Physics in medicine and biology最新文献

{"title":"Comment on 'CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images'.","authors":"Norbert Żołek, Anna Pawłowska","doi":"10.1088/1361-6560/ada7bc","DOIUrl":"https://doi.org/10.1088/1361-6560/ada7bc","url":null,"abstract":"<p><p>An analysis of the methodology used by the authors of the commented article is presented and errors related to data preparation are pointed out.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 3","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143033628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to Comment on 'CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images'.","authors":"Md Kamrul Hasan, Jarin Tasnim","doi":"10.1088/1361-6560/ada7bf","DOIUrl":"https://doi.org/10.1088/1361-6560/ada7bf","url":null,"abstract":"","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 3","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143033631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monte Carlo in the mechanistic modelling of the FLASH effect: a review.","authors":"Gavin Pikes, Joshua Dass, Suki Gill, Martin Ebert, Mark Reynolds, Pejman Rowshanfarzad","doi":"10.1088/1361-6560/ada51a","DOIUrl":"10.1088/1361-6560/ada51a","url":null,"abstract":"<p><p>FLASH radiotherapy employs ultra-high dose rates of>40Gy s^-1, which may reduce normal tissue complication as compared to conventional dose rate treatments, while still ensuring the same level of tumour control. The potential benefit this can offer to patients has been the cause of great interest within the radiation oncology community, but this has not translated to a direct understanding of the FLASH effect. The oxygen depletion and inter-track interaction hypotheses are currently the leading explanations as to the mechanisms behind FLASH, but these are still not well understood, with many questions remaining about the exact underpinnings of FLASH and the treatment parameters required to optimally induce it. Monte Carlo simulations may hold the key to unlocking the mystery behind FLASH, allowing for analysis of the underpinning mechanisms at a fundamental level, where the interactions between individual radiation particles, DNA strands and chemical species can be studied. Currently, however, there is still a great deal of disagreement in simulation findings and the importance of the different mechanisms they support. This review discusses current studies into the mechanisms of FLASH using the Monte Carlo method. The simulation parameters and results for all major investigations are provided. Discussion primarily revolves around the oxygen depletion and inter-track interactions hypotheses, though other, more novel, theories are also mentioned. A general list of recommendations for future simulations is provided, informed by the articles discussed. This review highlights some of the useful parameters and simulation methodologies that may be required to finally understand the FLASH effect.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142922754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep proximal gradient network for absorption coefficient recovery in photoacoustic tomography.","authors":"Sun Zheng, Geng Ranran","doi":"10.1088/1361-6560/ada868","DOIUrl":"10.1088/1361-6560/ada868","url":null,"abstract":"<p><p><i>Objective.</i>The optical absorption properties of biological tissues in photoacoustic (PA) tomography are typically quantified by inverting acoustic measurements. Conventional approaches to solving the inverse problem of forward optical models often involve iterative optimization. However, these methods are hindered by several challenges, including high computational demands, the need for regularization, and sensitivity to both the accuracy of the forward model and the completeness of the measurement data. The aim of this study is to introduce a novel learned iterative method for recovering spatially varying optical absorption coefficients (OACs) from PA pressure measurements.<i>Approach.</i>The study introduces a deep learning-based approach that employs the proximal gradient descent mechanism to achieve optical inversion. The proposed framework consists of multiple cascaded structural units, which iteratively update the absorption coefficients through a learning process, unit by unit.<i>Main results.</i>The proposed method was validated through simulations, phantom experiments, and<i>in vivo</i>studies. Comparative analyses demonstrated that the proposed approach outperforms traditional nonlearning and learning-based methods, achieving at least 12.85% improvement in relative errors, 3.50% improvement in peak signal-to-noise ratios, and 3.53% improvement in structural similarity in reconstructing the OAC distribution.<i>Significance.</i>This method significantly improves the accuracy and efficiency of quantitative PA tomography. By addressing key challenges such as computational demand and sensitivity to the accuracy of the forward model and the completeness of the measurement data, the proposed framework offers a more reliable and efficient alternative to traditional methods, with potential applications in medical imaging and diagnostics.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142952955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anℓ1-plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset.","authors":"Vladyslav Gapyak, Corinna Erika Rentschler, Thomas März, Andreas Weinmann","doi":"10.1088/1361-6560/ada5a1","DOIUrl":"10.1088/1361-6560/ada5a1","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic particle imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a Plug-and-Play approach based on a generic zero-shot denoiser with anℓ1-prior.<i>Approach.</i>We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, ART, DIP and the previous PP-MPI, which is a Plug-and-Play method with denoiser trained on MPI-friendly data.<i>Main results.</i>We derive a Plug-and-Play reconstruction method based on a generic zero-shot denoiser. Addressing (hyper)parameter selection, we perform an extended parameter search on a hybrid validation dataset we produced and apply the derived parameters for reconstruction on the 3D Open MPI Dataset. We offer a quantitative and qualitative evaluation of the zero-shot Plug-and-Play approach on the 3D Open MPI dataset with the validated parameters. Moreover, we show the quality of the approach with different levels of preprocessing of the data.<i>Significance.</i>The proposed method employs a zero-shot denoiser which has not been trained for the MPI reconstruction task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IPEM code of practice for proton therapy dosimetry based on the NPL primary standard proton calorimeter calibration service.","authors":"Stuart Green, Ana Lourenço, Hugo Palmans, Nigel Lee, Richard A Amos, Derek D'Souza, Francesca Fiorini, Frank Van den Heuvel, Andrzej Kacperek, Ranald I Mackay, John Pettingell, Russell A S Thomas","doi":"10.1088/1361-6560/adad2e","DOIUrl":"https://doi.org/10.1088/1361-6560/adad2e","url":null,"abstract":"<p><p>Internationally, reference dosimetry for clinical proton beams largely follows the guidelines published by the International Atomic Energy Agency (IAEA TRS-398 Rev. 1, 2024). This approach yields a relative standard uncertainty of 1.7% (k=1) on the absorbed dose to water determined under reference conditions. The new IPEM code of practice presented here, enables the relative standard uncertainty on the absorbed dose to water measured under reference conditions to be reduced to 1.0% (k=1). This improvement is based on the absorbed dose to water calibration service for proton beams provided by the National Physical Laboratory (NPL), the UK's primary standards laboratory. This significantly reduced uncertainty is achieved through the use of a primary standard level graphite calorimeter to derive absorbed dose to water directly in the clinical department's beam. This eliminates the need for beam quality correction factors (k_(Q,Q_0 )) as required by the IAEA TRS-398 approach. The portable primary standard level graphite calorimeter, developed over a number of years at the NPL, is sufficiently robust to be useable in the proton beams of clinical facilities both in the UK and overseas.&#xD;The new code of practice involves performing reference dosimetry measurements directly traceable to the primary standard level graphite calorimeter in a clinical proton beam. Calibration of an ionisation chamber is performed in the centre of a standard test volume (STV) of dose, defined here to be a 10 x 10 x 10 cm volume in water, centred at a depth of 15 cm. Further STVs at reduced and increased depths are also utilised. The designated ionisation chambers are Roos-type plane-parallel chambers.&#xD;This article provides all the necessary background material, formalism, and specifications of reference conditions required to implement reference dosimetry according to this new code of practice. The Annexes provide a detailed review of ion recombination and how this should be assessed (Annex A1) and detailed work instructions for creating and delivering the standard test volumes (Annex A2).&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143023993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time CBCT imaging and motion tracking via a single arbitrarily-angled x-ray projection by a joint dynamic reconstruction and motion estimation (DREME) framework.","authors":"Hua-Chieh Shao, Tielige Mengke, Tinsu Pan, You Zhang","doi":"10.1088/1361-6560/ada519","DOIUrl":"10.1088/1361-6560/ada519","url":null,"abstract":"<p><p><i>Objective.</i>Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a framework<u>d</u>ynamic<u>re</u>construction and<u>m</u>otion<u>e</u>stimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.<i>Approach.</i>DREME incorporates a deep learning-based real-time CBCT imaging and motion estimation method into a dynamic CBCT reconstruction framework. The reconstruction framework reconstructs a dynamic sequence of CBCTs in a data-driven manner from a standard pre-treatment scan, without requiring patient-specific prior knowledge. Meanwhile, a convolutional neural network-based motion encoder is jointly trained during the reconstruction to learn motion-related features relevant for real-time motion estimation, based on a single arbitrarily-angled x-ray projection. DREME was tested on digital phantom simulations and real patient studies.<i>Main Results.</i>DREME accurately solved 3D respiration-induced anatomical motion in real time (∼1.5 ms inference time for each x-ray projection). For the digital phantom studies, it achieved an average lung tumor center-of-mass localization error of 1.2 ± 0.9 mm (Mean ± SD). For the patient studies, it achieved a real-time tumor localization accuracy of 1.6 ± 1.6 mm in the projection domain.<i>Significance.</i>DREME achieves CBCT and volumetric motion estimation in real time from a single x-ray projection at arbitrary angles, paving the way for future clinical applications in intra-fractional motion management. In addition, it can be used for dose tracking and treatment assessment, when combined with real-time dose calculation.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142922756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust stochastic optimisation strategies for locoregional hyperthermia treatment planning using polynomial chaos expansion.","authors":"Jort A Groen, Timoteo D Herrera, Johannes Crezee, H Petra Kok","doi":"10.1088/1361-6560/ada685","DOIUrl":"https://doi.org/10.1088/1361-6560/ada685","url":null,"abstract":"<p><p><i>Objective.</i>Conventional temperature optimization in hyperthermia treatment planning aims to maximize tumour temperature (e.g.<i>T</i>90; the temperature reached in at least 90% of the tumour) while enforcing hard constraints on normal tissue temperature (max(T_tissue) ⩽45 °C). This method generally incorrectly assumes that tissue/perfusion properties are known, typically relying on average values from the literature. To enhance the reliability of temperature optimization in clinical applications, we developed new robust optimization strategies to reduce the impact of tissue/perfusion property uncertainties.<i>Approach.</i>Within the software package Plan2Heat, temperature calculations during optimization apply efficient superposition of precomputed distributions, represented by a temperature matrix (<i>T</i>-matrix). We extended this method using stochastic polynomial chaos expansion models to compute an average<i>T</i>-matrix (<i>T</i>_avg) and a covariance matrix<i>C</i>to account for uncertainties in tissue/perfusion properties. Three new strategies were implemented using<i>T</i>_avgand<i>C</i>during optimization: (1)<i>T</i>_avg90 maximization, hard constraint on max(<i>T</i>_tissue), (2)<i>T</i>_avg90 maximization, hard constraint on max(<i>T</i>_tissue) variation, and (3) combined<i>T</i>_avg90 maximization and variation minimization, hard constraint on max(<i>T</i>_tissue). Conventional and new optimization strategies were tested in a cervical cancer patient. 100 test cases were generated, randomly sampling tissue-property probability distributions. Tumour<i>T</i>90 and hot spots (max(<i>T</i>_tissue) >45 °C) were evaluated for each sample.<i>Main Results.</i>Conventional optimization had 28 samples without hot spots, with a median<i>T</i>90 of 39.7 °C. For strategies (1), (2) and (3), the number of samples without hot spots was increased to 33, 41 and 36, respectively. Median<i>T</i>90 was reduced lightly, by ∼0.1 °C-0.3 °C, for strategies (1-3). Tissue volumes exceeding 45 °C and variation in max(<i>T</i>_tissue) were less for the novel strategies.<i>Significance.</i>Optimization strategies that account for tissue-property uncertainties demonstrated fewer, and reduced in volume, normal tissue hot spots, with only a marginal reduction in tumour<i>T</i>90. This implies a potential clinical utility in reducing the need for, or the impact of, device setting adjustments during hyperthermia treatment.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum: Research and application of omics and artificial intelligence in cancer (2024<i>Phys. Med. Biol.</i>69 21TR01).","authors":"Ye Zhang, Wenwen Ma, Zhiqiang Huang, Kun Liu, Zhaoyi Feng, Lei Zhang, Dezhi Li, Tianlu Mo, Qing Liu","doi":"10.1088/1361-6560/ada5a2","DOIUrl":"https://doi.org/10.1088/1361-6560/ada5a2","url":null,"abstract":"","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fourier energy spectrum centroid: a robust and efficient approach for shear wave speed estimation in<i>ω</i>-<i>k</i>space.","authors":"Xi Zhang, Jinping Dong, Wei-Ning Lee","doi":"10.1088/1361-6560/ada686","DOIUrl":"https://doi.org/10.1088/1361-6560/ada686","url":null,"abstract":"<p><p><i>Objective.</i>The propagation speed of a shear wave, whether externally or internally induced, in biological tissues is directly linked to the tissue's stiffness. The group shear wave speed (SWS) can be estimated using a class of time-of-flight (TOF) methods in the time-domain or phase speed-based methods in the frequency domain. However, these methods suffer from biased estimations or time-consuming computations, and they are especially prone to wave distortions in<i>in vivo</i>cases. In this work, we present a parameter-free, robust, and efficient group SWS estimation method coined as Fourier energy spectrum centroid (FESC).<i>Approach.</i>The proposed FESC method is based on the center of mass inω-kspace. It was evaluated on data from computer simulations with additive Gaussian noise, a commercial elasticity phantom, an<i>ex vivo</i>pig liver, and<i>in vivo</i>biceps brachii muscles of three young healthy male subjects. The FESC method was compared with two 2D frequency-domain methods: Max-fre, which considers phase SWS at the peak of<i>k</i>-space, and Fre-regre, which applies linear regression of phase SWS within a fixed bandwidth. Two additional benchmarks included time-domain methods based on cross-correlation (X-Corr) and radon sum transformation (RD).<i>Main results.</i>Statistical results showed that our FESC method and the RD method had comparable accuracy and robustness, outperforming the other benchmark methods. In the simulation and phantom studies, when the signal-to-noise ratio was higher than 25 dB, our FESC showed higher accuracy than RD. In the<i>in vivo</i>study, our FESC method had better repeatability than RD. Furthermore, the proposed FESC method was 100 times faster than the runner-up method, X-Corr, and 3,000 times faster than the least efficient method, RD.<i>Significance.</i>All results indicated that our proposed Fourier-based method shows promise in reliably and efficiently providing reference values for group SWS in homogeneous bulk media.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}