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A Multiscale Spatial Transformer U-Net for Simultaneously Automatic Reorientation and Segmentation of 3-D Nuclear Cardiac Images 用于同时自动调整三维核素心脏图像方向和分割的多尺度空间变换器 U-Net
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-04-01 DOI: 10.1109/TRPMS.2024.3382318
Yangfan Ni;Duo Zhang;Gege Ma;Fan Rao;Yuanfeng Wu;Lijun Lu;Zhongke Huang;Wentao Zhu
Accurate reorientation and segmentation of the left ventricular (LV) is essential for the quantitative analysis of myocardial perfusion imaging (MPI). This study proposes an end-to-end model, named as multiscale spatial transformer UNet (MS-ST-UNet), which involves the multiscale spatial transformer network (MSSTN) and multiscale UNet (MSUNet) modules to perform simultaneous reorientation and segmentation of LV region from nuclear cardiac images. The multiscale sampler produces images with varying resolutions, while scale transformer (ST) blocks are employed to align the scales of features. The proposed method is trained and tested using two different nuclear cardiac image modalities: $^{13}text{N}$ -ammonia positron emission tomography (PET) and $^{99m}$ Tc-sestamibi single-photon emission computed tomography (SPECT). MS-ST-UNet attains dice similarity coefficient (DSC) scores of 91.48% and 94.81% for PET LV myocardium (LV-MY) and SPECT LV-MY, respectively. Additionally, the mean-square error (MSE) between predicted rigid registration parameters and ground truth decreases to below $1.4 times 10^{-2}$ . The experimental findings indicate that the MS-ST-UNet yields notably reduced registration errors and more precise boundary detection for the LV structure compared to existing methods. This joint learning framework promotes mutual enhancement between reorientation and segmentation tasks, leading to cutting edge performance and an efficient image processing workflow.
左心室(LV)的准确重新定向和分割对于心肌灌注成像(MPI)的定量分析至关重要。本研究提出了一种名为多尺度空间变换器 UNet(MS-ST-UNet)的端到端模型,该模型包括多尺度空间变换器网络(MSSTN)和多尺度 UNet(MSUNet)模块,可同时对心脏核图像中的左心室区域进行重新定向和分割。多尺度采样器生成不同分辨率的图像,而尺度变换器(ST)模块则用于对齐特征的尺度。使用两种不同的心脏核图像模式对所提出的方法进行了训练和测试:$^{13}text{N}$ -氨正电子发射断层扫描(PET)和$^{99m}$ Tc-sestamibi单光子发射计算机断层扫描(SPECT)。MS-ST-UNet 对 PET 左心室心肌(LV-MY)和 SPECT 左心室心肌的骰子相似系数(DSC)分别达到 91.48% 和 94.81%。此外,预测的刚性配准参数与地面实况之间的均方误差(MSE)减小到 1.4 (times 10^{-2}$ )以下。实验结果表明,与现有方法相比,MS-ST-UNet 能显著减少配准误差,并能更精确地检测 LV 结构的边界。这种联合学习框架促进了调整方向和分割任务之间的相互增强,从而实现了最先进的性能和高效的图像处理工作流程。
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引用次数: 0
Experimental Uses of Positronium and Potential for Biological Applications 正电子的实验用途和生物应用潜力
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-03-31 DOI: 10.1109/TRPMS.2024.3407981
A. Hourlier;F. Boisson;D. Brasse
Positrons are widely used in molecular imaging through the positron emission tomography (PET) imaging technique. However PET only reconstruct the distribution of the positron emitting radioisotopes, and because the $beta ^{+}$ isotopes are linked to a vector molecule, the distribution of $beta ^{+}$ isotopes is correlated to the distribution of a given biological function. Positron-electron annihilation can transit through a meta-stable called positronium, which can exist in two spin states: 1) the single state—parapositronium and 2) the triplet state—orthopositronium. The orthopositronium lifetime $(tau _{mathrm {oPs}})$ , formation probabilities and decay modes are sensitive to the physical and chemical state of the neighboring medium and could therefore provide information on the tissues themselves during a PET acquisition. However, traditional PET only relies on the detection of the two annihilation photons, therefore the lifetime and annihilation higher-multiplicity annihilations are not accessible to such PET paradigm. This review will present some of the use cases of positronium as a specific signature for event selection in astrophysics and particle physics, and as a probe for the microscopic state of materials and tissues. These usages of positronium highlight the interest for positronium for diagnostic in medical science, the projects for using positronium in upcoming PET tomographs are then presented.
正电子通过正电子发射断层扫描(PET)成像技术广泛应用于分子成像。然而,PET 只能重建正电子发射放射性同位素的分布,由于 $beta ^{+}$同位素与矢量分子相连,因此 $beta ^{+}$同位素的分布与特定生物功能的分布相关。正电子-电子湮灭可以通过一种叫做正电子的元稳态,它可以以两种自旋态存在:1) 单态-正电子鎓(parapositronium)和 2) 三重态-正电子鎓(orthopositronium)。正电子寿命$(tau _mathrm {oPs}})$、形成概率和衰变模式对邻近介质的物理和化学状态非常敏感,因此可以在 PET 采集过程中提供有关组织本身的信息。然而,传统的正电子发射计算机只能检测到两个湮灭光子,因此这种正电子发射计算机范例无法获得寿命和湮灭的高倍率湮灭。本综述将介绍正电子作为天体物理学和粒子物理学事件选择的特定特征,以及作为材料和组织微观状态探针的一些应用案例。正氚的这些用途凸显了正氚在医学诊断中的重要性,随后将介绍在即将推出的正电子发射计算机断层成像仪中使用正氚的项目。
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引用次数: 0
Temporal Image Sequence Separation in Dual-Tracer Dynamic PET With an Invertible Network 利用可逆网络在双踪动态正电子发射计算机中进行时态图像序列分离
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-03-30 DOI: 10.1109/TRPMS.2024.3407120
Chuanfu Sun;Bin Huang;Jie Sun;Yangfan Ni;Huafeng Liu;Qian Xia;Qiegen Liu;Wentao Zhu
Positron emission tomography (PET) is a widely used functional imaging technique in clinic. Compared to single-tracer PET, dual-tracer dynamic PET allows two sequences of different nuclear pharmaceuticals in one scan, revealing richer physiological information. However, dynamically separating the mixed signals in dual-tracer PET is challenging due to identical energy ~511 keV in gamma photon pairs from both tracers. We propose a method for dynamic PET dual-tracer separation based on invertible neural networks (DTS-INNs). This network enables the forward and backward process simultaneously. Therefore, producing the mixed image sequences from the separation results through backward process may impose extra constraints for optimizing the network. The loss is composed of two components corresponding to the forward and backward propagation processes, which results in more accurate gradient computations and more stable gradient propagation with cycle consistency. We assess our model’s performance using simulated and real data, comparing it with several reputable dual-tracer separation methods. The results of DTS-INN outperform counterparts with lower-mean square error, higher-structural similarity, and peak signal to noise ratio. Additionally, it exhibits robustness against noise levels, phantoms, tracer combinations, and scanning protocols, offering a dependable solution for dual-tracer PET image separation.
正电子发射断层扫描(PET)是一种广泛应用于临床的功能成像技术。与单示踪剂正电子发射计算机断层成像相比,双示踪剂动态正电子发射计算机断层成像允许在一次扫描中使用两种不同的核药物序列,从而揭示更丰富的生理信息。然而,由于两种示踪剂的伽马光子对能量(约 511 keV)相同,动态分离双示踪剂 PET 中的混合信号具有挑战性。我们提出了一种基于可逆神经网络(DTS-INN)的动态 PET 双示踪剂分离方法。该网络可同时进行前向和后向处理。因此,通过后向处理根据分离结果生成混合图像序列可能会对网络的优化造成额外的限制。损耗由对应于前向和后向传播过程的两个部分组成,这使得梯度计算更加精确,梯度传播更加稳定,并具有周期一致性。我们使用模拟数据和真实数据评估了我们模型的性能,并将其与几种著名的双踪分离方法进行了比较。DTS-INN 的结果以更低的均方误差、更高的结构相似性和峰值信噪比优于同类方法。此外,DTS-INN 对噪音水平、模型、示踪剂组合和扫描方案都有很好的适应性,为双示踪剂 PET 图像分离提供了可靠的解决方案。
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引用次数: 0
Comparison of Timing Measurement Methods of Dual-Ended Readout Scintillator Array PET Detectors 双端读出闪烁体阵列 PET 探测器定时测量方法的比较
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-03-29 DOI: 10.1109/TRPMS.2024.3382990
Ming Niu;Zhonghua Kuang;Xiaohui Wang;Ning Ren;Ziru Sang;Tao Sun;Zheng Liu;Zhanli Hu;Zheng Gu;Yongfeng Yang
The main focus of this work is to compare different timing measurement methods of individual silicon photomultiplier (SiPM) arrays and dual-ended readout PET detectors. Two lutetium yttrium oxyorthosilicate (LYSO) crystal arrays with $3.10times 3.10times 20$ - ${mathrm { mm}}^{3}$ crystals, enhanced specular reflector (ESR), and barium sulfate (BaSO4) reflector and one LYSO crystal array with $1.88times 1.88times 20$ - ${mathrm { mm}}^{3}$ crystals and $rm BaSO_{4}$ reflector with dual-ended read out by $8times 8$ SiPM arrays of $3times 3$ - ${mathrm { mm}}^{2}$ active pixel area were measured. Signals of the SiPM arrays were processed individually using 64 channel PETsys TOFPET2 application specific integrated circuits designed for time-of-flight PET applications. For the SiPM arrays, an energy square-weighted average timing method using the timings of the fastest 2 SiPM pixels was found to provide the best-coincidence timing resolutions (CTRs). For the dual-ended readout detectors, the method of using the energy-weighted average timings of the two SiPM arrays provided the best CTR of 234 ps for the detector using $3.10times 3.10times 20$ - ${mathrm { mm}}^{3}$ crystals and ESR reflector, 239 ps for the detector using $3.10times 3.10times 20$ - ${mathrm { mm}}^{3}$ crystals and $rm BaSO_{4}$ reflector, and 275 ps for the detector using $1.88times 1.88times 20$ - ${mathrm { mm}}^{3}$ crystals and $rm BaSO_{4}$ reflector for an energy window of 410–610 keV. The dual-ended readout detectors developed in this work provide better CTRs than those of single-ended readout detectors and a high-3-D position resolution which can be used in the future to develop whole-body PET scanners to simultaneously achieve uniform high-spatial resolution, high sensitivity and high-timing resolution.
这项工作的重点是比较单个硅光电倍增管(SiPM)阵列和双端读出 PET 探测器的不同定时测量方法。两个镥钇氧正硅酸盐(LYSO)晶体阵列(3.10/times 3.10/times 20$ - ${mathrm { mm}}^{3}$ 晶体、增强镜面反射器(ESR)和硫酸钡(BaSO4)反射器)和一个 LYSO 晶体阵列(1.88times 1.88times 20$ - ${mathrm { mm}}^{3}$ 晶体和 $rm BaSO_{4}$ 反射器,并通过 8times 8$ SiPM 阵列(3$times 3$ - ${mathrm { mm}}^{2}$ 有效像素面积)进行双端读出测量。使用专为飞行时间 PET 应用设计的 64 通道 PETsys TOFPET2 专用集成电路对 SiPM 阵列的信号进行了单独处理。对于 SiPM 阵列,使用最快的 2 个 SiPM 像素定时的能量平方加权平均定时方法可提供最佳的重合定时分辨率 (CTR)。对于双端读出探测器,使用两个 SiPM 阵列的能量加权平均定时方法为使用 3.10/times 3.10/times 20$ -${mathrm { mm}}^{3}$ 晶体和 ESR 反射器的探测器提供了 234 ps 的最佳 CTR,为使用 3.10/times 3.10/times 20$ -${mathrm { mm}}^{3}$ 晶体和 ESR 反射器的探测器提供了 239 ps 的最佳 CTR。在 410-610 keV 的能量窗口中,使用 1.88times 1.88times 20$ - ${mathrm { mm}}^{3}$ 晶体和 $rm BaSO_{4}$ 反射器的探测器为 275 ps。与单端读出探测器相比,本研究开发的双端读出探测器具有更好的 CTR 和更高的三维位置分辨率,未来可用于开发全身 PET 扫描仪,以同时实现均匀的高空间分辨率、高灵敏度和高定时分辨率。
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引用次数: 0
PBPK-Adapted Deep Learning for Pretherapy Prediction of Voxelwise Dosimetry: In-Silico Proof of Concept 用于治疗前预测体素剂量测定的 PBPK 适应性深度学习:实验室概念验证
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-03-28 DOI: 10.1109/TRPMS.2024.3381849
Mohamed Kassar;Milos Drobnjakovic;Gabriele Birindelli;Song Xue;Andrei Gafita;Thomas Wendler;Ali Afshar-Oromieh;Nassir Navab;Wolfgang A. Weber;Matthias Eiber;Sibylle Ziegler;Axel Rominger;Kuangyu Shi
Pretherapy dosimetry prediction is a prerequisite for treatment planning and personalized optimization of the emerging radiopharmaceutical therapy (RPT). Physiologically based pharmacokinetic (PBPK) model, describing the intrinsic pharmacokinetics of radiopharmaceuticals, have been proposed for pretherapy prediction of dosimetry. However, it is restricted with organwise prediction and the customization based on pretherapy measurements is still challenging. On the other side, artificial intelligence (AI) has demonstrated the potential in pretherapy dosimetry prediction. Nevertheless, it is still challenging for pure data-driven model to achieve voxelwise prediction due to huge gap between the pretherapy imaging and post-therapy dosimetry. This study aims to integrate the PBPK model into deep learning for voxelwise pretherapy dosimetry prediction. A conditional generative adversarial network (cGAN) integrated with the PBPK model as regularization was developed. For proof of concept, 120 virtual patients with 68Ga-PSMA-11 PET imaging and 177Lu-PSMA-I&T dosimetry were generated using realistic in silico simulations. In kidneys, spleen, liver and salivary glands, the proposed method achieved better accuracy and dose volume histogram than pure deep learning. The preliminary results confirmed that the proposed PBPK-adapted deep learning can improve the pretherapy voxelwise dosimetry prediction and may provide a practical solution to support treatment planning of heterogeneous dose distribution for personalized RPT.
治疗前剂量预测是新兴的放射性药物疗法(RPT)制定治疗计划和进行个性化优化的先决条件。描述放射性药物内在药代动力学的生理学药代动力学(PBPK)模型已被提出用于治疗前剂量预测。然而,该模型仅限于器官预测,而且根据治疗前测量结果进行定制仍具有挑战性。另一方面,人工智能(AI)在治疗前剂量预测方面已显示出潜力。然而,由于治疗前成像与治疗后剂量测定之间存在巨大差距,纯数据驱动模型实现体素预测仍具有挑战性。本研究旨在将 PBPK 模型集成到深度学习中,以实现体素预测。研究开发了一个条件生成对抗网络(cGAN),该网络集成了 PBPK 模型作为正则化。为了验证这一概念,利用现实的硅学模拟生成了 120 位具有 68Ga-PSMA-11 PET 成像和 177Lu-PSMA-I&T 剂量测定的虚拟患者。在肾脏、脾脏、肝脏和唾液腺方面,与纯深度学习相比,所提出的方法获得了更好的准确性和剂量体积直方图。初步结果证实,所提出的 PBPK 适应性深度学习可以改善治疗前的体素剂量预测,并可为支持个性化 RPT 的异质性剂量分布治疗规划提供实用的解决方案。
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引用次数: 0
PET Detectors Based on Multi-Resolution SiPM Arrays 基于多分辨率 SiPM 阵列的 PET 探测器
IF 4.4 Q1 Physics and Astronomy Pub Date : 2024-03-26 DOI: 10.1109/TRPMS.2024.3381865
Jiahao Xie;Haibo Wang;Simon R. Cherry;Junwei Du
Almost all high spatial resolution positron emission tomography (PET) detectors based on pixelated scintillator arrays utilize crystal arrays with smaller pitches than photodetector arrays, leading to challenges in resolving edge crystals. To address this issue, this article introduces a novel multi-resolution silicon photomultiplier (SiPM) array design aimed at decreasing the number of readout channels required while maintaining the crystal resolvability of the detector, especially for edge crystals. The performance of a pseudo $9times9$ multi-resolution SiPM array, consisting of $6.47times6.47$ mm 2, $6.47times3.07$ mm 2, and $3.07times3.07$ mm2 SiPMs, was compared to those of a pseudo $8times8$ SiPM array with a 6.8-mm pitch, and a $16times16$ SiPM array with a 3.4-mm pitch using a $36times36$ LYSO array with a pitch of 1.5 mm. The large-size pseudo SiPMs were implemented by digitally grouping multiple $3.07times3.07$ mm2 SiPMs. The flood histograms show that the edge crystal resolvability of the pseudo $9times9$ multi-resolution SiPM array is comparable to that of the $16times16$ SiPM array and is significantly better than that of the $8times8$ SiPM array.
几乎所有基于像素化闪烁体阵列的高空间分辨率正电子发射断层扫描(PET)探测器都采用了比光电探测器阵列间距更小的晶体阵列,这就给分辨边缘晶体带来了挑战。为解决这一问题,本文介绍了一种新型多分辨率硅光电倍增管(SiPM)阵列设计,旨在减少所需的读出通道数量,同时保持探测器的晶体分辨能力,尤其是对边缘晶体的分辨能力。一个由 6.47 美元/次 6.47 美元 mm 2、6.47 美元/次 3.07 美元 mm 2 和 3.07 美元/次 3.07 美元 mm 2 硅光电倍增管组成的伪 9 美元/次 9 美元多分辨率硅光电倍增管阵列的性能,以及该阵列的读出通道数量。与间距为 6.8 毫米的 8(times8)美元伪 SiPM 阵列和间距为 3.4 毫米的 16(times16)美元 SiPM 阵列以及间距为 1.5 毫米的 36(times36)美元 LYSO 阵列进行了比较。大尺寸伪 SiPM 是通过对多个 3.07times3.07mm2 SiPM 进行数字分组实现的。泛光直方图显示,9(times9)美元伪多分辨率 SiPM 阵列的边缘晶体分辨能力与 16(times16)美元 SiPM 阵列相当,明显优于 8(times8)美元 SiPM 阵列。
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引用次数: 0
DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT DEMIST:基于深度学习的心肌灌注 SPECT 检测任务特定去噪方法
IF 4.4 Q1 Physics and Astronomy Pub Date : 2024-03-25 DOI: 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.
心肌灌注成像(MPI)单光子发射计算机断层扫描(SPECT)图像是以较低的辐射剂量和/或采集时间获得的,与低剂量图像相比,处理后的图像能提高观察者在检测灌注缺陷的临床任务中的表现。为了满足这一需求,我们基于模型-观察者理论的概念和对人类视觉系统的理解,提出了一种基于深度学习的检测任务特定方法,用于对 MPI SPECT 图像进行去噪处理(DEMIST)。该方法在进行去噪的同时,旨在保留影响观察者在检测任务中表现的特征。我们通过一项回顾性研究,使用在两台扫描仪上进行 MPI 研究的患者的匿名临床数据($N,=$ 338),对 DEMIST 检测灌注缺陷的任务进行了客观评估。评估是在 6.25%、12.5% 和 25% 的低剂量水平下进行的,使用的是拟人化通道化 Hotelling 观察器。使用接收器工作特性曲线下面积(AUC)对性能进行量化。与相应的低剂量图像和使用常用的基于任务识别的深度学习去噪方法去噪的图像相比,使用 DEMIST 去噪的图像的 AUC 明显更高。基于患者性别和缺陷类型的分层分析也观察到了类似的结果。此外,DEMIST 还提高了低剂量图像的视觉保真度,并使用均方根误差和结构相似性指数进行量化。数学分析显示,DEMIST 保留了有助于检测任务的特征,同时改善了噪声特性,从而提高了观察者的表现。这些结果为进一步临床评估 DEMIST 在 MPI SPECT 中对低计数图像进行去噪提供了有力证据。
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引用次数: 0
Emphasizing Cherenkov Photons From Bismuth Germanate by Single Photon Response Deconvolution 通过单光子响应解卷积强调来自锗酸铋的切伦科夫光子
IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-03-22 DOI: 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。
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引用次数: 0
Two-Stage Deep Denoising With Self-Guided Noise Attention for Multimodal Medical Images 针对多模态医学图像的两级深度去噪与自引导噪声关注
IF 4.4 Q1 Physics and Astronomy Pub Date : 2024-03-21 DOI: 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.
医学图像去噪被认为是最具挑战性的视觉任务之一。尽管具有现实世界的意义,但现有的去噪方法存在明显的缺陷,因为它们在应用于异构医学图像时往往会产生视觉伪影。本研究采用人工智能(AI)驱动的两阶段学习策略,解决了当代去噪方法的局限性。所提出的方法通过学习来估计噪声图像中的残余噪声。随后,它采用了一种新颖的噪声关注机制,将估计的残余噪声与噪声输入相关联,以 "从过程到细化 "的方式执行去噪。本研究还建议利用多模态学习策略,在医学图像模式和多种噪声模式之间进行通用去噪,以实现广泛应用。通过密集的实验评估了所提方法的实用性。实验结果表明,所提出的方法在定量和定性比较方面明显优于现有的医学图像去噪方法,达到了最先进的性能。总体而言,该方法在峰值信噪比(PSNR)、结构相似性指数(SSIM)、DeltaE $(Delta E)$ 0.80、视觉信息像素保真度(VIFP)和均方误差(MSE)指标上的性能增益分别为 7.64、0.1021、0.80、0.1855 和 18.54。
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引用次数: 0
Medical Multimodal Image Transformation With Modality Code Awareness 具有模态代码意识的医学多模态图像转换
IF 4.4 Q1 Physics and Astronomy Pub Date : 2024-03-20 DOI: 10.1109/TRPMS.2024.3379580
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
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。
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引用次数: 0
期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
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