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Acoustic holography in biomedical applications.
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-03-03 DOI: 10.1088/1361-6560/adb89a
Rachel Burstow, Diana Andrés, Noé Jiménez, Francisco Camarena, Maya Thanou, Antonios N Pouliopoulos

Acoustic holography can be used to construct an arbitrary wavefront at a desired 2D plane or 3D volume by beam shaping an emitted field and is a relatively new technique in the field of biomedical applications. Acoustic holography was first theorized in 1985 following Gabor's work in creating optical holograms in the 1940s. Recent developments in 3D printing have led to an easier and faster way to manufacture monolithic acoustic holographic lenses that can be attached to single-element transducers. As ultrasound passes through the lens material, a phase shift is applied to the waves, causing an interference pattern at the 2D image plane or 3D volume, which forms the desired pressure field. This technology has many applications already in use and has become of increasing interest for the biomedical community, particularly for treating regions that are notoriously difficult to operate on, such as the brain. Acoustic holograms could provide a non-invasive, precise, and patient specific way to deliver drugs, induce hyperthermia, or create tissue cell patterns. However, there are still limitations in acoustic holography, such as the difficulties in creating 3D holograms and the passivity of monolithic lenses. This review aims to outline the biomedical applications of acoustic holograms reported to date and discuss their current limitations and the future work that is needed for them to reach their full potential in the biomedical community.

声全息技术可通过对发射场进行波束赋形,在所需的二维平面或三维体积上构建所需的波阵面,是生物医学应用领域一项相对较新的技术。继 Gabor 在 20 世纪 40 年代创造出光学全息图之后,声全息技术于 1985 年首次被理论化,而 3D 打印技术的最新发展则为制造单片声全息透镜提供了更简便、更快捷的方法,这种透镜可连接到单元素传感器上。当超声波通过透镜材料时,会对其进行相移,从而在二维图像平面上产生干涉图案,形成所需的压力场。这项技术已经有许多应用,并越来越受到医学界的关注,尤其是在治疗众所周知难以操作的区域(如大脑)方面。声学全息图可以提供一种非侵入性、精确和针对特定病人的方式来给药、诱导热疗、创建组织细胞模式,并对许多其他医学领域有所帮助。然而,声全息影像仍存在一些局限性,如创建三维全息图的困难和单片透镜的被动性。本综述旨在概述迄今为止所报道的声全息图在生物医学方面的应用,并讨论其目前的局限性以及为充分发挥其在生物医学界的潜力而需要开展的未来工作。
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引用次数: 0
Alleviating the trade-off between coincidence time resolution and sensitivity using scalable TOF-DOI detectors.
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-03-03 DOI: 10.1088/1361-6560/adb936
Yuya Onishi, Ryosuke Ota

Objective. Coincidence time resolution (CTR) in time-of-flight positron emission tomography (TOF-PET) has significantly improved with advancements in scintillators, photodetectors, and readout electronics. Achieving a CTR of 100 ps remains challenging due to the need for sufficiently thick scintillators-typically 20 mm-to ensure adequate sensitivity because the photon transit time spread within these thick scintillators impedes achieving 100 ps CTR. Therefore thinner scintillators are preferable for CTR better than 100 ps. To address the trade-off between TOF capability and sensitivity, we propose a readout scheme of PET detectors.Approach. The proposed scheme utilizes two orthogonally stacked one-dimensional PET detectors, enabling the thickness of the scintillators to be reduced to approximately 13 mm without compromising sensitivity. This is achieved by stacking the detectors along the depth-of-interaction (DOI) axis of a PET scanner. We refer to this design as the cross-stacked detector, or xDetector. Furthermore, the xDetector inherently provides DOI information using the same readout scheme.Main results. Experimental evaluations demonstrated that the xDetector achieved the best CTR of 175 ps full width at half maximum (FWHM) and an energy resolution of 11% FWHM at 511 keV with 3 × 3 × 12.8 mm3lutetium oxyorthosilicate crystals, each coupled one-to-one with silicon photomultipliers. The CTRs are between the xDetector and reference detector with a single timing resolution of 111.2 ± 0.8 ps FWHM. In terms ofxy-spatial resolution, the xDetector exhibited an asymmetric resolution due to its readout scheme: one resolution was defined by the 3.2 mm readout pitch, while the other was calculated using the center-of-gravity method.Significance. The xDetector effectively resolves the trade-off between TOF capability and sensitivity while offering scalability and DOI capability. By integrating state-of-the-art scintillators, photodetectors, and readout electronics with the xDetector scheme, achieving a CTR of 100 ps FWHM alongside high DOI resolution becomes a practical possibility.

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引用次数: 0
Evaluation of motion mitigation strategies for carbon ion therapy of abdominal tumors based on non-periodic imaging data.
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-03-03 DOI: 10.1088/1361-6560/adb89b
Timo Steinsberger, Anestis Nakas, Alessandro Vai, Silvia Molinelli, Marco Donetti, Marco Pullia, Maria Chiara Martire, Cosimo Galeone, Mario Ciocca, Andrea Pella, Viviana Vitolo, Amelia Barcelini, Ester Orlandi, Sara Imparato, Lennart Volz, Guido Baroni, Chiara Paganelli, Marco Durante, Christian Graeff

Objective.To identify suitable combination strategies for treatment planning and beam delivery in scanned carbon ion therapy of moving tumors.Approach. Carbon ion treatment plans for five abdominal tumors were optimized on four-dimensional (4D) computed tomography (CT) data using the following approaches. 4DITV across all phases and within a gating window, single phase uniform dose, and an innovative 4D tracking internal target volume (ITV) strategy. Delivered single-fraction doses were calculated on time-resolved virtual CT images reconstructed from 2D cine-magnetic resonance imaging series, using a deformable image registration pipeline. Treatment plans were combined with various beam delivery techniques: three-dimensional (no motion mitigation), rescanning, gating, beam tracking, and multi-phase 4D delivery with and without residual tracking (MP4D and MP4DRT) to form in total 11 treatment modalities. Single fraction doses were accumulated to simulate a fractionated treatment.Main results. Breath-sampled treatments using the MP4D and MP4DRT delivery techniques were the only to achieveD95> 95% for hypofractionated treatments, with little dependence on the number of fractions. A combination of MP4DRT with the new 4D tracking ITV approach resulting in conformal dose distributions and demonstrated the greatest robustness against irregular motion and anatomical changes.Significance. This study demonstrates, that real-time adaptive beam delivery strategies can deliver conformal doses within single fractions, thereby enabling hypofractionated treatment schemes that are not feasible with conventional strategies.

目标:确定移动肿瘤扫描碳离子疗法的治疗计划和射束传输的合适组合策略:为移动肿瘤的扫描碳离子治疗确定合适的治疗计划和射束输送组合策略:使用以下方法对 4DCT 数据中的五个腹部肿瘤的碳离子治疗计划进行优化:在所有阶段和门窗内的 4DITV、单相均匀剂量 (SPUD) 和创新的 4D 跟踪 ITV 策略。使用可变形图像注册管道,在由二维 cine-MRI 系列重建的时间分辨虚拟 CT 图像上计算出单分量的给药剂量。治疗计划与各种射束输送技术相结合:三维(无运动缓解)、重扫描、门控、光束跟踪以及带或不带残余跟踪(MP4D 和 MP4DRT)的多相 4D 传输,共形成 11 种治疗模式。累积单次分次剂量以模拟分次治疗:使用 MP4D 和 MP4DRT 输送技术的呼吸采样治疗是唯一能使低分次治疗的 D95 > 95 % 的方法,而且与分次数量的关系不大。将 MP4DRT 与新的 4D 跟踪 ITV 方法相结合,可实现适形剂量分布,并对不规则运动和解剖变化表现出最大的稳健性:这项研究表明,实时自适应射束传输策略可以在单次分次治疗中提供适形剂量,从而实现传统策略无法实现的低分次治疗方案。
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引用次数: 0
Bowel tracking for MR-guided radiotherapy: simultaneous optimization of small bowel imaging and tracking.
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-02-28 DOI: 10.1088/1361-6560/adbbac
S L C Damen, Astrid L H M W van Lier, Cornel Zachiu, Bas W Raaymakers

Background and purpose The small bowel is one of the most radiosensitive organs-at-risk during radiotherapy in the pelvis. This is further complicated due to anatomical and physiological motion. Thus, its accurate tracking becomes of particular importance during therapy delivery, to obtain better dose-toxicity relations and/or to perform safe adaptive treatments. The aim of this work is to simultaneously optimize the MR imaging sequence and motion estimation solution towards improved small bowel tracking precision during radiotherapy delivery. Materials and methods An MRI sequence was optimized, to adhere to the respiratory and peristaltic motion frequencies, by assesing the performance of an image registration algorithm on data acquired on volunteers and patients. In terms of tracking, three registration algorithms, previously-employed in the scope of image-guided radiotherapy, were investigated. The optimized scan was acquired for 7.5 min., in 18 patients and for 15 min., in 10 volunteers at a 1.5T MRL (Unity, Elekta AB). The tracking precision was evaluated and validated by means of three different quality assurance criteria: Structural Similarity Index Metric (SSIM), Inverse Consistency (IC) and Absolute Intensity Difference (AID). Results The optimal sequence was a balanced Fast Field Echo (FFE), which acquired a 3D volume of the abdomen, with a dynamic scan time of 1.8 seconds. An optical flow algorithm performed best and which was able to resolve most of the motion. This was shown by mean IC values of < 1 mm and a mean SSIM >0.9 for the majority of the cases. A strong positive correlation (p<0.001) between the registration performance and visceral fat percentage was found, where a higher visceral fat percentage gave a better registration due to the better image contrast. Conclusions A method for simultaneous optimization of imaging and tracking was presented, which derived an imaging and registration procedure for accurate small bowel tracking on the MR-Linac.

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引用次数: 0
A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction. 图像协调技术在减轻CT采集和重建差异中的比较分析。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-02-27 DOI: 10.1088/1361-6560/adabad
Anil Yadav, Spencer Welland, John M Hoffman, Grace Hyun J Kim, Matthew S Brown, Ashley E Prosper, Denise R Aberle, Michael F McNitt-Gray, William Hsu

Objective. The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.Approach. A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel). Harmonized scans were evaluated for image similarity using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS), and for the reproducibility of radiomic and deep features using concordance correlation coefficient (CCC).Main Results. CNNs consistently yielded higher image similarity metrics amongst others; for Sharp/10%, which exhibited the poorest visual similarity, PSNR increased from a mean ± CI of 17.763 ± 0.492 to 31.925 ± 0.571, SSIM from 0.219 ± 0.009 to 0.754 ± 0.017, and LPIPS decreased from 0.490 ± 0.005 to 0.275 ± 0.016. Texture-based radiomic features exhibited a greater degree of variability across conditions, i.e. a CCC of 0.500 ± 0.332, compared to intensity-based features (0.972 ± 0.045). GANs achieved the highest CCC (0.969 ± 0.009 for radiomic and 0.841 ± 0.070 for deep features) amongst others. CNNs are suitable if downstream applications necessitate visual interpretation of images, whereas GANs are better alternatives for generating reproducible quantitative image features needed for machine learning applications.Significance. Understanding the efficacy of harmonization in addressing multi-parameter variability is crucial for optimizing diagnostic accuracy and a critical step toward building generalizable models suitable for clinical use.

目的:本研究旨在系统表征CT参数变化对图像及肺放射学和深部特征的影响,并评估不同图像协调方法减轻观察到的变化的能力。方法:通过不同的辐射剂量(100%,25%,10%)和重建核(平滑,中等,锐利)重建100个低剂量胸部CT扫描的回顾性内部sinogram数据集。训练了一组图像处理、卷积神经网络(cnn)和基于生成对抗网络(gan)的方法,以协调所有图像条件到参考条件(100%剂量,中等核)。利用峰值信噪比(PSNR)、结构相似指数(SSIM)和学习感知图像斑块相似度(LPIPS)评估协调扫描图像的相似性,并利用一致性相关系数(CCC)评估放射学和深度特征的再现性。主要结果:cnn在其他图像中始终获得更高的图像相似度指标;视觉相似性最差的Sharp/10%的PSNR从平均±CI(17.763±0.492)上升到31.925±0.571,SSIM从0.219±0.009上升到0.754±0.017,LPIPS从0.490±0.005下降到0.275±0.016。与基于强度的特征(0.972±0.045)相比,基于纹理的放射学特征在不同条件下表现出更大程度的可变性,即CCC为0.500±0.332。其中,gan的CCC最高(放射性特征为0.969±0.009,深层特征为0.841±0.070)。如果下游应用需要图像的视觉解释,那么卷积神经网络是合适的,而生成对抗网络是生成机器学习应用所需的可重复定量图像特征的更好选择。意义:了解协调在解决多参数变异性方面的功效对于优化诊断准确性至关重要,也是构建适合临床使用的可推广模型的关键一步。
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引用次数: 0
DRACO: differentiable reconstruction for arbitrary CBCT orbits.
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-02-27 DOI: 10.1088/1361-6560/adbb50
Chengze Ye, Linda-Sophie Schneider, Yipeng Sun, Mareike Thies, Siyuan Mei, Andreas Maier

Objective: This study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits, addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms.

Approach: The proposed method employs a differentiable shift-variant filtered backprojection neural network, optimized for arbitrary trajectories. By integrating known operators into the learning model, the approach minimizes the number of trainable parameters while enhancing model interpretability. This framework adapts seamlessly to specific orbit geometries, including non-continuous trajectories such as circular-plus-arc or sinusoidal paths, enabling faster and more accurate CBCT reconstructions.

Main results: Experimental validation demonstrates that the method significantly accelerates reconstruction, reducing computation time by over 97% compared to conventional iterative algorithms. It achieves superior or comparable image quality with reduced noise, as evidenced by a 38.6% reduction in mean squared error, a 7.7% increase in peak signal-to-noise ratio, and a 5.0% improvement in the structural similarity index measure. The flexibility and robustness of the approach are confirmed through its ability to handle data from diverse scan geometries.

Significance: This method represents a significant advancement in interventional medical imaging, particularly for robotic C-arm CT systems, enabling real-time, high-quality CBCT reconstructions for customized orbits. It offers a transformative solution for clinical applications requiring computational efficiency and precision in imaging. Code Availability: Code is available at https://github.com/ChengzeYe/Defrise-and-Clack-reconstruction.

{"title":"DRACO: differentiable reconstruction for arbitrary CBCT orbits.","authors":"Chengze Ye, Linda-Sophie Schneider, Yipeng Sun, Mareike Thies, Siyuan Mei, Andreas Maier","doi":"10.1088/1361-6560/adbb50","DOIUrl":"https://doi.org/10.1088/1361-6560/adbb50","url":null,"abstract":"<p><strong>Objective: </strong>This study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits, addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms.</p><p><strong>Approach: </strong>The proposed method employs a differentiable shift-variant filtered backprojection neural network, optimized for arbitrary trajectories. By integrating known operators into the learning model, the approach minimizes the number of trainable parameters while enhancing model interpretability. This framework adapts seamlessly to specific orbit geometries, including non-continuous trajectories such as circular-plus-arc or sinusoidal paths, enabling faster and more accurate CBCT reconstructions.</p><p><strong>Main results: </strong>Experimental validation demonstrates that the method significantly accelerates reconstruction, reducing computation time by over 97% compared to conventional iterative algorithms. It achieves superior or comparable image quality with reduced noise, as evidenced by a 38.6% reduction in mean squared error, a 7.7% increase in peak signal-to-noise ratio, and a 5.0% improvement in the structural similarity index measure. The flexibility and robustness of the approach are confirmed through its ability to handle data from diverse scan geometries.</p><p><strong>Significance: </strong>This method represents a significant advancement in interventional medical imaging, particularly for robotic C-arm CT systems, enabling real-time, high-quality CBCT reconstructions for customized orbits. It offers a transformative solution for clinical applications requiring computational efficiency and precision in imaging. Code Availability: Code is available at https://github.com/ChengzeYe/Defrise-and-Clack-reconstruction.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524137","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}
引用次数: 0
CycleH-CUT: an unsupervised medical image translation method based on cycle consistency and hybrid contrastive learning.
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-02-26 DOI: 10.1088/1361-6560/adb2d7
Weiwei Jiang, Yingyu Qin, Xiaoyan Wang, Qiuju Chen, Qiu Guan, Minhua Lu

Unsupervised medical image translation tasks are challenging due to the difficulty of obtaining perfectly paired medical images. CycleGAN-based methods have proven effective in unpaired medical image translation. However, these methods can produce artifacts in the generated medical images. To address this issue, we propose an unsupervised network based on cycle consistency and hybrid contrastive unpaired translation (CycleH-CUT). CycleH-CUT consists of two CUT (H-CUT) networks. In the H-CUT network, a query-selected attention mechanism is adopted to select queries with important features. The boosted contrastive learning loss is employed to reweight all negative patches via the optimal transport strategy. We further apply spectral normalization to improve training stability, allowing the generator to extract complex features. On the basis of the H-CUT network, a new CycleH-CUT framework is proposed to integrate contrastive learning and cycle consistency. Two H-CUT networks are used to reconstruct the generated images back to the source domain, facilitating effective translation between unpaired medical images. We conduct extensive experiments on three public datasets (BraTS, OASIS3, and IXI) and a private Spinal Column dataset to demonstrate the effectiveness of CycleH-CUT and H-CUT. Specifically, CycleH-CUT achieves an average SSIM of 0.926 in the BraTS dataset, an average SSIM of 0.796 on the OASIS3 dataset, an average SSIM of 0.932 on the IXI dataset, and an average SSIM of 0.890 on the private Spinal Column dataset.

{"title":"CycleH-CUT: an unsupervised medical image translation method based on cycle consistency and hybrid contrastive learning.","authors":"Weiwei Jiang, Yingyu Qin, Xiaoyan Wang, Qiuju Chen, Qiu Guan, Minhua Lu","doi":"10.1088/1361-6560/adb2d7","DOIUrl":"10.1088/1361-6560/adb2d7","url":null,"abstract":"<p><p>Unsupervised medical image translation tasks are challenging due to the difficulty of obtaining perfectly paired medical images. CycleGAN-based methods have proven effective in unpaired medical image translation. However, these methods can produce artifacts in the generated medical images. To address this issue, we propose an unsupervised network based on cycle consistency and hybrid contrastive unpaired translation (CycleH-CUT). CycleH-CUT consists of two CUT (H-CUT) networks. In the H-CUT network, a query-selected attention mechanism is adopted to select queries with important features. The boosted contrastive learning loss is employed to reweight all negative patches via the optimal transport strategy. We further apply spectral normalization to improve training stability, allowing the generator to extract complex features. On the basis of the H-CUT network, a new CycleH-CUT framework is proposed to integrate contrastive learning and cycle consistency. Two H-CUT networks are used to reconstruct the generated images back to the source domain, facilitating effective translation between unpaired medical images. We conduct extensive experiments on three public datasets (BraTS, OASIS3, and IXI) and a private Spinal Column dataset to demonstrate the effectiveness of CycleH-CUT and H-CUT. Specifically, CycleH-CUT achieves an average SSIM of 0.926 in the BraTS dataset, an average SSIM of 0.796 on the OASIS3 dataset, an average SSIM of 0.932 on the IXI dataset, and an average SSIM of 0.890 on the private Spinal Column dataset.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143256367","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}
引用次数: 0
Automated hallucination detection for synthetic CT images used in MR-only radiotherapy workflows. 用于纯磁共振放疗工作流程的合成 CT 图像的幻觉自动检测。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-02-25 DOI: 10.1088/1361-6560/adb5eb
Abdul K Parchur, Mohammad Zarenia, Colette Gage, Eric S Paulson, Ergun Ahunbay

Objective. Artificial intelligence (AI)-generated synthetic CT (sCT) images have become commercially available to provide electron densities and reference anatomies in MR-only radiotherapy workflows. However, hallucinations (false regions of bone or air) introduced in AI-generated sCT images may affect the accuracy of dose calculation and patient setup verification. We developed a tool to detect bone hallucinations and/or inaccuracies in AI-generated pelvic sCT images used in MR-only workflows.Approach. A deep learning auto segmentation (DLAS) model was trained to auto-segment bone on MR images. The model was implemented with a 3D SegResNet network architecture using the MONAI framework with a training dataset of 86 Dixon MR image sets paired with their corresponding ground truth contours derived from planning CT images deformed to the MR images. The model performance was then assessed on an independent testing dataset (n= 10).Main results. The DLAS model-based hallucination screener identified hallucinations in bone structures using daily MR images and accurately flagged these regions on sCT images. The sensitivity of the screener is adjustable based on the distance of discrepancies between bone regions derived from sCT to bone contours generated by the DLAS. The average specificity of the DLAS model was 0.78, 0.93 and 0.98 for distance parameters of 0.8, 1.0 and 1.2 cm, respectively. The screener identified false high-density hallucination regions in the abdomen of AI-generated sCT images for all testing patients, highlighting potential issues with the training data used for the AI sCT model.Significance. A hallucination screener for AI-generated pelvic sCT images was developed and implemented for routine clinical use. The screener serves as an important quality assurance tool for MR-only radiotherapy workflows. By identifying potential AI-generated errors, the hallucination screener may improve the safety and accuracy of sCT images used for dose calculation and image guidance.

{"title":"Automated hallucination detection for synthetic CT images used in MR-only radiotherapy workflows.","authors":"Abdul K Parchur, Mohammad Zarenia, Colette Gage, Eric S Paulson, Ergun Ahunbay","doi":"10.1088/1361-6560/adb5eb","DOIUrl":"10.1088/1361-6560/adb5eb","url":null,"abstract":"<p><p><i>Objective</i>. Artificial intelligence (AI)-generated synthetic CT (sCT) images have become commercially available to provide electron densities and reference anatomies in MR-only radiotherapy workflows. However, hallucinations (false regions of bone or air) introduced in AI-generated sCT images may affect the accuracy of dose calculation and patient setup verification. We developed a tool to detect bone hallucinations and/or inaccuracies in AI-generated pelvic sCT images used in MR-only workflows.<i>Approach</i>. A deep learning auto segmentation (DLAS) model was trained to auto-segment bone on MR images. The model was implemented with a 3D SegResNet network architecture using the MONAI framework with a training dataset of 86 Dixon MR image sets paired with their corresponding ground truth contours derived from planning CT images deformed to the MR images. The model performance was then assessed on an independent testing dataset (<i>n</i>= 10).<i>Main results</i>. The DLAS model-based hallucination screener identified hallucinations in bone structures using daily MR images and accurately flagged these regions on sCT images. The sensitivity of the screener is adjustable based on the distance of discrepancies between bone regions derived from sCT to bone contours generated by the DLAS. The average specificity of the DLAS model was 0.78, 0.93 and 0.98 for distance parameters of 0.8, 1.0 and 1.2 cm, respectively. The screener identified false high-density hallucination regions in the abdomen of AI-generated sCT images for all testing patients, highlighting potential issues with the training data used for the AI sCT model.<i>Significance</i>. A hallucination screener for AI-generated pelvic sCT images was developed and implemented for routine clinical use. The screener serves as an important quality assurance tool for MR-only radiotherapy workflows. By identifying potential AI-generated errors, the hallucination screener may improve the safety and accuracy of sCT images used for dose calculation and image guidance.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414142","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}
引用次数: 0
Retrospective study on the resonance of thermoacoustic emissions and their possible biological implications in cats treated with electron FLASH beams.
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-02-25 DOI: 10.1088/1361-6560/adb679
Julie Lascaud, Martin Rädler, Carla Rohrer Bley, Marie-Catherine Vozenin, Katia Parodi

Objective.Radiotherapy delivered at an ultra-high dose rate (UHDR) is a promising cancer treatment. In the last years, it has been shown to selectively reduce toxicity in healthy tissue by triggering the so-called FLASH effect achieved through specific temporal dose fractionation. However, the increase of the instantaneous dose rate results in the production of stronger thermoacoustic emissions for microsecond or shorter pulsed ionizing beams, which could potentially impact the treatment outcomes. Focusing on scenarios expected to create the highest acoustic intensities, the objectives of this work were to assess whether acoustic resonance can theoretically occurin vivoand how it could be mitigated in cases where it would influence the biological response.Approach.Thermoacoustic emissions were retrospectively simulated from post-treatment x-ray computed tomography scans of cats irradiated with a single high dose of electron FLASH to treat squamous carcinoma of the nasal planum. The peak dose, pressure intensity and location of the acoustic resonance were assessed for different beam positioning and reproduced for three animals.Main results.Irradiation of nasal planum in cats using a frontal electron beam results in pressure hot spots due to acoustic resonance that are observed in the vicinity of the rostral maxillary bone. The pressure distribution is mostly influenced by the anatomy (i.e. geometry and heterogeneous composition of the irradiated object), whereas its intensity largely depends on the irradiation setup. While further experimental investigation is needed to understand and mitigate potential associated risks, our results underline that acoustic phenomena so far neglected in conventional radiotherapy may need to be accounted for when using UHDR delivery.Significance.We show that specific irradiation scenarios can induce geometry-dependent thermoacoustic resonancesin vivowhich may be of sufficient magnitude to induce biological effects and impact the outcomes of FLASH radiotherapy.

{"title":"Retrospective study on the resonance of thermoacoustic emissions and their possible biological implications in cats treated with electron FLASH beams.","authors":"Julie Lascaud, Martin Rädler, Carla Rohrer Bley, Marie-Catherine Vozenin, Katia Parodi","doi":"10.1088/1361-6560/adb679","DOIUrl":"10.1088/1361-6560/adb679","url":null,"abstract":"<p><p><i>Objective.</i>Radiotherapy delivered at an ultra-high dose rate (UHDR) is a promising cancer treatment. In the last years, it has been shown to selectively reduce toxicity in healthy tissue by triggering the so-called FLASH effect achieved through specific temporal dose fractionation. However, the increase of the instantaneous dose rate results in the production of stronger thermoacoustic emissions for microsecond or shorter pulsed ionizing beams, which could potentially impact the treatment outcomes. Focusing on scenarios expected to create the highest acoustic intensities, the objectives of this work were to assess whether acoustic resonance can theoretically occur<i>in vivo</i>and how it could be mitigated in cases where it would influence the biological response.<i>Approach.</i>Thermoacoustic emissions were retrospectively simulated from post-treatment x-ray computed tomography scans of cats irradiated with a single high dose of electron FLASH to treat squamous carcinoma of the nasal planum. The peak dose, pressure intensity and location of the acoustic resonance were assessed for different beam positioning and reproduced for three animals.<i>Main results.</i>Irradiation of nasal planum in cats using a frontal electron beam results in pressure hot spots due to acoustic resonance that are observed in the vicinity of the rostral maxillary bone. The pressure distribution is mostly influenced by the anatomy (i.e. geometry and heterogeneous composition of the irradiated object), whereas its intensity largely depends on the irradiation setup. While further experimental investigation is needed to understand and mitigate potential associated risks, our results underline that acoustic phenomena so far neglected in conventional radiotherapy may need to be accounted for when using UHDR delivery.<i>Significance.</i>We show that specific irradiation scenarios can induce geometry-dependent thermoacoustic resonances<i>in vivo</i>which may be of sufficient magnitude to induce biological effects and impact the outcomes of FLASH radiotherapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425898","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}
引用次数: 0
Nuclear interaction correction based on dual-energy computed tomography in carbon-ion radiotherapy. 碳离子放射治疗中基于双能计算机断层扫描的核相互作用校正。
IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2025-02-21 DOI: 10.1088/1361-6560/adaad4
Yushi Wakisaka, Masashi Yagi, Yuki Tominaga, Shinichi Shimizu, Teiji Nishio, Kazuhiko Ogawa

Objective.Accurate dose predictions are crucial to maximizing the benefits of carbon-ion therapy (CIT). Carbon beams incident on the human body cause nuclear interactions with tissues, resulting in changes in the constituent nuclides and leading to dose errors that are conventionally corrected using conventional single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) has frequently been used for stopping power estimation in particle therapy and is well suited for correcting nuclear reactions because of its detailed body-tissue elemental information. This study proposes a correction method for the absolute dose in CIT that considers changes in nuclide composition resulting from nuclear reactions with body tissues, as a novel application of DECT.Approach.The change in dose associated with nuclear reactions is determined by correcting each integrated depth dose component of the carbon beam using a nuclear interaction correction factor. This factor is determined based on the stopping power, mass density, and nuclear interaction cross-section in body tissue. The stopping power and mass density were calculated using established methods, whereas the nuclear interaction cross-section was newly defined through a conversion equation derived from the effective atomic number.Main results.Nuclear interaction correction factors and corrected doses were determined for 85 body tissues with known compositions, comparing them with existing SECT-based methods. The root-mean-square errors of the SECT- and DECT-based nuclear interaction correction factors relative to theoretical values were 0.66% and 0.39%, respectively.Significance.This indicates a notable enhancement in the estimation accuracy with DECT. The dose calculations in uniform body tissues derived from SECT showed slight over-correction in adipose and bone tissues, whereas those based on DECT were almost consistent with theoretical values. Our proposed method demonstrates the potential of DECT for enhancing dose calculation accuracy in CIT, complementing its established role in stopping power estimation.

目的:准确的剂量预测对于最大化碳离子治疗的益处至关重要。照射在人体上的碳束引起核与组织的相互作用,导致核素成分的变化,并导致剂量误差,通常使用传统的单能量计算机断层扫描(SECT)进行校正。双能计算机断层扫描(DECT)经常用于粒子治疗中的停止功率估计,并且由于其详细的身体组织元素信息而非常适合校正核反应。本研究提出了一种校正碳离子治疗中绝对剂量的方法,该方法考虑了与身体组织核反应引起的核素组成的变化,作为DECT的一种新应用。方法:与核反应相关的剂量变化是通过使用核相互作用校正因子校正碳束的每个综合深度剂量分量来确定的。这个因素是根据停止能力、质量密度和身体组织中的核相互作用截面来确定的。利用已有的方法计算了停止功率和质量密度,并通过有效原子序数的转换方程重新定义了核相互作用截面。主要结果: ;确定了85个已知成分的身体组织的核相互作用校正因子和校正剂量,并与现有的基于ect的方法进行了比较。基于SECT和ect的核相互作用校正因子相对于理论值的均方根误差分别为0.66%和0.39%。 ;显著性: ;这表明使用DECT可以显著提高估计精度。基于SECT的均匀体组织的剂量计算显示,脂肪和骨组织的剂量计算略有过度校正,而基于DECT的剂量计算几乎与理论值一致。我们提出的方法证明了DECT在提高碳离子治疗剂量计算精度方面的潜力,补充了其在停止功率估计方面的既定作用。
{"title":"Nuclear interaction correction based on dual-energy computed tomography in carbon-ion radiotherapy.","authors":"Yushi Wakisaka, Masashi Yagi, Yuki Tominaga, Shinichi Shimizu, Teiji Nishio, Kazuhiko Ogawa","doi":"10.1088/1361-6560/adaad4","DOIUrl":"10.1088/1361-6560/adaad4","url":null,"abstract":"<p><p><i>Objective.</i>Accurate dose predictions are crucial to maximizing the benefits of carbon-ion therapy (CIT). Carbon beams incident on the human body cause nuclear interactions with tissues, resulting in changes in the constituent nuclides and leading to dose errors that are conventionally corrected using conventional single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) has frequently been used for stopping power estimation in particle therapy and is well suited for correcting nuclear reactions because of its detailed body-tissue elemental information. This study proposes a correction method for the absolute dose in CIT that considers changes in nuclide composition resulting from nuclear reactions with body tissues, as a novel application of DECT.<i>Approach.</i>The change in dose associated with nuclear reactions is determined by correcting each integrated depth dose component of the carbon beam using a nuclear interaction correction factor. This factor is determined based on the stopping power, mass density, and nuclear interaction cross-section in body tissue. The stopping power and mass density were calculated using established methods, whereas the nuclear interaction cross-section was newly defined through a conversion equation derived from the effective atomic number.<i>Main results.</i>Nuclear interaction correction factors and corrected doses were determined for 85 body tissues with known compositions, comparing them with existing SECT-based methods. The root-mean-square errors of the SECT- and DECT-based nuclear interaction correction factors relative to theoretical values were 0.66% and 0.39%, respectively.<i>Significance.</i>This indicates a notable enhancement in the estimation accuracy with DECT. The dose calculations in uniform body tissues derived from SECT showed slight over-correction in adipose and bone tissues, whereas those based on DECT were almost consistent with theoretical values. Our proposed method demonstrates the potential of DECT for enhancing dose calculation accuracy in CIT, complementing its established role in stopping power estimation.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010007","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}
引用次数: 0
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Physics in medicine and biology
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