Objective: T2-weighted 2D fast spin echo sequence serves as the standard sequence in
clinical pelvic MR imaging protocols. However, motion artifacts and blurring caused
by peristalsis present significant challenges. Patient preparation such as administering
antiperistaltic agents is often required before examination to reduce artifacts, which
discomfort the patients. This work introduce a novel dynamic approach for T2
weighted pelvic imaging to address peristalsis-induced motion issue without any patient
preparation.
Approach: A rapid dynamic data acquisition strategy with complementary sampling
trajectory is designed to enable highly undersampled motion-resistant data sampling,
and an unrolling method based on deep equilibrium model is leveraged to reconstruct
images from the dynamic sampled k-space data. Moreover, the fix-point convergence of
the equilibrium model ensures the stability of the reconstruction. The high acceleration
factor in each temporal phase, which is much higher than that in traditional static
imaging, has the potential to effectively freeze pelvic motion, thereby transforming
the imaging problem from conventional motion prevention or removal to motion
reconstruction.
Main results: Experiments on both retrospective and prospective data have
demonstrated the superior performance of the proposed dynamic approach in reducing
motion artifacts and accurately depicting structural details compared to standard static
imaging.
Significance: The proposed dynamic approach effectively captures motion states
through dynamic data acquisition and deep learning-based reconstruction, addressing
motion-related challenges in pelvic imaging.
{"title":"A dynamic approach for MR T2-weighted pelvic imaging.","authors":"Jing Cheng, Qingneng Li, Naijia Liu, Jun Yang, Yu Fu, Zhuoxu Cui, Zhenkui Wang, Guobin Li, Huimao Zhang, Dong Liang","doi":"10.1088/1361-6560/ad8335","DOIUrl":"https://doi.org/10.1088/1361-6560/ad8335","url":null,"abstract":"<p><strong>Objective: </strong>T2-weighted 2D fast spin echo sequence serves as the standard sequence in
clinical pelvic MR imaging protocols. However, motion artifacts and blurring caused
by peristalsis present significant challenges. Patient preparation such as administering
antiperistaltic agents is often required before examination to reduce artifacts, which
discomfort the patients. This work introduce a novel dynamic approach for T2
weighted pelvic imaging to address peristalsis-induced motion issue without any patient
preparation.
Approach: A rapid dynamic data acquisition strategy with complementary sampling
trajectory is designed to enable highly undersampled motion-resistant data sampling,
and an unrolling method based on deep equilibrium model is leveraged to reconstruct
images from the dynamic sampled k-space data. Moreover, the fix-point convergence of
the equilibrium model ensures the stability of the reconstruction. The high acceleration
factor in each temporal phase, which is much higher than that in traditional static
imaging, has the potential to effectively freeze pelvic motion, thereby transforming
the imaging problem from conventional motion prevention or removal to motion
reconstruction.
Main results: Experiments on both retrospective and prospective data have
demonstrated the superior performance of the proposed dynamic approach in reducing
motion artifacts and accurately depicting structural details compared to standard static
imaging.
Significance: The proposed dynamic approach effectively captures motion states
through dynamic data acquisition and deep learning-based reconstruction, addressing
motion-related challenges in pelvic imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372587","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}
Objective:
Blood flow sensitivity is a crucial metric for appraising the effectiveness of color Doppler flow imaging (CDFI). Color Doppler velocity maps based on classic autocorrelation techniques are widely used in clinical practice. However, these techniques often produce twinkling artifacts in noisy regions due to the inherent randomness of noise phases. To mitigate artifacts and improve image quality, Power Mask (PoM) technology becomes imperative. Nevertheless, PoM technology unintentionally filters out small flow signals that have similar power and frequency characteristics to noise signals, thereby reducing the imaging system's sensitivity to flow.
Approach:
To address this issue, a novel Flow Recycling Algorithm (FRA) based on phase anomaly is introduced in this study. This algorithm, excavating small flow signals from noise, aims to enhance the small flow signals with low-velocity by the phase characteristics of the color Doppler flow information.
Main results:
Experiments in multi-organ imaging have shown that the FRA-CDFI approach is more effective in suppressing twinkling artifacts in noisy regions, preserving intricate small flow signals, and markedly improving small blood flow sensitivity. This novel approach provides adequate technical support for clinical ultrasound imaging of organs with dense small blood vessels, such as the brain, kidneys, liver, and more.
Significance:
As a novel post-processing method, FRA-CDFI holds significant potential for future deployment in clinical high-frame-rate ultrasound imaging devices.
{"title":"Improving microvascular sensitivity of color doppler using phase mask based flow recycling algorithm.","authors":"Hao Yu, Jiabin Zhang, Jingyi Yin, Jinyu Yang, Daichao Chen, Yu Xia, Jue Zhang","doi":"10.1088/1361-6560/ad8292","DOIUrl":"https://doi.org/10.1088/1361-6560/ad8292","url":null,"abstract":"<p><strong>Objective: </strong>
Blood flow sensitivity is a crucial metric for appraising the effectiveness of color Doppler flow imaging (CDFI). Color Doppler velocity maps based on classic autocorrelation techniques are widely used in clinical practice. However, these techniques often produce twinkling artifacts in noisy regions due to the inherent randomness of noise phases. To mitigate artifacts and improve image quality, Power Mask (PoM) technology becomes imperative. Nevertheless, PoM technology unintentionally filters out small flow signals that have similar power and frequency characteristics to noise signals, thereby reducing the imaging system's sensitivity to flow. 
Approach:
To address this issue, a novel Flow Recycling Algorithm (FRA) based on phase anomaly is introduced in this study. This algorithm, excavating small flow signals from noise, aims to enhance the small flow signals with low-velocity by the phase characteristics of the color Doppler flow information. 
Main results: 
Experiments in multi-organ imaging have shown that the FRA-CDFI approach is more effective in suppressing twinkling artifacts in noisy regions, preserving intricate small flow signals, and markedly improving small blood flow sensitivity. This novel approach provides adequate technical support for clinical ultrasound imaging of organs with dense small blood vessels, such as the brain, kidneys, liver, and more. 
Significance: 
As a novel post-processing method, FRA-CDFI holds significant potential for future deployment in clinical high-frame-rate ultrasound imaging devices.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142366167","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}
Pub Date : 2024-10-01DOI: 10.1088/1361-6560/ad7dcb
Mark Wright, Gawon Han, Jihyun Yun, Eugene Yip, Zsolt Gabos, Nawaid Usmani, B Gino Fallone, Keith Wachowicz
Objective.To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications.Approach.Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting.Main results.It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation.Significance.This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU).
目标:以混合方式开发一种利用主成分分析(PCA)的二维磁共振加速方法,用于快速实时应用:对 10 个肺部、10 个肝部和 10 个前列腺 3T 磁共振成像数据集进行回顾性测试,以检测图像质量和目标可视性。对 k 空间进行采样时,在每一帧中获取中心(低频)数据,同时对高频数据进行不连贯的欠采样,从而在预先确定的帧数中获取所有 k 空间。首先,使用代表中央和外部 k 空间数据帧内相关性的主成分(PC)来估计相关帧中的未采样数据。然后,为了进一步增加稳定性,将代表最近帧重建窗口内时域波动的 PC 与外部 k 空间数据(包括上述估计值)进行拟合,以获得相关帧的最终估计值。对 3 倍和 8 倍之间的加速重建进行了图像质量和可比性测试,同时测试了拟合 PC 的最佳数量:结果发现,在较高的加速度下,图像质量并没有明显下降。同样,在所有测试的加速度和地点,图像质量都足以使用自动轮廓软件绘制目标轮廓。在所有测试的加速度下,SSIM 值均≥ 0.91。同样,即使在 8 倍加速度下,不同地点的骰子系数也≥ 0.89,与观察者内部差异相当或更好:与以前的 PCA 方法相比,这种方法似乎能提高图像质量和轮廓度,同时还能在重建过程中使用更多 PC。该方法可使用简单的单通道线圈运行,不需要强大的计算能力就能达到实时介入标准(在英特尔 i5 CPU 上重建时间约为 50 毫秒/帧)。
{"title":"A hybrid PCA acceleration method for rapid real-time 2D MRI.","authors":"Mark Wright, Gawon Han, Jihyun Yun, Eugene Yip, Zsolt Gabos, Nawaid Usmani, B Gino Fallone, Keith Wachowicz","doi":"10.1088/1361-6560/ad7dcb","DOIUrl":"10.1088/1361-6560/ad7dcb","url":null,"abstract":"<p><p><i>Objective.</i>To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications.<i>Approach.</i>Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting.<i>Main results.</i>It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation.<i>Significance.</i>This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU).</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293203","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}
Pub Date : 2024-10-01DOI: 10.1088/1361-6560/ad7e75
Alexandre Merasli, Brunnhilde Ponsi, Maël Millardet, Thomas Carlier, Simon Stute
We read with great interest the paper by Limet al(2018Phys. Med. Biol.63035042) on bias reduction in Y-90 PET imaging. In particular, they proposed a new formulation of the tomographic reconstruction problem that enforces non-negativity in projection space as opposed to image space. We comment on the algorithm they derived from this formulation and bring some clarifications on the constraint that this algorithm respects.
我们饶有兴趣地阅读了 Limet al(2018Phys. Med. Biol.63035042)关于减少 Y-90 PET 成像偏差的论文。特别是,他们提出了一种断层重建问题的新表述,该表述在投影空间而非图像空间中强制执行非负性。我们对他们从这一表述中推导出的算法进行了评论,并对这一算法所遵守的约束条件做了一些说明。
{"title":"Comment on 'A PET reconstruction formulation that enforces non-negativity in projection space for bias reduction in Y-90 imaging'.","authors":"Alexandre Merasli, Brunnhilde Ponsi, Maël Millardet, Thomas Carlier, Simon Stute","doi":"10.1088/1361-6560/ad7e75","DOIUrl":"https://doi.org/10.1088/1361-6560/ad7e75","url":null,"abstract":"<p><p>We read with great interest the paper by Lim<i>et al</i>(2018<i>Phys. Med. Biol.</i><b>63</b>035042) on bias reduction in Y-90 PET imaging. In particular, they proposed a new formulation of the tomographic reconstruction problem that enforces non-negativity in projection space as opposed to image space. We comment on the algorithm they derived from this formulation and bring some clarifications on the constraint that this algorithm respects.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142352073","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}
Pub Date : 2024-09-30DOI: 10.1088/1361-6560/ad7d59
C Galeone, T Steinsberger, M Donetti, M C Martire, F M Milian, R Sacchi, A Vignati, L Volz, M Durante, S Giordanengo, C Graeff
Objective. Real-time adaptive particle therapy is being investigated as a means to maximize the treatment delivery accuracy. To react to dosimetric errors, a system for fast and reliable verification of the agreement between planned and delivered doses is essential. This study presents a clinically feasible, real-time 4D-dose reconstruction system, synchronized with the treatment delivery and motion of the patient, which can provide the necessary feedback on the quality of the delivery.Approach. A GPU-based analytical dose engine capable of millisecond dose calculation for carbon ion therapy has been developed and interfaced with the next generation of the dose delivery system (DDS) in use at Centro Nazionale di Adroterapia Oncologica (CNAO). The system receives the spot parameters and the motion information of the patient during the treatment and performs the reconstruction of the planned and delivered 4D-doses. After each iso-energy layer, the results are displayed on a graphical user interface by the end of the spill pause of the synchrotron, permitting verification against the reference dose. The framework has been verified experimentally at CNAO for a lung cancer case based on a virtual phantom 4DCT. The patient's motion was mimicked by a moving Ionization Chamber (IC) 2D-array.Mainresults. For the investigated static and 4D-optimized treatment delivery cases, real-time dose reconstruction was achieved with an average pencil beam dose calculation speed up to more than one order of magnitude smaller than the spot delivery. The reconstructed doses have been benchmarked against offline log-file based dose reconstruction with the TRiP98 treatment planning system, as well as QA measurements with the IC 2D-array, where an average gamma-index passing rate (3%/3 mm) of 99.8% and 98.3%, respectively, were achieved.Significance. This work provides the first real-time 4D-dose reconstruction engine for carbon ion therapy. The framework integration with the CNAO DDS paves the way for a swift transition to the clinics.
{"title":"Real-time delivered dose assessment in carbon ion therapy of moving targets.","authors":"C Galeone, T Steinsberger, M Donetti, M C Martire, F M Milian, R Sacchi, A Vignati, L Volz, M Durante, S Giordanengo, C Graeff","doi":"10.1088/1361-6560/ad7d59","DOIUrl":"10.1088/1361-6560/ad7d59","url":null,"abstract":"<p><p><i>Objective</i>. Real-time adaptive particle therapy is being investigated as a means to maximize the treatment delivery accuracy. To react to dosimetric errors, a system for fast and reliable verification of the agreement between planned and delivered doses is essential. This study presents a clinically feasible, real-time 4D-dose reconstruction system, synchronized with the treatment delivery and motion of the patient, which can provide the necessary feedback on the quality of the delivery.<i>Approach</i>. A GPU-based analytical dose engine capable of millisecond dose calculation for carbon ion therapy has been developed and interfaced with the next generation of the dose delivery system (DDS) in use at Centro Nazionale di Adroterapia Oncologica (CNAO). The system receives the spot parameters and the motion information of the patient during the treatment and performs the reconstruction of the planned and delivered 4D-doses. After each iso-energy layer, the results are displayed on a graphical user interface by the end of the spill pause of the synchrotron, permitting verification against the reference dose. The framework has been verified experimentally at CNAO for a lung cancer case based on a virtual phantom 4DCT. The patient's motion was mimicked by a moving Ionization Chamber (IC) 2D-array.<i>Main</i><i>results</i>. For the investigated static and 4D-optimized treatment delivery cases, real-time dose reconstruction was achieved with an average pencil beam dose calculation speed up to more than one order of magnitude smaller than the spot delivery. The reconstructed doses have been benchmarked against offline log-file based dose reconstruction with the TRiP98 treatment planning system, as well as QA measurements with the IC 2D-array, where an average gamma-index passing rate (3%/3 mm) of 99.8% and 98.3%, respectively, were achieved.<i>Significance</i>. This work provides the first real-time 4D-dose reconstruction engine for carbon ion therapy. The framework integration with the CNAO DDS paves the way for a swift transition to the clinics.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293205","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}
Pub Date : 2024-09-27DOI: 10.1088/1361-6560/ad7d5c
Zahra Ahmadi Ganjeh, Brian Zapien-Campos, Erik Traneus, Stefan Both, Peter Dendooven
Objective.12N, having a half-life of 11 ms, is a highly effective positron emitter that can potentially provide near real-time feedback in proton therapy. There is currently no framework for comparing and validating positron emission imaging of12N. This work describes the development and validation of a Monte Carlo (MC) framework to calculate the images of12N, as well as long-lived isotopes, originating from activation by protons.Approach. The available dual-panel Biograph mCT PET scanner was modeled in GATE and validated by comparing the simulated sensitivity map with the measured one. The distributions of12N and long-lived isotopes were calculated by RayStation and used as the input of GATE simulations. The RayStation/GATE combination was verified using proton beam irradiations of homogeneous phantoms. A 120 MeV pulsed pencil beam with 108protons per pulse was used. Two-dimensional images were created from the GATE output and compared with the images based on the measurements and the 1D longitudinal projection of the full 2D image was used to calculate the12N activity range.Main results. The simulated sensitivity in the center of the FoV (5.44%) agrees well with the measured one (5.41%). The simulated and measured 2D sensitivity maps agree in good detail. The relative difference between the measured and simulated positron activity range for both12N and long-lived isotopes is less than 1%. The broadening of the12N images relative to those of the longer-lived isotopes can be understood in terms of the large positron range of12N.Significance. We developed and validated a MC framework based on RayStation/GATE to support the in-beam PET method for quality assurance of proton therapy. The inclusion of the very short-lived isotope12N makes the framework useful for developing near real-time verification. This represents a significant step towards translating12N real-time in vivo verification to the clinic.
{"title":"RayStation/GATE Monte Carlo simulation framework for verification of proton therapy based on the<sup>12</sup>N imaging.","authors":"Zahra Ahmadi Ganjeh, Brian Zapien-Campos, Erik Traneus, Stefan Both, Peter Dendooven","doi":"10.1088/1361-6560/ad7d5c","DOIUrl":"10.1088/1361-6560/ad7d5c","url":null,"abstract":"<p><p><i>Objective</i>.<sup>12</sup>N, having a half-life of 11 ms, is a highly effective positron emitter that can potentially provide near real-time feedback in proton therapy. There is currently no framework for comparing and validating positron emission imaging of<sup>12</sup>N. This work describes the development and validation of a Monte Carlo (MC) framework to calculate the images of<sup>12</sup>N, as well as long-lived isotopes, originating from activation by protons.<i>Approach</i>. The available dual-panel Biograph mCT PET scanner was modeled in GATE and validated by comparing the simulated sensitivity map with the measured one. The distributions of<sup>12</sup>N and long-lived isotopes were calculated by RayStation and used as the input of GATE simulations. The RayStation/GATE combination was verified using proton beam irradiations of homogeneous phantoms. A 120 MeV pulsed pencil beam with 10<sup>8</sup>protons per pulse was used. Two-dimensional images were created from the GATE output and compared with the images based on the measurements and the 1D longitudinal projection of the full 2D image was used to calculate the<sup>12</sup>N activity range.<i>Main results</i>. The simulated sensitivity in the center of the FoV (5.44%) agrees well with the measured one (5.41%). The simulated and measured 2D sensitivity maps agree in good detail. The relative difference between the measured and simulated positron activity range for both<sup>12</sup>N and long-lived isotopes is less than 1%. The broadening of the<sup>12</sup>N images relative to those of the longer-lived isotopes can be understood in terms of the large positron range of<sup>12</sup>N.<i>Significance</i>. We developed and validated a MC framework based on RayStation/GATE to support the in-beam PET method for quality assurance of proton therapy. The inclusion of the very short-lived isotope<sup>12</sup>N makes the framework useful for developing near real-time verification. This represents a significant step towards translating<sup>12</sup>N real-time in vivo verification to the clinic.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293204","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}
Pub Date : 2024-09-25DOI: 10.1088/1361-6560/ad75df
Jeffrey Dhari, Jesse Tanguay
Objective.Photon-counting x-ray detectors (PCDs) can produce dual-energy (DE) x-ray images of lung cancer in a single x-ray exposure. It is important to understand the factors that affect contrast, noise and the contrast-to-noise ratio (CNR). This study quantifies the dependence of CNR on tube voltage, energy threshold and patient thickness in single exposure, DE, bone-suppressed thoracic imaging with PCDs, and elucidates how the fundamental processes inherent in x-ray detection by PCDs contribute to CNR degradation.Approach.We modeled the DE CNR for five theoretical PCDs, ranging from an ideal PCD that detects every primary photon in the correct energy bin while rejecting all scattered radiation to a non-ideal PCD that suffers from charge-sharing and electronic noise, and detects scatter. CNR was computed as a function of tube voltage and high energy threshold for average and larger-than-average patients. Model predictions were compared with experimental data extracted from images acquired using a cadmium telluride (CdTe) PCD with two energy bins and analog charge summing for charge-sharing suppression. The imaging phantom simulated attenuation, scatter and contrast in lung nodule imaging. We quantified CNR improvements achievable with anti-correlated noise reduction (ACNR) and measured the range of exposure rates over which pulse pile-up is negligible.Main Results.The realistic model predicted overall trends observed in the experimental data. CNR improvements with ACNR were approximately five-fold, and modeled CNR-enhancements were on average within 10% of experiment. CNR increased modestly (i.e.<20%) when increasing the tube voltage from 90 kV to 130 kV. Optimal energy thresholds ranged from 50 keV to 70 keV across all tube voltages and patient thicknesses with and without ACNR. Quantum efficiency, electronic noise, charge sharing and scatter degraded CNR by ~50%. Charge sharing and scatter had the largest effect on CNR, degrading it by ~30% and ~15% respectively. Dead-time losses were less than 5% for patient exposure rates within the range of clinical exposure rates.Significance.In this study, we (1) employed analytical and computational models to assess the impact of different factors on CNR in single-exposure DE imaging with PCDs, (2) evaluated the accuracy of these models in predicting experimental trends, (3) quantified improvements in CNR achievable through ACNR and (4) determined the range of patient exposure rates at which pulse pile-up can be considered negligible. To the best of our knowledge, this study represents the first systematic investigation of single-exposure DE imaging of lung nodules with PCDs.
目的:光子计数 X 射线探测器(PCD)可在一次 X 射线曝光中生成肺癌的双能量(DE)X 射线图像。本研究量化了使用 PCD 进行单次曝光、双能量、骨抑制胸部成像时对比度-噪声比(CNR)与管电压、能量阈值和患者厚度的关系,并阐明了 PCD 检测 X 射线的固有过程如何导致 CNR 下降:我们对五种理论 PCD 的 DE CNR 进行了建模,这些 PCD 既有在正确的能量区间检测到每个光子并拒绝散射的理想 PCD,也有受电荷共享和电子噪声影响并检测到散射的非理想 PCD。模型预测结果与使用碲化镉 PCD 采集的图像实验数据进行了比较。成像模型模拟了肺结节成像中的衰减、散射和对比度。我们量化了反相关降噪(AcNR)可实现的 CNR 改进,并测量了脉冲堆积可忽略不计的曝光率范围:在最佳能量阈值下,使用或不使用 ACNR 的模型 CNR 与实验 CNR 的差距在 10%以内。使用 ACNR 时,CNR 提高了约五倍。CNR 增加
{"title":"Contrast and quantum noise in single-exposure dual-energy thoracic imaging with photon-counting x-ray detectors.","authors":"Jeffrey Dhari, Jesse Tanguay","doi":"10.1088/1361-6560/ad75df","DOIUrl":"10.1088/1361-6560/ad75df","url":null,"abstract":"<p><p><i>Objective.</i>Photon-counting x-ray detectors (PCDs) can produce dual-energy (DE) x-ray images of lung cancer in a single x-ray exposure. It is important to understand the factors that affect contrast, noise and the contrast-to-noise ratio (CNR). This study quantifies the dependence of CNR on tube voltage, energy threshold and patient thickness in single exposure, DE, bone-suppressed thoracic imaging with PCDs, and elucidates how the fundamental processes inherent in x-ray detection by PCDs contribute to CNR degradation.<i>Approach.</i>We modeled the DE CNR for five theoretical PCDs, ranging from an ideal PCD that detects every primary photon in the correct energy bin while rejecting all scattered radiation to a non-ideal PCD that suffers from charge-sharing and electronic noise, and detects scatter. CNR was computed as a function of tube voltage and high energy threshold for average and larger-than-average patients. Model predictions were compared with experimental data extracted from images acquired using a cadmium telluride (CdTe) PCD with two energy bins and analog charge summing for charge-sharing suppression. The imaging phantom simulated attenuation, scatter and contrast in lung nodule imaging. We quantified CNR improvements achievable with anti-correlated noise reduction (ACNR) and measured the range of exposure rates over which pulse pile-up is negligible.<i>Main Results.</i>The realistic model predicted overall trends observed in the experimental data. CNR improvements with ACNR were approximately five-fold, and modeled CNR-enhancements were on average within 10% of experiment. CNR increased modestly (i.e.<20%) when increasing the tube voltage from 90 kV to 130 kV. Optimal energy thresholds ranged from 50 keV to 70 keV across all tube voltages and patient thicknesses with and without ACNR. Quantum efficiency, electronic noise, charge sharing and scatter degraded CNR by ~50%. Charge sharing and scatter had the largest effect on CNR, degrading it by ~30% and ~15% respectively. Dead-time losses were less than 5% for patient exposure rates within the range of clinical exposure rates.<i>Significance.</i>In this study, we (1) employed analytical and computational models to assess the impact of different factors on CNR in single-exposure DE imaging with PCDs, (2) evaluated the accuracy of these models in predicting experimental trends, (3) quantified improvements in CNR achievable through ACNR and (4) determined the range of patient exposure rates at which pulse pile-up can be considered negligible. To the best of our knowledge, this study represents the first systematic investigation of single-exposure DE imaging of lung nodules with PCDs.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142110918","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}
Pub Date : 2024-09-25DOI: 10.1088/1361-6560/ad7fc6
Rohan Nadkarni, Darin P Clark, Alex Jeffrey Allphin, Cristian T Badea
Objective:
Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging.
Approach.
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm.
Main results.
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D).
Significance.
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.
目的:
用于 CT 成像的光子计数探测器(PCD)利用能量阈值同时获取多个能量的投影,使其适用于光谱成像和材料分解。遗憾的是,设置多个能量阈值会导致分析重建时出现噪声,原因是高能量区的光子计数较低。迭代重建可提供高质量的光子计数 CT(PCCT)图像,但对于 5D(3D + 能量 + 时间)活体心脏成像来说,需要耗费大量的计算时间。
方法。
我们最近推出了一种深度学习(DL)方法 UnetU,它能在各种采集设置下对 4D(3D + 能量)PCCT 重建的轴切片进行精确去噪。在本研究中,我们探索了用于 5D 心脏 PCCT 去噪的 UnetU 配置,重点是沿能量和时间维度的奇异值分解 (SVD) 修正,以及 3D U-net、FastDVDNet 和 Swin Transformer UNet 等替代网络架构。
主要结果。
我们使用真实小鼠数据和数字 MOBY 幻影进行评估,结果显示所有 DL 方法都比迭代重建快 16 倍以上。使用 SVD 对能量维度进行去噪的 DL 方法最为有效,其均方根误差和参考熵差的时空缩小率都很低,而且与迭代重建的定性一致。在 5D 心脏 PCCT 重构中,能量维度的有效秩比时间维度的有效秩低,因此具有这种优势。ME NLM 的表现有时优于采用时间 SVD 或时间和能量 SVD 的 DL,但落后于采用能量 SVD 的迭代重建和 DL。
Significance.
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoication, providing a 32-fold speed increase from iterative reconstruction.这一进步为心血管成像中的 DL 应用树立了新的标杆。
{"title":"Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images.","authors":"Rohan Nadkarni, Darin P Clark, Alex Jeffrey Allphin, Cristian T Badea","doi":"10.1088/1361-6560/ad7fc6","DOIUrl":"https://doi.org/10.1088/1361-6560/ad7fc6","url":null,"abstract":"<p><strong>Objective: </strong>
Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. 

Approach. 
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. 

Main results. 
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). 

Significance. 
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142351977","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}
Pub Date : 2024-09-25DOI: 10.1088/1361-6560/ad7fc7
Thierry L Lefebvre, Paul W Sweeney, Janek Grohl, Lina Hacker, Emma L Brown, Thomas R Else, Mariam-Eleni Oraiopoulou, Algernon Bloom, David Y Lewis, Sarah E Bohndiek
Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.
{"title":"Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature.","authors":"Thierry L Lefebvre, Paul W Sweeney, Janek Grohl, Lina Hacker, Emma L Brown, Thomas R Else, Mariam-Eleni Oraiopoulou, Algernon Bloom, David Y Lewis, Sarah E Bohndiek","doi":"10.1088/1361-6560/ad7fc7","DOIUrl":"10.1088/1361-6560/ad7fc7","url":null,"abstract":"<p><p><b>Objective:</b>The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.<b>Approach:</b>Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.<b>Main results:</b>Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.<b>Significance:</b>We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142351991","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}
Pub Date : 2024-09-25DOI: 10.1088/1361-6560/ad6edc
Teresa Bernardo, Lena Heuchel, Feline Heinzelmann, Johannes Esser, Lutz Lüdemann, Beate Timmermann, Armin Lühr, Cläre von Neubeck
Objective.The energy deposition of photons and protons differs. It depends on the position in the proton Bragg peak (BP) and the linear energy transfer (LET) leading to a variable relative biological effectiveness (RBE). Here, we investigate LET dependent alterations on metabolic viability and proliferation of sarcoma and endothelium cell lines following proton irradiation in comparison to photon exposure.Approach.Using a multi-step range shifter, each column of a 96-well plate was positioned in a different depth along four BP curves with increasing intensities. The high-throughput experimental setup covers dose, LET, and RBE changes seen in a treatment field. Photon irradiation was performed to calculate the RBE along the BP curve. Two biological information out of one experiment were extracted allowing a correlation between metabolic viability and proliferation of the cells.Main results.The metabolic viability and cellular proliferation were column-wise altered showing a depth-dose profile. Endothelium cell viability recovers within 96 h post BP irradiation while sarcoma cell viability remains reduced. Highest RBE values were observed at the BP distal fall-off regarding proliferation of the sarcoma and endothelial cells.Significance.The high-throughput experimental setup introduced here (I) covers dose, LET, and RBE changes seen in a treatment field, (II) measures short-term effects within 48 h to 96 h post irradiation, and (III) can additionally be transferred to various cell types without time consuming experimental adaptations. Traditionally, RBE values are calculated from clonogenic cell survival. Measured RBE profiles strongly depend on physical characteristics such as dose and LET and biological characteristics for example cell type and time point. Metabolic viability and proliferation proofed to be in a similar effect range compared to clonogenic survival results. Based on limited data of combined irradiation with doxorubicin, future experiments will test combined treatment with systemic therapies applied in clinics e.g. cyclin-dependent inhibitors.
目的:光子和质子的能量沉积各不相同。它取决于质子布拉格峰(BP)的位置和线性能量传递(LET),从而导致不同的相对生物效应(RBE)。在此,我们研究了质子辐照与光子辐照相比,对肉瘤和内皮细胞系的代谢活力和增殖所产生的 LET 依赖性变化:使用多级范围转换器(MSRS),将 96 孔板中的每一列沿强度递增的四条 BP 曲线放置在不同深度。高通量实验装置涵盖了治疗场中的剂量、LET 和 RBE 变化。通过光子辐照计算 BP 曲线上的 RBE。从一次实验中提取了两个生物信息,从而将细胞的代谢活力和增殖联系起来:代谢活力和细胞增殖呈柱状变化,显示出深度-剂量曲线。内皮细胞的活力在 BP 照射后 96 小时内恢复,而肉瘤细胞的活力仍然下降。在肉瘤和内皮细胞增殖的 BP 远端落差处观察到了最高的 RBE 值:这里介绍的高通量实验装置 I) 涵盖了治疗场中的剂量、LET 和 RBE 变化;II) 可测量辐照后 48 至 96 小时内的短期效应;III) 还可用于各种细胞类型,而无需耗时的实验调整。传统上,RBE 值是通过克隆细胞存活率计算得出的。测得的 RBE 曲线在很大程度上取决于物理特性(如剂量和 LET)和生物特性(如细胞类型和时间点)。事实证明,代谢活力和增殖与克隆存活结果的影响范围相似。基于联合辐照与多柔比星的有限数据,未来的实验将测试与临床应用的系统疗法(如细胞周期蛋白依赖性抑制剂)联合治疗的效果。
{"title":"Linear energy transfer dependent variation in viability and proliferation along the Bragg peak curve in sarcoma and normal tissue cells.","authors":"Teresa Bernardo, Lena Heuchel, Feline Heinzelmann, Johannes Esser, Lutz Lüdemann, Beate Timmermann, Armin Lühr, Cläre von Neubeck","doi":"10.1088/1361-6560/ad6edc","DOIUrl":"10.1088/1361-6560/ad6edc","url":null,"abstract":"<p><p><i>Objective.</i>The energy deposition of photons and protons differs. It depends on the position in the proton Bragg peak (BP) and the linear energy transfer (LET) leading to a variable relative biological effectiveness (RBE). Here, we investigate LET dependent alterations on metabolic viability and proliferation of sarcoma and endothelium cell lines following proton irradiation in comparison to photon exposure.<i>Approach.</i>Using a multi-step range shifter, each column of a 96-well plate was positioned in a different depth along four BP curves with increasing intensities. The high-throughput experimental setup covers dose, LET, and RBE changes seen in a treatment field. Photon irradiation was performed to calculate the RBE along the BP curve. Two biological information out of one experiment were extracted allowing a correlation between metabolic viability and proliferation of the cells.<i>Main results.</i>The metabolic viability and cellular proliferation were column-wise altered showing a depth-dose profile. Endothelium cell viability recovers within 96 h post BP irradiation while sarcoma cell viability remains reduced. Highest RBE values were observed at the BP distal fall-off regarding proliferation of the sarcoma and endothelial cells.<i>Significance.</i>The high-throughput experimental setup introduced here (I) covers dose, LET, and RBE changes seen in a treatment field, (II) measures short-term effects within 48 h to 96 h post irradiation, and (III) can additionally be transferred to various cell types without time consuming experimental adaptations. Traditionally, RBE values are calculated from clonogenic cell survival. Measured RBE profiles strongly depend on physical characteristics such as dose and LET and biological characteristics for example cell type and time point. Metabolic viability and proliferation proofed to be in a similar effect range compared to clonogenic survival results. Based on limited data of combined irradiation with doxorubicin, future experiments will test combined treatment with systemic therapies applied in clinics e.g. cyclin-dependent inhibitors.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976366","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}