jan Sebek,&nbsp;Punit Prakash","doi":"10.1002/mp.17404","DOIUrl":"10.1002/mp.17404","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background and purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Thermochromic gel phantoms provide a controlled medium for visual assessment of thermal ablation device performance. However, there are limited studies reporting on the comparative assessment of ablation profiles assessed in thermochromic gel phantoms against those in ex vivo tissue. The objective of this study was to compare microwave ablation zones in a thermochromic tissue-mimicking gel phantom and ex vivo bovine liver and to report on measurements of the temperature-dependent dielectric and thermal properties of the phantom.&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;Thermochromic polyacrylamide phantoms were fabricated following a previously reported protocol. Phantom samples were heated to temperatures in the range of 20°C–90°C in a temperature-controlled water bath, and colorimetric analysis of images of the phantom taken after heating was used to develop a calibration between color changes and the temperature to which the phantom was heated. Using a custom, 2.45 GHz water-cooled microwave ablation antenna, ablations were performed in fresh ex vivo liver and phantoms using 65 W applied for 5 min or 10 min (&lt;i&gt;n = &lt;/i&gt;3 samples in each medium for each power/time combination). Broadband (500 MHz–6 GHz) temperature-dependent dielectric and thermal properties of the phantom were measured over the temperature range of 22°C–100°C.&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;Colorimetric analysis showed that the sharp change in gel phantom color commences at a temperature of 57°C. Short and long axes of the ablation zone in the phantom (as assessed by the 57°C isotherm) for 65 W, 5 min ablations were aligned with the extents of the ablation zone observed in ex vivo bovine liver. However, for the 65 W, 10 min setting, ablations in the phantom were on average 23.7% smaller in the short axis and 7.4 % smaller in the long axis than those observed in ex vivo liver. Measurements of the temperature-dependent relative permittivity, thermal conductivity, and volumetric heat capacity of the phantom largely followed similar trends to published values for ex vivo liver tissue.&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;Thermochromic tissue-mimicking phantoms provides a controlled, and reproducible medium for comparative assessment of microwave ablation devices and energy delivery settings. However, ablation zone size and shapes in the thermochromic phantom do not accurately represent ablation sizes and shapes observed in ex vivo liver tissue for high energy delivery treatments (65 W, 10 min). One cause for this limitation is the difference in temperature-dependent thermal and diel","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8442-8453"},"PeriodicalIF":3.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268551","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":"Cover","authors":"","doi":"10.1002/mp.15753","DOIUrl":"https://doi.org/10.1002/mp.15753","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 9","pages":"C1"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.15753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234676","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}