Pub Date : 2024-03-25DOI: 10.1109/TRPMS.2024.3379215
Md Ashequr Rahman;Zitong Yu;Richard Laforest;Craig K. Abbey;Barry A. Siegel;Abhinav K. Jha
There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners ( $N,,=$ 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.
{"title":"DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT","authors":"Md Ashequr Rahman;Zitong Yu;Richard Laforest;Craig K. Abbey;Barry A. Siegel;Abhinav K. Jha","doi":"10.1109/TRPMS.2024.3379215","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3379215","url":null,"abstract":"There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (\u0000<inline-formula> <tex-math>$N,,=$ </tex-math></inline-formula>\u0000 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"439-450"},"PeriodicalIF":4.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10477616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342755","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}
Pub Date : 2024-03-22DOI: 10.1109/TRPMS.2024.3403959
Ryosuke Ota;Kibo Ote
Bismuth germanate (BGO) has been receiving attention again because it is a potential scintillator for future time-of-flight positron emission tomography. Owing to its optical properties, BGO emits a relatively large number of Cherenkov photons after 511-keV gamma-ray interactions, which can enable good coincidence time resolution (CTR). Nonetheless, optimally exploiting the Cherenkov emissions can be confounded by scintillation emissions. Thus, we propose a method efficiently emphasizing Cherenkov photon from a detector waveform by deconvolving a single photon response of photodetector. As a proof-of-concept, we perform the deconvolution, and a probability density function (PDF) of BGO was obtained, which is compared to a conventional time correlated single photon counting (TCSPC) method. Furthermore, we investigate if the proposed deconvolution can emphasize a faint Cherenkov signal. Consequently, the PDF obtained by the proposed deconvolution shows a good agreement with that obtained using a conventional TCSPC methods. A CTR obtained using the proposed deconvolution is improved by 12% and 43% in full width at half maximum compared to a voltage-based leading edge discriminator for with and without high-frequency readout electronics, respectively. Thus, the proposed deconvolution method can efficiently emphasize Cherenkov photon by lowering the threshold level and improve the timing performance of BGO-based detectors.
锗酸铋（BGO）再次受到关注，因为它是未来飞行时间正电子发射断层扫描的潜在闪烁体。由于其光学特性，锗酸铋在与 511-keV 伽马射线相互作用后会发出相对较多的切伦科夫光子，从而实现良好的重合时间分辨率（CTR）。然而，最佳利用切伦科夫发射可能会受到闪烁发射的干扰。因此，我们提出了一种方法，通过对光电探测器的单光子响应进行解卷积，从探测器波形中有效地强调切伦科夫光子。作为概念验证，我们进行了解卷积，得到了 BGO 的概率密度函数（PDF），并与传统的时间相关单光子计数（TCSPC）方法进行了比较。此外，我们还研究了拟议的解卷积是否能突出微弱的切伦科夫信号。结果表明，拟议解卷积得到的 PDF 与传统 TCSPC 方法得到的 PDF 非常吻合。与基于电压的前沿鉴别器相比，在有高频读出电子设备和无高频读出电子设备的情况下，利用拟议解卷积法获得的 CTR 在半最大全宽方面分别提高了 12% 和 43%。因此，建议的解卷积方法可以通过降低阈值水平有效地强调切伦科夫光子，并改善基于 BGO 的探测器的计时性能。
{"title":"Emphasizing Cherenkov Photons From Bismuth Germanate by Single Photon Response Deconvolution","authors":"Ryosuke Ota;Kibo Ote","doi":"10.1109/TRPMS.2024.3403959","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3403959","url":null,"abstract":"Bismuth germanate (BGO) has been receiving attention again because it is a potential scintillator for future time-of-flight positron emission tomography. Owing to its optical properties, BGO emits a relatively large number of Cherenkov photons after 511-keV gamma-ray interactions, which can enable good coincidence time resolution (CTR). Nonetheless, optimally exploiting the Cherenkov emissions can be confounded by scintillation emissions. Thus, we propose a method efficiently emphasizing Cherenkov photon from a detector waveform by deconvolving a single photon response of photodetector. As a proof-of-concept, we perform the deconvolution, and a probability density function (PDF) of BGO was obtained, which is compared to a conventional time correlated single photon counting (TCSPC) method. Furthermore, we investigate if the proposed deconvolution can emphasize a faint Cherenkov signal. Consequently, the PDF obtained by the proposed deconvolution shows a good agreement with that obtained using a conventional TCSPC methods. A CTR obtained using the proposed deconvolution is improved by 12% and 43% in full width at half maximum compared to a voltage-based leading edge discriminator for with and without high-frequency readout electronics, respectively. Thus, the proposed deconvolution method can efficiently emphasize Cherenkov photon by lowering the threshold level and improve the timing performance of BGO-based detectors.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"595-606"},"PeriodicalIF":4.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500389","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}
Pub Date : 2024-03-21DOI: 10.1109/TRPMS.2024.3380090
S. M. A. Sharif;Rizwan Ali Naqvi;Woong-Kee Loh
Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often generate visual artifacts when applied to heterogeneous medical images. This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (AI)-driven two-stage learning strategy. The proposed method learns to estimate the residual noise from the noisy images. Later, it incorporates a novel noise attention mechanism to correlate estimated residual noise with noisy inputs to perform denoising in a course-to-refine manner. This study also proposes to leverage a multimodal learning strategy to generalize the denoising among medical image modalities and multiple noise patterns for widespread applications. The practicability of the proposed method has been evaluated with dense experiments. The experimental results demonstrated that the proposed method achieved state-of-the-art performance by significantly outperforming the existing medical image denoising methods in quantitative and qualitative comparisons. Overall, it illustrates a performance gain of 7.64 in peak signal-to-noise ratio (PSNR), 0.1021 in structural similarity index (SSIM), 0.80 in DeltaE $(Delta E)$ , 0.1855 in visual information fidelity pixelwise (VIFP), and 18.54 in mean squared error (MSE) metrics.
{"title":"Two-Stage Deep Denoising With Self-Guided Noise Attention for Multimodal Medical Images","authors":"S. M. A. Sharif;Rizwan Ali Naqvi;Woong-Kee Loh","doi":"10.1109/TRPMS.2024.3380090","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3380090","url":null,"abstract":"Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often generate visual artifacts when applied to heterogeneous medical images. This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (AI)-driven two-stage learning strategy. The proposed method learns to estimate the residual noise from the noisy images. Later, it incorporates a novel noise attention mechanism to correlate estimated residual noise with noisy inputs to perform denoising in a course-to-refine manner. This study also proposes to leverage a multimodal learning strategy to generalize the denoising among medical image modalities and multiple noise patterns for widespread applications. The practicability of the proposed method has been evaluated with dense experiments. The experimental results demonstrated that the proposed method achieved state-of-the-art performance by significantly outperforming the existing medical image denoising methods in quantitative and qualitative comparisons. Overall, it illustrates a performance gain of 7.64 in peak signal-to-noise ratio (PSNR), 0.1021 in structural similarity index (SSIM), 0.80 in DeltaE \u0000<inline-formula> <tex-math>$(Delta E)$ </tex-math></inline-formula>\u0000, 0.1855 in visual information fidelity pixelwise (VIFP), and 18.54 in mean squared error (MSE) metrics.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"521-531"},"PeriodicalIF":4.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820221","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}
In the planning phase of radiation therapy, positron emission tomography (PET) images are frequently integrated with computed tomography (CT) and MRI to accurately delineate the target region for treatment. However, obtaining additional CT or magnetic resonance (MR) images solely for localization purposes proves to be financially burdensome, time-intensive, and may increase patient radiation exposure. To alleviate these issues, a deep learning model with dynamic modality translation capabilities is introduced. This approach is achieved through the incorporation of adaptive modality translation layers within the decoder module. The adaptive modality translation layer effectively governs modality transformation by reshaping the data distribution of features extracted by the encoder using switch codes. The model’s performance is assessed on images with reference images using evaluation metrics, such as peak signal-to-noise ratio, structural similarity index measure, and normalized mean square error. For results without reference images, subjective assessments are provided by six nuclear medicine physicians based on clinical interpretations. The proposed model demonstrates impressive performance in transforming nonattenuation corrected PET images into user-specified modalities (attenuation corrected PET, MR, or CT), effectively streamlining the acquisition of supplemental modality images in radiation therapy scenarios.
在放射治疗的计划阶段，正电子发射断层扫描（PET）图像经常与计算机断层扫描（CT）和磁共振成像（MRI）结合使用，以准确划定治疗靶区。然而，仅为定位目的而获取额外的 CT 或磁共振（MR）图像不仅经济负担重、耗时长，还可能增加患者的辐射量。为了缓解这些问题，我们引入了一种具有动态模式转换能力的深度学习模型。这种方法是通过在解码器模块中加入自适应模态转换层来实现的。自适应模态转换层通过重塑编码器使用开关代码提取的特征的数据分布，有效控制模态转换。利用峰值信噪比、结构相似性指数测量和归一化均方误差等评估指标，对带有参考图像的图像进行模型性能评估。对于无参考图像的结果，则由六位核医学医生根据临床解释进行主观评估。所提出的模型在将非衰减校正 PET 图像转换为用户指定模式（衰减校正 PET、MR 或 CT）方面表现出色，有效简化了放射治疗场景中补充模式图像的获取。
{"title":"Medical Multimodal Image Transformation With Modality Code Awareness","authors":"Zhihua Li;Yuxi Jin;Qingneng Li;Zhenxing Huang;Zixiang Chen;Chao Zhou;Na Zhang;Xu Zhang;Wei Fan;Jianmin Yuan;Qiang He;Weiguang Zhang;Dong Liang;Zhanli Hu","doi":"10.1109/TRPMS.2024.3379580","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3379580","url":null,"abstract":"In the planning phase of radiation therapy, positron emission tomography (PET) images are frequently integrated with computed tomography (CT) and MRI to accurately delineate the target region for treatment. However, obtaining additional CT or magnetic resonance (MR) images solely for localization purposes proves to be financially burdensome, time-intensive, and may increase patient radiation exposure. To alleviate these issues, a deep learning model with dynamic modality translation capabilities is introduced. This approach is achieved through the incorporation of adaptive modality translation layers within the decoder module. The adaptive modality translation layer effectively governs modality transformation by reshaping the data distribution of features extracted by the encoder using switch codes. The model’s performance is assessed on images with reference images using evaluation metrics, such as peak signal-to-noise ratio, structural similarity index measure, and normalized mean square error. For results without reference images, subjective assessments are provided by six nuclear medicine physicians based on clinical interpretations. The proposed model demonstrates impressive performance in transforming nonattenuation corrected PET images into user-specified modalities (attenuation corrected PET, MR, or CT), effectively streamlining the acquisition of supplemental modality images in radiation therapy scenarios.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"511-520"},"PeriodicalIF":4.4,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820224","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}
Pub Date : 2024-03-18DOI: 10.1109/TRPMS.2024.3378421
S. Di Giacomo;M. Ronchi;G. Borghi;D. R. Schaart;M. Carminati;C. Fiorini
Embedding signal processing in the front-end of radiation detectors represents an approach to cope with the growing complexity of nuclear imaging scanners with increasing field of view (i.e., higher number of channels). Machine learning (ML) offers a good compromise between intrinsic image reconstruction performance and computational power. While most hardware accelerators for ML are based on digital circuits and, thus, require the analog-to-digital conversion of all individual signals from photodetectors, an analog approach allows to streamline the pipeline. We present the study of an analog accelerator implementing a neural network (NN) with 42 neurons in a 0.35- ${mu }$ m CMOS process node. The specific target is the reconstruction of the position of interaction of gamma-rays in the scintillator crystal of Anger cameras used for PET and SPECT. This chip can be used stand-alone or monolithically integrated within the application specific integrated circuit (ASIC) for the filtering of current signals from arrays of silicon photomultipliers (SiPMs). Computation is performed in charge domain by means of crossbar arrays of programmable capacitor. The architecture of the 64-input ASIC and the training of the NN are presented, discussing the impact of weight quantization on 5 bits. From MATLAB and circuit simulations, consistent with ASIC topology and operations, the NN capabilities were tested using two different datasets, obtained from both simulated data and experimental data, both based on PET detector composed by a monolithic scintillator crystal readout by an $8times 8$ array of SiPMs. Simulations show an achievable spatial resolution better than 2-mm full-width-at-half-maximum with a 10-mm thick crystal, a max. count rate of 200kHz and the energy efficiency per inference is estimated to be of 93.5GOP/J, i.e., competitive with digital counterparts, with an energy consumption of 38nJ per inference and area of 23mm2.
随着视野范围的不断扩大（即通道数增加），核成像扫描仪的复杂性也在不断增加，将信号处理嵌入辐射探测器前端是应对这种情况的一种方法。机器学习（ML）是内在图像重建性能和计算能力之间的良好折衷。虽然大多数用于 ML 的硬件加速器都基于数字电路，因此需要对来自光电探测器的所有单独信号进行模数转换，但模拟方法可以简化流水线。我们介绍了对模拟加速器的研究，该加速器在 0.35- ${mu }$ m CMOS 工艺节点上实现了一个拥有 42 个神经元的神经网络 (NN)。具体目标是重建用于 PET 和 SPECT 的 Anger 相机闪烁晶体中伽马射线相互作用的位置。该芯片可独立使用，也可单片集成在专用集成电路（ASIC）中，用于过滤硅光电倍增管（SiPM）阵列发出的电流信号。计算是通过可编程电容器横条阵列在电荷域中进行的。介绍了 64 输入 ASIC 的结构和 NN 的训练，讨论了权重量化对 5 位的影响。根据 MATLAB 和电路仿真（与 ASIC 拓扑结构和操作一致），使用两个不同的数据集测试了 NN 的能力，这两个数据集均来自模拟数据和实验数据，均基于由单片闪烁晶体组成的 PET 检测器，该检测器由 8 美元/次的 SiPM 阵列读出。模拟结果表明，使用 10 毫米厚的晶体，可实现优于 2 毫米全宽半最大值的空间分辨率，最大计数率为 200kHz，每次推理的能效估计为 93.5GOP/J，即与数字同行相比具有竞争力，每次推理的能耗为 38nJ，面积为 23 平方毫米。
{"title":"Implementing an Integrated Neural Network for Real-Time Position Reconstruction in Emission Tomography With Monolithic Scintillators","authors":"S. Di Giacomo;M. Ronchi;G. Borghi;D. R. Schaart;M. Carminati;C. Fiorini","doi":"10.1109/TRPMS.2024.3378421","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3378421","url":null,"abstract":"Embedding signal processing in the front-end of radiation detectors represents an approach to cope with the growing complexity of nuclear imaging scanners with increasing field of view (i.e., higher number of channels). Machine learning (ML) offers a good compromise between intrinsic image reconstruction performance and computational power. While most hardware accelerators for ML are based on digital circuits and, thus, require the analog-to-digital conversion of all individual signals from photodetectors, an analog approach allows to streamline the pipeline. We present the study of an analog accelerator implementing a neural network (NN) with 42 neurons in a 0.35-\u0000<inline-formula> <tex-math>${mu }$ </tex-math></inline-formula>\u0000m CMOS process node. The specific target is the reconstruction of the position of interaction of gamma-rays in the scintillator crystal of Anger cameras used for PET and SPECT. This chip can be used stand-alone or monolithically integrated within the application specific integrated circuit (ASIC) for the filtering of current signals from arrays of silicon photomultipliers (SiPMs). Computation is performed in charge domain by means of crossbar arrays of programmable capacitor. The architecture of the 64-input ASIC and the training of the NN are presented, discussing the impact of weight quantization on 5 bits. From MATLAB and circuit simulations, consistent with ASIC topology and operations, the NN capabilities were tested using two different datasets, obtained from both simulated data and experimental data, both based on PET detector composed by a monolithic scintillator crystal readout by an \u0000<inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula>\u0000 array of SiPMs. Simulations show an achievable spatial resolution better than 2-mm full-width-at-half-maximum with a 10-mm thick crystal, a max. count rate of 200kHz and the energy efficiency per inference is estimated to be of 93.5GOP/J, i.e., competitive with digital counterparts, with an energy consumption of 38nJ per inference and area of 23mm2.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"501-510"},"PeriodicalIF":4.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820220","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}
Pub Date : 2024-03-17DOI: 10.1109/TRPMS.2024.3402272
Zixiang Chen;Yuxi Jin;Zhenxing Huang;Na Zhang;Kaiyi Liang;Guotao Quan;Dong Liang;Hairong Zheng;Zhanli Hu
Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from the low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both the simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in the clinical low-dose PCT imaging tasks.
{"title":"Building a Kinetic Induced Voxel-Clustering Filter (KVCF) for Low-Dose Perfusion CT Imaging","authors":"Zixiang Chen;Yuxi Jin;Zhenxing Huang;Na Zhang;Kaiyi Liang;Guotao Quan;Dong Liang;Hairong Zheng;Zhanli Hu","doi":"10.1109/TRPMS.2024.3402272","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3402272","url":null,"abstract":"Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from the low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both the simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in the clinical low-dose PCT imaging tasks.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"762-773"},"PeriodicalIF":4.6,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143650","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}
Pub Date : 2024-03-15DOI: 10.1109/TRPMS.2024.3401391
Riccardo Latella;Antonio J. Gonzalez;José M. Benlloch;Paul Lecoq;Georgios Konstantinou
The signal-to-noise ratio in positron emission tomography (PET) improves with precise timing resolution. PET systems enabling the capability of time-of-flight (ToF) are nowadays available. This study assesses various data configurations, comparing the obtained timing performances applicable to time-of-flight positron emission tomography (ToF-PET) systems. Different readout configurations were evaluated together with silicon photomultipliers (SiPMs) photosensors from the Fondazione Bruno Kessler (FBK), with and without the so-called metal trench (MT) technology. The tests were carried out with scintillation crystals of $3times 3times $ 5 mm 3 (LYSO:Ce,Ca) from SIPAT. Two onboard FPGA-based systems, namely, the Felix time-to-digital converter (TDC) from Tediel S.r.l. and the ASIC-based FastIC from the University of Barcelona, along with custom-made high-frequency electronics (CM-HF), were compared. Considering only photopeak events, the best-coincidence timing resolution (CTR) results obtained were 71 ps with the MT SiPMs. This result worsened to 88 ps with the old version of the same device that does not include the MT technology (called HD). The results demonstrate substantial CTR improvements when MT SiPMs were used across the different scenarios, resulting in a timing improvement in the 10 to 45-ps range compared to HD SiPMs. Notably, the Felix TDC achieved sub-100-ps timing results, emphasizing the potential of FPGA technology in ToF-PET applications. Moreover, the fully passive version of the CM-HF connected to the MT SiPMs shows only a degradation of 8-ps difference compared to the version using amplifiers. The novel MT-type SiPMs promise superior timing performance, enhancing accuracy and efficiency in PET imaging systems.
{"title":"Comparative Analysis of Data Acquisition Setups for Fast Timing in ToF-PET Applications","authors":"Riccardo Latella;Antonio J. Gonzalez;José M. Benlloch;Paul Lecoq;Georgios Konstantinou","doi":"10.1109/TRPMS.2024.3401391","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3401391","url":null,"abstract":"The signal-to-noise ratio in positron emission tomography (PET) improves with precise timing resolution. PET systems enabling the capability of time-of-flight (ToF) are nowadays available. This study assesses various data configurations, comparing the obtained timing performances applicable to time-of-flight positron emission tomography (ToF-PET) systems. Different readout configurations were evaluated together with silicon photomultipliers (SiPMs) photosensors from the Fondazione Bruno Kessler (FBK), with and without the so-called metal trench (MT) technology. The tests were carried out with scintillation crystals of \u0000<inline-formula> <tex-math>$3times 3times $ </tex-math></inline-formula>\u00005 mm\u0000<sup>3</sup>\u0000 (LYSO:Ce,Ca) from SIPAT. Two onboard FPGA-based systems, namely, the Felix time-to-digital converter (TDC) from Tediel S.r.l. and the ASIC-based FastIC from the University of Barcelona, along with custom-made high-frequency electronics (CM-HF), were compared. Considering only photopeak events, the best-coincidence timing resolution (CTR) results obtained were 71 ps with the MT SiPMs. This result worsened to 88 ps with the old version of the same device that does not include the MT technology (called HD). The results demonstrate substantial CTR improvements when MT SiPMs were used across the different scenarios, resulting in a timing improvement in the 10 to 45-ps range compared to HD SiPMs. Notably, the Felix TDC achieved sub-100-ps timing results, emphasizing the potential of FPGA technology in ToF-PET applications. Moreover, the fully passive version of the CM-HF connected to the MT SiPMs shows only a degradation of 8-ps difference compared to the version using amplifiers. The novel MT-type SiPMs promise superior timing performance, enhancing accuracy and efficiency in PET imaging systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"743-751"},"PeriodicalIF":4.6,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143677","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}
Molecular radiotherapy is a treatment modality that requires personalized dosimetry for efficient treatment and reduced toxicity. Current clinical imaging systems and miniaturized gamma-cameras lack the necessary features for this task. In this article, we present the design and optimization of a mobile gamma-camera with a $10times 10$ cm2 field of view tailored for quantitative imaging during $^{131}text{I}$ therapy of thyroid diseases. The camera uses a monolithic $10times 10times 1$ cm3 CeBr3 scintillator coupled to a $16times 16$ SiPMs array and commercial electronics. It exhibits high imaging performance with an intrinsic spatial resolution (SR) of 1.15-mm FWHM, an energy resolution of 8% FWHM at 356 keV and negligible deadtime up to 150 kcps. Images are reconstructed in real time using a convolutional neural network. The manufacturing method of tungsten collimators and shielding was optimized using laser 3-D printing to achieve an effective density of 97% that of bulk tungsten. Their geometry was adjusted with Monte-Carlo simulations in order to reduce septal penetration and scattering and optimize the signal-to-noise ratio at short times after treatment administration. Two high-energy parallel-hole collimators with high sensitivity or very high SR were designed for treatment planning and post-treatment control. The fully operational gamma-camera will soon be clinically assessed.
{"title":"A High-Resolution Portable Gamma-Camera for Estimation of Absorbed Dose in Molecular Radiotherapy","authors":"T. Bossis;M.-A. Verdier;C. Trigila;L. Pinot;F. Bouvet;A. Blot;H. Ramarijaona;T. Beaumont;D. Broggio;O. Caselles;S. Zerdoud;L. Ménard","doi":"10.1109/TRPMS.2024.3376826","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3376826","url":null,"abstract":"Molecular radiotherapy is a treatment modality that requires personalized dosimetry for efficient treatment and reduced toxicity. Current clinical imaging systems and miniaturized gamma-cameras lack the necessary features for this task. In this article, we present the design and optimization of a mobile gamma-camera with a \u0000<inline-formula> <tex-math>$10times 10$ </tex-math></inline-formula>\u0000 cm2 field of view tailored for quantitative imaging during \u0000<inline-formula> <tex-math>$^{131}text{I}$ </tex-math></inline-formula>\u0000 therapy of thyroid diseases. The camera uses a monolithic \u0000<inline-formula> <tex-math>$10times 10times 1$ </tex-math></inline-formula>\u0000 cm3 CeBr3 scintillator coupled to a \u0000<inline-formula> <tex-math>$16times 16$ </tex-math></inline-formula>\u0000 SiPMs array and commercial electronics. It exhibits high imaging performance with an intrinsic spatial resolution (SR) of 1.15-mm FWHM, an energy resolution of 8% FWHM at 356 keV and negligible deadtime up to 150 kcps. Images are reconstructed in real time using a convolutional neural network. The manufacturing method of tungsten collimators and shielding was optimized using laser 3-D printing to achieve an effective density of 97% that of bulk tungsten. Their geometry was adjusted with Monte-Carlo simulations in order to reduce septal penetration and scattering and optimize the signal-to-noise ratio at short times after treatment administration. Two high-energy parallel-hole collimators with high sensitivity or very high SR were designed for treatment planning and post-treatment control. The fully operational gamma-camera will soon be clinically assessed.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"556-570"},"PeriodicalIF":4.4,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10472319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820334","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}
X-ray spectrum plays an essential role in CT applications. Since it is difficult to measure X-ray spectrum directly in practice, X-ray spectrum is always indirectly obtained by using transmission measurements through a calibration phantom of known thickness and materials. These methods are independent of CT image reconstruction and bring extra cost. To solve this problem, we propose a parametric physical model-based X-ray spectrum estimation algorithm for CT imaging. First, an X-ray spectrum model with six parameters is proposed, which is derived from the X-ray imaging physics. Second, a template image containing different material components can be obtained by segmenting CT reconstructed images with a simple method. And the estimated projections can be calculated by reprojecting the template image with the proposed spectrum model. Finally, the six model parameters can be solved by iteratively minimizing the error between the estimated projection and real measurements. The effectiveness of the proposed method has been validated on both simulated and real data. Experimental results demonstrate that the proposed method can estimate the accurate spectra at different energies and provide a good reconstruction of characteristic radiations without extra cost.
X 射线光谱在 CT 应用中起着至关重要的作用。由于在实践中很难直接测量 X 射线频谱，因此总是通过已知厚度和材料的校准模型进行透射测量，从而间接获得 X 射线频谱。这些方法与 CT 图像重建无关，而且会带来额外成本。为解决这一问题，我们提出了一种基于参数物理模型的 CT 成像 X 射线光谱估算算法。首先，我们从 X 射线成像物理学出发，提出了具有六个参数的 X 射线光谱模型。其次，可以通过简单的方法分割 CT 重建图像，获得包含不同材料成分的模板图像。然后，利用所提出的光谱模型对模板图像进行再投影，即可计算出估计投影。最后，通过迭代最小化估计投影与实际测量之间的误差，即可求解六个模型参数。建议方法的有效性已在模拟数据和真实数据上得到验证。实验结果表明，所提出的方法可以估算出不同能量下的精确光谱，并在不增加额外成本的情况下提供良好的特征辐射重建。
{"title":"A Parametric Physical Model-Based X-Ray Spectrum Estimation Approach for CT Imaging","authors":"Shaojie Chang;Chaoyang Zhang;Xuanqin Mou;Qiong Xu;Lijun He;Xi Chen","doi":"10.1109/TRPMS.2024.3374702","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3374702","url":null,"abstract":"X-ray spectrum plays an essential role in CT applications. Since it is difficult to measure X-ray spectrum directly in practice, X-ray spectrum is always indirectly obtained by using transmission measurements through a calibration phantom of known thickness and materials. These methods are independent of CT image reconstruction and bring extra cost. To solve this problem, we propose a parametric physical model-based X-ray spectrum estimation algorithm for CT imaging. First, an X-ray spectrum model with six parameters is proposed, which is derived from the X-ray imaging physics. Second, a template image containing different material components can be obtained by segmenting CT reconstructed images with a simple method. And the estimated projections can be calculated by reprojecting the template image with the proposed spectrum model. Finally, the six model parameters can be solved by iteratively minimizing the error between the estimated projection and real measurements. The effectiveness of the proposed method has been validated on both simulated and real data. Experimental results demonstrate that the proposed method can estimate the accurate spectra at different energies and provide a good reconstruction of characteristic radiations without extra cost.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"532-539"},"PeriodicalIF":4.4,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820222","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}
Pub Date : 2024-03-12DOI: 10.1109/TRPMS.2024.3368802
Georgios Konstantinou;Lei Zhang;Daniel Bonifacio;Riccardo Latella;Jose Maria Benlloch;Antonio J. Gonzalez;Paul Lecoq
In this study, we propose and examine a unique semimonolithic metascintillator (SMMS) detector design, where slow scintillators (BGO or LYSO) are split into thin slabs and read by an array of SiPM, offering depth-of-interaction (DOI) information. These are alternated with thin segmented fast scintillators (plastic EJ232 or EJ232Q), also read by single SiPMs, which provides pixel-level coincidence time resolution (CTR). The structure combines layers of slow scintillators of size $0.3times 25.5times $ (15 or 24) mm3 with fast scintillators of size $0.1times 3.1times $ (15 or 24) mm3. We use a Monte Carlo Gate simulation to gauge this novel semimonolithic detector’s performance. We found that the time resolution of SMMS is comparable to pixelated metascintillator designs with the same materials. For example, a 15-mm deep LYSO-based SMMS yielded a CTR of 121 ps before applying timewalk correction (after correction, 107-ps CTR). The equivalent BGO-based SMMS presented a CTR of 241 ps, which is a 15% divergence from metascintillator pixel experimental findings from previous works. We also applied neural networks to the photon distributions and timestamps recorded at the SiPM array, following guidelines on semimonolithic detectors. This led to determining the DOI with less than 3-mm precision and a confidence level of 0.85 in the best case, plus more than 2 standard deviations accuracy in reconstructing energy sharing and interaction energy. In summary, neural network prediction capabilities outperform standard energy calculation methods or any analytical approach on energy sharing, thanks to the improved understanding of photon distribution.
{"title":"Semi-Monolithic Meta-Scintillator Simulation Proof-of-Concept, Combining Accurate DOI and TOF","authors":"Georgios Konstantinou;Lei Zhang;Daniel Bonifacio;Riccardo Latella;Jose Maria Benlloch;Antonio J. Gonzalez;Paul Lecoq","doi":"10.1109/TRPMS.2024.3368802","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3368802","url":null,"abstract":"In this study, we propose and examine a unique semimonolithic metascintillator (SMMS) detector design, where slow scintillators (BGO or LYSO) are split into thin slabs and read by an array of SiPM, offering depth-of-interaction (DOI) information. These are alternated with thin segmented fast scintillators (plastic EJ232 or EJ232Q), also read by single SiPMs, which provides pixel-level coincidence time resolution (CTR). The structure combines layers of slow scintillators of size \u0000<inline-formula> <tex-math>$0.3times 25.5times $ </tex-math></inline-formula>\u0000 (15 or 24) mm3 with fast scintillators of size \u0000<inline-formula> <tex-math>$0.1times 3.1times $ </tex-math></inline-formula>\u0000(15 or 24) mm3. We use a Monte Carlo Gate simulation to gauge this novel semimonolithic detector’s performance. We found that the time resolution of SMMS is comparable to pixelated metascintillator designs with the same materials. For example, a 15-mm deep LYSO-based SMMS yielded a CTR of 121 ps before applying timewalk correction (after correction, 107-ps CTR). The equivalent BGO-based SMMS presented a CTR of 241 ps, which is a 15% divergence from metascintillator pixel experimental findings from previous works. We also applied neural networks to the photon distributions and timestamps recorded at the SiPM array, following guidelines on semimonolithic detectors. This led to determining the DOI with less than 3-mm precision and a confidence level of 0.85 in the best case, plus more than 2 standard deviations accuracy in reconstructing energy sharing and interaction energy. In summary, neural network prediction capabilities outperform standard energy calculation methods or any analytical approach on energy sharing, thanks to the improved understanding of photon distribution.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"482-492"},"PeriodicalIF":4.4,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820427","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}
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