Pub Date : 2024-04-02DOI: 10.1109/TRPMS.2024.3380109
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2024.3380109","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3380109","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342665","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 : 2024-04-02DOI: 10.1109/TRPMS.2024.3380107
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2024.3380107","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3380107","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488731","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342792","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 : 2024-04-02DOI: 10.1109/TRPMS.2024.3381311
{"title":"IEEE Data Port","authors":"","doi":"10.1109/TRPMS.2024.3381311","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3381311","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"451-451"},"PeriodicalIF":4.4,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342794","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 : 2024-04-02DOI: 10.1109/TRPMS.2024.3381309
{"title":"Member Get-A-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2024.3381309","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3381309","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"452-452"},"PeriodicalIF":4.4,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342655","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}
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.
{"title":"A Multiscale Spatial Transformer U-Net for Simultaneously Automatic Reorientation and Segmentation of 3-D Nuclear Cardiac Images","authors":"Yangfan Ni;Duo Zhang;Gege Ma;Fan Rao;Yuanfeng Wu;Lijun Lu;Zhongke Huang;Wentao Zhu","doi":"10.1109/TRPMS.2024.3382318","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3382318","url":null,"abstract":"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: \u0000<inline-formula> <tex-math>$^{13}text{N}$ </tex-math></inline-formula>\u0000-ammonia positron emission tomography (PET) and \u0000<inline-formula> <tex-math>$^{99m}$ </tex-math></inline-formula>\u0000Tc-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 \u0000<inline-formula> <tex-math>$1.4 times 10^{-2}$ </tex-math></inline-formula>\u0000. 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.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"632-645"},"PeriodicalIF":4.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500375","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 : 2024-03-31DOI: 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.
{"title":"Experimental Uses of Positronium and Potential for Biological Applications","authors":"A. Hourlier;F. Boisson;D. Brasse","doi":"10.1109/TRPMS.2024.3407981","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3407981","url":null,"abstract":"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 \u0000<inline-formula> <tex-math>$beta ^{+}$ </tex-math></inline-formula>\u0000 isotopes are linked to a vector molecule, the distribution of \u0000<inline-formula> <tex-math>$beta ^{+}$ </tex-math></inline-formula>\u0000 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 \u0000<inline-formula> <tex-math>$(tau _{mathrm {oPs}})$ </tex-math></inline-formula>\u0000, 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.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"581-594"},"PeriodicalIF":4.6,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500180","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}
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。
{"title":"Temporal Image Sequence Separation in Dual-Tracer Dynamic PET With an Invertible Network","authors":"Chuanfu Sun;Bin Huang;Jie Sun;Yangfan Ni;Huafeng Liu;Qian Xia;Qiegen Liu;Wentao Zhu","doi":"10.1109/TRPMS.2024.3407120","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3407120","url":null,"abstract":"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.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"774-787"},"PeriodicalIF":4.6,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10542421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143785","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 : 2024-03-29DOI: 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.
{"title":"Comparison of Timing Measurement Methods of Dual-Ended Readout Scintillator Array PET Detectors","authors":"Ming Niu;Zhonghua Kuang;Xiaohui Wang;Ning Ren;Ziru Sang;Tao Sun;Zheng Liu;Zhanli Hu;Zheng Gu;Yongfeng Yang","doi":"10.1109/TRPMS.2024.3382990","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3382990","url":null,"abstract":"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 \u0000<inline-formula> <tex-math>$3.10times 3.10times 20$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>${mathrm { mm}}^{3}$ </tex-math></inline-formula>\u0000 crystals, enhanced specular reflector (ESR), and barium sulfate (BaSO4) reflector and one LYSO crystal array with \u0000<inline-formula> <tex-math>$1.88times 1.88times 20$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>${mathrm { mm}}^{3}$ </tex-math></inline-formula>\u0000 crystals and \u0000<inline-formula> <tex-math>$rm BaSO_{4}$ </tex-math></inline-formula>\u0000 reflector with dual-ended read out by \u0000<inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula>\u0000 SiPM arrays of \u0000<inline-formula> <tex-math>$3times 3$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>${mathrm { mm}}^{2}$ </tex-math></inline-formula>\u0000 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 \u0000<inline-formula> <tex-math>$3.10times 3.10times 20$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>${mathrm { mm}}^{3}$ </tex-math></inline-formula>\u0000 crystals and ESR reflector, 239 ps for the detector using \u0000<inline-formula> <tex-math>$3.10times 3.10times 20$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>${mathrm { mm}}^{3}$ </tex-math></inline-formula>\u0000 crystals and \u0000<inline-formula> <tex-math>$rm BaSO_{4}$ </tex-math></inline-formula>\u0000 reflector, and 275 ps for the detector using \u0000<inline-formula> <tex-math>$1.88times 1.88times 20$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>${mathrm { mm}}^{3}$ </tex-math></inline-formula>\u0000 crystals and \u0000<inline-formula> <tex-math>$rm BaSO_{4}$ </tex-math></inline-formula>\u0000 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.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"607-617"},"PeriodicalIF":4.6,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10485386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500325","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 : 2024-03-28DOI: 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.
{"title":"PBPK-Adapted Deep Learning for Pretherapy Prediction of Voxelwise Dosimetry: In-Silico Proof of Concept","authors":"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","doi":"10.1109/TRPMS.2024.3381849","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3381849","url":null,"abstract":"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.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"646-654"},"PeriodicalIF":4.6,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10481675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500252","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 : 2024-03-26DOI: 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.
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