Radiological Physics and Technology最新文献_第5页

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography. 深度学习重建对使用计算机断层扫描评估胰腺囊性病变的影响。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-08-15 DOI: 10.1007/s12194-024-00834-6

Jun Kanzawa, Koichiro Yasaka, Yuji Ohizumi, Yuichi Morita, Mariko Kurokawa, Osamu Abe

{"title":"Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.","authors":"Jun Kanzawa, Koichiro Yasaka, Yuji Ohizumi, Yuichi Morita, Mariko Kurokawa, Osamu Abe","doi":"10.1007/s12194-024-00834-6","DOIUrl":"https://doi.org/10.1007/s12194-024-00834-6","url":null,"abstract":"This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective study included 54 patients (mean age: 67.7 ± 13.1) who underwent contrast-enhanced CT from May 2023 to August 2023. Among eligible patients, 30 and 24 were positive and negative for pancreatic cystic lesions, respectively. DLR and FBP were used to reconstruct portal venous phase images. Objective image quality analyses calculated quantitative image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) using regions of interest on the abdominal aorta, pancreatic lesion, and pancreatic parenchyma. Three blinded radiologists performed subjective image quality assessment and lesion detection tests. Lesion depiction, normal structure illustration, subjective image noise, and overall image quality were utilized as subjective image quality indicators. DLR significantly reduced quantitative image noise compared with FBP (p < 0.001). SNR and CNR were significantly improved in DLR compared with FBP (p < 0.001). Three radiologists rated significantly higher scores for DLR in all subjective image quality indicators (p ≤ 0.029). Performance of DLR and FBP were comparable in lesion detection, with no statistically significant differences in the area under the receiver operating characteristic curve, sensitivity, specificity and accuracy. DLR reduced image noise and improved image quality with a clearer depiction of pancreatic structures. These improvements may have a positive effect on evaluating pancreatic cystic lesions, which can contribute to appropriate management of these lesions.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy. 评估基于加速器的硼中子俘获疗法商业治疗计划系统的计算精度和计算时间。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-08-14 DOI: 10.1007/s12194-024-00833-7

Akihiko Takeuchi, Katsumi Hirose, Ryohei Kato, Shinya Komori, Mariko Sato, Tomoaki Motoyanagi, Yuhei Yamazaki, Yuki Narita, Yoshihiro Takai, Takahiro Kato

{"title":"Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy.","authors":"Akihiko Takeuchi, Katsumi Hirose, Ryohei Kato, Shinya Komori, Mariko Sato, Tomoaki Motoyanagi, Yuhei Yamazaki, Yuki Narita, Yoshihiro Takai, Takahiro Kato","doi":"10.1007/s12194-024-00833-7","DOIUrl":"https://doi.org/10.1007/s12194-024-00833-7","url":null,"abstract":"This study aims to evaluate the feasibility of using a commercially available boron neutron capture therapy (BNCT) dose calculation program (NeuCure® Dose Engine) in terms of calculation accuracy and computation time. Treatment planning was simulated under the following calculation parameters: 1.5-5.0 mm grid sizes and 1-10% statistical uncertainties. The calculated monitor units (MUs) and computation times were evaluated. The MUs calculated on grid sizes larger than 2 mm were overestimated by 2% compared with the result of 1.5 mm grid. We established the two-step method for the routine administration of BNCT: multiple calculations involved in beam optimization should be done at a 5 mm grid and a 10% statistical uncertainty (the shortest computation time: 10.3 ± 2.1 min) in the first-step, and final dose calculations should be performed at a 2 mm grid and a 10% statistical uncertainty (satisfied clinical accuracy: 6.9 ± 0.3 h) in the second-step.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy. 在前列腺癌放疗计划 CT 图像上开发基于深度学习的新型前列腺尿道自动分割技术。

IF 1.7

Radiological Physics and Technology Pub Date : 2024-08-14 DOI: 10.1007/s12194-024-00832-8

Hisamichi Takagi, Ken Takeda, Noriyuki Kadoya, Koki Inoue, Shiki Endo, Noriyoshi Takahashi, Takaya Yamamoto, Rei Umezawa, Keiichi Jingu

{"title":"Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy.","authors":"Hisamichi Takagi, Ken Takeda, Noriyuki Kadoya, Koki Inoue, Shiki Endo, Noriyoshi Takahashi, Takaya Yamamoto, Rei Umezawa, Keiichi Jingu","doi":"10.1007/s12194-024-00832-8","DOIUrl":"https://doi.org/10.1007/s12194-024-00832-8","url":null,"abstract":"Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate's position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving image quality using the pause function combination to PROPELLER sequence in brain MRI: a phantom study. 在脑磁共振成像中使用暂停功能组合 PROPELLER 序列提高图像质量：一项模型研究。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-02-17 DOI: 10.1007/s12194-024-00784-z

Kousaku Saotome, Koji Matsumoto, Yoshiaki Kato, Yoshihiro Ozaki, Motohiro Nagai, Tomoyuki Hasegawa, Hiroki Tsuchiya, Tensho Yamao

引用次数: 0

Improved detection of cholesterol gallstones using quasi-material decomposition images generated from single-energy computed tomography images via deep learning. 通过深度学习利用单能计算机断层扫描图像生成的准物质分解图像改进胆固醇胆结石的检测。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-02-23 DOI: 10.1007/s12194-024-00783-0

Kojiro Nishijima, Junji Shiraishi

{"title":"Improved detection of cholesterol gallstones using quasi-material decomposition images generated from single-energy computed tomography images via deep learning.","authors":"Kojiro Nishijima, Junji Shiraishi","doi":"10.1007/s12194-024-00783-0","DOIUrl":"10.1007/s12194-024-00783-0","url":null,"abstract":"In this study, we developed a method for generating quasi-material decomposition (quasi-MD) images from single-energy computed tomography (SECT) images using a deep convolutional neural network (DCNN). Our aim was to improve the detection of cholesterol gallstones and to determine the clinical utility of quasi-MD images. Four thousand pairs of virtual monochromatic images (70 keV) and MD images (fat/water) of the same section, obtained via dual-energy computed tomography (DECT), were used to train the DCNN. The trained DCNN can automatically generate quasi-MD images from the SECT images. Additional SECT images were obtained from 70 patients (40 with and 30 without cholesterol gallstones) to generate quasi-MD images for testing. The presence of gallstones in this dataset was confirmed by ultrasonography. We conducted a receiver operating characteristic (ROC) observer study with three radiologists to validate the clinical utility of the quasi-MD images for detecting cholesterol gallstones. The mean area under the ROC curve for the detection of cholesterol gallstones improved from 0.867 to 0.921 (p = 0.001) when quasi-MD images were added to SECT images. The clinical utility of quasi-MD imaging for detecting cholesterol gallstones was showed. This study demonstrated that the lesion detection capability of images obtained from SECT can be improved using a DCNN trained with DECT images obtained using high-end computed tomography systems.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139933508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep-learning-based scatter correction with water equivalent path length map for digital radiography. 基于深度学习的数字射线摄影散射校正与水等效路径长度图。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-05-02 DOI: 10.1007/s12194-024-00807-9

Masayuki Hattori, Hisato Tsubakiya, Sung-Hyun Lee, Takayuki Kanai, Koji Suzuki, Tetsuya Yuasa

{"title":"A deep-learning-based scatter correction with water equivalent path length map for digital radiography.","authors":"Masayuki Hattori, Hisato Tsubakiya, Sung-Hyun Lee, Takayuki Kanai, Koji Suzuki, Tetsuya Yuasa","doi":"10.1007/s12194-024-00807-9","DOIUrl":"10.1007/s12194-024-00807-9","url":null,"abstract":"We proposed a new deep learning (DL) model for accurate scatter correction in digital radiography. The proposed network featured a pixel-wise water equivalent path length (WEPL) map of subjects with diverse sizes and 3D inner structures. The proposed U-Net model comprises two concatenated modules: one for generating a WEPL map and the other for predicting scatter using the WEPL map as auxiliary information. First, 3D CT images were used as numerical phantoms for training and validation, generating observed and scattered images by Monte Carlo simulation, and WEPL maps using Siddon's algorithm. Then, we optimised the model without overfitting. Next, we validated the proposed model's performance by comparing it with other DL models. The proposed model obtained scatter-corrected images with a peak signal-to-noise ratio of 44.24 ± 2.89 dB and a structural similarity index measure of 0.9987 ± 0.0004, which were higher than other DL models. Finally, scatter fractions (SFs) were compared with other DL models using an actual phantom to confirm practicality. Among DL models, the proposed model showed the smallest deviation from measured SF values. Furthermore, using an actual radiograph containing an acrylic object, the contrast-to-noise ratio (CNR) of the proposed model and the anti-scatter grid were compared. The CNR of the images corrected using the proposed model are 16% and 82% higher than those of the raw and grid-applied images, respectively. The advantage of the proposed method is that no actual radiography system is required for collecting training dataset, as the dataset is created from CT images using Monte Carlo simulation.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140871844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D-printed boluses for radiotherapy: influence of geometrical and printing parameters on dosimetric characterization and air gap evaluation. 用于放射治疗的三维打印栓剂：几何参数和打印参数对剂量测定特征和气隙评估的影响。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-02-13 DOI: 10.1007/s12194-024-00782-1

Simone Giovanni Gugliandolo, Shabarish Purushothaman Pillai, Shankar Rajendran, Maria Giulia Vincini, Matteo Pepa, Floriana Pansini, Mattia Zaffaroni, Giulia Marvaso, Daniela Alterio, Andrea Vavassori, Stefano Durante, Stefania Volpe, Federica Cattani, Barbara Alicja Jereczek-Fossa, Davide Moscatelli, Bianca Maria Colosimo

{"title":"3D-printed boluses for radiotherapy: influence of geometrical and printing parameters on dosimetric characterization and air gap evaluation.","authors":"Simone Giovanni Gugliandolo, Shabarish Purushothaman Pillai, Shankar Rajendran, Maria Giulia Vincini, Matteo Pepa, Floriana Pansini, Mattia Zaffaroni, Giulia Marvaso, Daniela Alterio, Andrea Vavassori, Stefano Durante, Stefania Volpe, Federica Cattani, Barbara Alicja Jereczek-Fossa, Davide Moscatelli, Bianca Maria Colosimo","doi":"10.1007/s12194-024-00782-1","DOIUrl":"10.1007/s12194-024-00782-1","url":null,"abstract":"The work investigates the implementation of personalized radiotherapy boluses by means of additive manufacturing technologies. Boluses materials that are currently used need an excessive amount of human intervention which leads to reduced repeatability in terms of dosimetry. Additive manufacturing can solve this problem by eliminating the human factor in the process of fabrication. Planar boluses with fixed geometry and personalized boluses printed starting from a computed tomography scan of a radiotherapy phantom were produced. First, a dosimetric characterization study on planar bolus designs to quantify the effects of print parameters such as infill density and geometry on the radiation beam was made. Secondly, a volumetric quantification of air gap between the bolus and the skin of the patient as well as dosimetric analyses were performed. The optimization process according to the obtained dosimetric and airgap results allowed us to find a combination of parameters to have the 3D-printed bolus performing similarly to that in conventional use. These preliminary results confirm those in the relevant literature, with 3D-printed boluses showing a dosimetric performance similar to conventional boluses with the additional advantage of being perfectly conformed to the patient geometry.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11128404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139730720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Denoising parameter dependence of coronary artery depictability in compressed sensing magnetic resonance angiography. 压缩传感磁共振血管造影中冠状动脉描绘性的去噪参数依赖性。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-03-09 DOI: 10.1007/s12194-024-00787-w

Junji Takahashi, Yoshio Machida, Kei Fukuzawa, Yoshinori Tsuji, Yuki Ohmoto-Sekine

{"title":"Denoising parameter dependence of coronary artery depictability in compressed sensing magnetic resonance angiography.","authors":"Junji Takahashi, Yoshio Machida, Kei Fukuzawa, Yoshinori Tsuji, Yuki Ohmoto-Sekine","doi":"10.1007/s12194-024-00787-w","DOIUrl":"10.1007/s12194-024-00787-w","url":null,"abstract":"Using numerical indices and visual evaluation, we evaluated the dependence of coronary-artery depictability on the denoising parameter in compressed sensing magnetic resonance angiography (CS-MRA). This study was conducted to clarify the acceleration factor (AF) and denoising factor (DF) dependence of CS-MRA image quality. Vascular phantoms and clinical images were acquired using three-dimensional CS-MRA on a clinical 1.5 T system. For the phantom measurements, we compared the full width at half maximum (FWHM), sharpness, and contrast ratio of the vascular profile curves for various AFs and DFs. In the clinical cases, the FWHM, sharpness, contrast ratio, signal-to-noise ratio, noise level values, and visual evaluation results were compared for various DFs. Phantom image analyses demonstrated that the respective measurements of the FWHM, sharpness, and contrast ratios did not significantly change with an increase in AF. The FWHM and sharpness measurements slightly changed with the DF level. However, the contrast ratio tended to increase with an increase in the DF level. In the clinical cases, the FWHM and sharpness showed no significant differences, even when the DF level was changed. However, the contrast ratio tended to decrease as the DF level increased. When the DF levels of the clinical cases increased, the background signals of the myocardium, fat, and noise levels decreased. We investigated the dependence of the coronary-artery depictability on AF and DF using CS-MRA. Analysis of the coronary-artery profile curves indicated that a better image quality was achieved with a stronger DF on coronary CS-MRA.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140068847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm. 使用自监督去噪算法处理的低剂量 CT 图像的主观和客观图像质量。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-02-27 DOI: 10.1007/s12194-024-00786-x

Yuya Kimura, Takeru Q Suyama, Yasuteru Shimamura, Jun Suzuki, Masato Watanabe, Hiroshi Igei, Yuya Otera, Takayuki Kaneko, Maho Suzukawa, Hirotoshi Matsui, Hiroyuki Kudo

{"title":"Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm.","authors":"Yuya Kimura, Takeru Q Suyama, Yasuteru Shimamura, Jun Suzuki, Masato Watanabe, Hiroshi Igei, Yuya Otera, Takayuki Kaneko, Maho Suzukawa, Hirotoshi Matsui, Hiroyuki Kudo","doi":"10.1007/s12194-024-00786-x","DOIUrl":"10.1007/s12194-024-00786-x","url":null,"abstract":"This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-fractional error and intra-fractional motion of prostate and dosimetry comparisons of patient position registrations with versus without fiducial markers during treatment with carbon-ion radiotherapy. 碳离子放射治疗过程中前列腺的点间误差和点内运动，以及有无靶标的患者位置登记的剂量测定比较。

IF 1.6

Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-05-01 DOI: 10.1007/s12194-024-00808-8

Yuma Iwai, Shinichiro Mori, Hitoshi Ishikawa, Nobuyuki Kanematsu, Shinnosuke Matsumoto, Taku Nakaji, Noriyuki Okonogi, Kana Kobayashi, Masaru Wakatsuki, Takashi Uno, Shigeru Yamada

{"title":"Inter-fractional error and intra-fractional motion of prostate and dosimetry comparisons of patient position registrations with versus without fiducial markers during treatment with carbon-ion radiotherapy.","authors":"Yuma Iwai, Shinichiro Mori, Hitoshi Ishikawa, Nobuyuki Kanematsu, Shinnosuke Matsumoto, Taku Nakaji, Noriyuki Okonogi, Kana Kobayashi, Masaru Wakatsuki, Takashi Uno, Shigeru Yamada","doi":"10.1007/s12194-024-00808-8","DOIUrl":"10.1007/s12194-024-00808-8","url":null,"abstract":"A few reports have discussed the influence of inter-fractional position error and intra-fractional motion on dose distribution, particularly regarding a spread-out Bragg peak. We investigated inter-fractional and intra-fractional prostate position error by monitoring fiducial marker positions. In 2020, data from 15 patients with prostate cancer who received carbon-ion beam radiotherapy (CIRT) with gold markers were investigated. We checked marker positions before and during irradiation to calculate the inter-fractional positioning and intra-fractional movement and evaluated the CIRT dose distribution by adjusting the planning beam isocenter and clinical target volume (CTV) position. We compared the CTV dose coverages (CTV receiving 95% [V95%] or 98% [V98%] of the prescribed dose) between skeletal and fiducial matching irradiation on the treatment planning system. For inter-fractional error, the mean distance between the marker position in the planning images and that in a patient starting irradiation with skeletal matching was 1.49 ± 1.11 mm (95th percentile = 1.85 mm). The 95th percentile (maximum) values of the intra-fractional movement were 0.79 mm (2.31 mm), 1.17 mm (2.48 mm), 1.88 mm (4.01 mm), 1.23 mm (3.00 mm), and 2.09 mm (8.46 mm) along the lateral, inferior, superior, dorsal, and ventral axes, respectively. The mean V95% and V98% were 98.2% and 96.2% for the skeletal matching plan and 99.5% and 96.8% for the fiducial matching plan, respectively. Fiducial matching irradiation improved the CTV dose coverage compared with skeletal matching irradiation for CIRT for prostate cancer.","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0