Pub Date : 2025-06-20DOI: 10.1109/TRPMS.2025.3579351
Jiawen Zhou;Fei Wang;Chao Cai;Qingguo Xie
The coincident event analysis is of paramount importance in positron emission tomography (PET). The result of this process, often termed coincidence time resolution (CTR), is one of the most important quantitative factors that determines the performance of a PET system. Optimizing CTR, typically attempted by lowering threshold voltages in leading edge discriminators (LEDs), presents a challenge due to prevalent pick-up noises. In light of this, in this article, a post-processing algorithm is proposed. This algorithm is dedicated to a detector front-end with the addition of a low noise amplifier (LNA). It can effectively identify outliers and tackle signal distortions so as to mitigate pick-up noises and finally improve CTR. The key contribution of this study is that it can notably improve CTR while still maintaining adequate detection efficiency. Extensive experiments are carried out to demonstrate that the proposed post-processing algorithm can effectively improve CTR, from about 240 ps down to around 100 ps, even with a crystal length of 20 mm (the energy window is 450 to 600 keV). The power consumption of the single channel is only 0.12 W.
{"title":"A Post-Processing Algorithm to Correct Time Walk and Boost CTR to 100 ps Level","authors":"Jiawen Zhou;Fei Wang;Chao Cai;Qingguo Xie","doi":"10.1109/TRPMS.2025.3579351","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3579351","url":null,"abstract":"The coincident event analysis is of paramount importance in positron emission tomography (PET). The result of this process, often termed coincidence time resolution (CTR), is one of the most important quantitative factors that determines the performance of a PET system. Optimizing CTR, typically attempted by lowering threshold voltages in leading edge discriminators (LEDs), presents a challenge due to prevalent pick-up noises. In light of this, in this article, a post-processing algorithm is proposed. This algorithm is dedicated to a detector front-end with the addition of a low noise amplifier (LNA). It can effectively identify outliers and tackle signal distortions so as to mitigate pick-up noises and finally improve CTR. The key contribution of this study is that it can notably improve CTR while still maintaining adequate detection efficiency. Extensive experiments are carried out to demonstrate that the proposed post-processing algorithm can effectively improve CTR, from about 240 ps down to around 100 ps, even with a crystal length of 20 mm (the energy window is 450 to 600 keV). The power consumption of the single channel is only 0.12 W.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1002-1014"},"PeriodicalIF":3.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435711","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 : 2025-06-19DOI: 10.1109/TRPMS.2025.3581204
Se-In Jang;Cristina Lois Gomez;Alex Becker;Emma Thibault;Julie C. Price;Keith A. Johnson;Georges El Fakhri;Kuang Gong
Tau PET imaging is an essential imaging modality for the diagnosis and monitoring of Alzheimer’s disease and related dementias. To enable tau PET imaging-based longitudinal monitoring of disease progression, further reducing the injected dose during each scan is important. In this work, we developed a novel deep learning approach that incorporated cross-modality transformer blocks to integrate both PET and MR prior information to further improve low-dose tau PET imaging. Both spatial and channel information were utilized during the calculation of cross-modality self-attention maps. Performance of the proposed method was evaluated based on the early-frame and late-frame images from 139 dynamic 18F-MK-6240 tau PET datasets. Results showed that the proposed network can outperform other reference networks which concatenated PET and MR images together as the network input.
Tau PET成像是诊断和监测阿尔茨海默病及相关痴呆的重要成像方式。为了实现基于tau PET成像的疾病进展纵向监测，在每次扫描期间进一步减少注射剂量是重要的。在这项工作中，我们开发了一种新的深度学习方法，该方法结合了跨模态变压器块来整合PET和MR先验信息，以进一步改善低剂量tau PET成像。在计算跨模态自注意图时同时利用了空间信息和通道信息。基于139个动态18F-MK-6240 tau PET数据集的早帧和晚帧图像，对所提方法的性能进行了评估。结果表明，该网络的性能优于将PET和MR图像拼接在一起作为网络输入的参考网络。
{"title":"A Cross-Modality Transformer Network for MR-Guided Low-Dose Tau PET Image Denoising","authors":"Se-In Jang;Cristina Lois Gomez;Alex Becker;Emma Thibault;Julie C. Price;Keith A. Johnson;Georges El Fakhri;Kuang Gong","doi":"10.1109/TRPMS.2025.3581204","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3581204","url":null,"abstract":"Tau PET imaging is an essential imaging modality for the diagnosis and monitoring of Alzheimer’s disease and related dementias. To enable tau PET imaging-based longitudinal monitoring of disease progression, further reducing the injected dose during each scan is important. In this work, we developed a novel deep learning approach that incorporated cross-modality transformer blocks to integrate both PET and MR prior information to further improve low-dose tau PET imaging. Both spatial and channel information were utilized during the calculation of cross-modality self-attention maps. Performance of the proposed method was evaluated based on the early-frame and late-frame images from 139 dynamic 18F-MK-6240 tau PET datasets. Results showed that the proposed network can outperform other reference networks which concatenated PET and MR images together as the network input.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"249-257"},"PeriodicalIF":3.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116829","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 : 2025-06-17DOI: 10.1109/TRPMS.2025.3580379
Leonor Rebolo;Ryan Heller;Marta Freire;Pedro Correia;Ana Luisa Silva;Sara St. James;Antonio J. González;Joshua W. Cates;Gerard Ariño-Estrada
proton range verification (PRV) in proton therapy is an unmet clinical need. prompt-gamma imaging (PGI) using thick collimators is a PRV modality that has obtained the most success to-date. The gamma detectors in such approach consist of scintillation crystals coupled to photodetectors. In this work, we report the development and use of detectors made of monolithic pure Cherenkov emitter crystals for the same purpose. We demonstrate for the first time the ability of such detector configuration to provide spatial resolution information in one direction using measurements from a collimated slit. The detector consisted of a PbF2 crystal with dimensions $25times 25times $ 10 mm3 coupled to a S13361-3050AE-08 array of $8times 8$ SiPMs from Hamamatsu. The SiPM array was connected to a row-column readout, with 8+8 channels, and triggered on the sum of the columns. Three different event reconstruction algorithms were tested: center of gravity (CoG), rise to the power (RTP), and neural network (NN). The NN yielded the best spatial resolution, with $3.7pm 0$ .9 mm full width half maximum (FWHM) in average for all positions. CoG and RTP also showed a consistent shift with the change of position of the slit, although with more modest results, between 4 and 7 mm in average for all positions. This is the first characterization of monolithic pure Cherenkov emitters for Multi-MeV gamma imaging. Results are promising for this detector concept, showing that it can offer an alternative for collimated PGI in PRV with potential of sustaining high count rates, with effective background rejection, and low production costs based on the cost of primary components of the crystals.
{"title":"CHEMONO: A Cherenkov-Only Monolithic Detector for PGI in Proton Range Verification","authors":"Leonor Rebolo;Ryan Heller;Marta Freire;Pedro Correia;Ana Luisa Silva;Sara St. James;Antonio J. González;Joshua W. Cates;Gerard Ariño-Estrada","doi":"10.1109/TRPMS.2025.3580379","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3580379","url":null,"abstract":"proton range verification (PRV) in proton therapy is an unmet clinical need. prompt-gamma imaging (PGI) using thick collimators is a PRV modality that has obtained the most success to-date. The gamma detectors in such approach consist of scintillation crystals coupled to photodetectors. In this work, we report the development and use of detectors made of monolithic pure Cherenkov emitter crystals for the same purpose. We demonstrate for the first time the ability of such detector configuration to provide spatial resolution information in one direction using measurements from a collimated slit. The detector consisted of a PbF2 crystal with dimensions <inline-formula> <tex-math>$25times 25times $ </tex-math></inline-formula> 10 mm3 coupled to a S13361-3050AE-08 array of <inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula> SiPMs from Hamamatsu. The SiPM array was connected to a row-column readout, with 8+8 channels, and triggered on the sum of the columns. Three different event reconstruction algorithms were tested: center of gravity (CoG), rise to the power (RTP), and neural network (NN). The NN yielded the best spatial resolution, with <inline-formula> <tex-math>$3.7pm 0$ </tex-math></inline-formula>.9 mm full width half maximum (FWHM) in average for all positions. CoG and RTP also showed a consistent shift with the change of position of the slit, although with more modest results, between 4 and 7 mm in average for all positions. This is the first characterization of monolithic pure Cherenkov emitters for Multi-MeV gamma imaging. Results are promising for this detector concept, showing that it can offer an alternative for collimated PGI in PRV with potential of sustaining high count rates, with effective background rejection, and low production costs based on the cost of primary components of the crystals.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"268-275"},"PeriodicalIF":3.5,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116886","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 : 2025-06-16DOI: 10.1109/TRPMS.2025.3579209
Maxime Toussaint;Francis Loignon-Houle;Jean-Pierre Dussault;Roger Lecomte
One of the limiting factors of spatial resolution in positron emission tomography (PET) imaging is annihilation photon acollinearity (APA). For whole-body PET scanners, APA induces a blur ranging from 1.7 to 2.2 mm FWHM. For long axial field-of-view (FOV) scanners, this range increases even more, depending on the maximum ring difference. It was previously shown that perfect time-of-flight (TOF) resolution sharpens the APA-induced blur by altering its expected Gaussian shape into a profile resembling a 1/r function, thereby reducing its contribution to spatial resolution loss. This suggests that the conventional theoretical limit of PET spatial resolution could be overcome if sufficient TOF resolution can be achieved. However, the requirements to achieve an observable gain in spatial resolution have yet to be explored. We propose an investigation of these requirements for whole-body and long axial FOV scanners, in terms of TOF resolution and count statistics. Using a fictive 81-cm diameter scanner with 2-mm wide detectors, we show that ultrafast TOF resolution—13 ps FWHM—enables an observable gain in spatial resolution for a range of count statistics. In addition, we show that lower TOF resolutions (i.e., higher TOF values of 27 or 67 ps) could mitigate APA for the oblique tubes of response of long axial FOV systems subjected to larger APA blurring. This last observation is of particular interest as it suggests that the nonstationary nature of spatial resolution in PET imaging can be further mitigated when such TOF precision is achieved.
{"title":"Time-of-Flight Requirements to Mitigate Blurring Induced by Annihilation Photon Acollinearity","authors":"Maxime Toussaint;Francis Loignon-Houle;Jean-Pierre Dussault;Roger Lecomte","doi":"10.1109/TRPMS.2025.3579209","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3579209","url":null,"abstract":"One of the limiting factors of spatial resolution in positron emission tomography (PET) imaging is annihilation photon acollinearity (APA). For whole-body PET scanners, APA induces a blur ranging from 1.7 to 2.2 mm FWHM. For long axial field-of-view (FOV) scanners, this range increases even more, depending on the maximum ring difference. It was previously shown that perfect time-of-flight (TOF) resolution sharpens the APA-induced blur by altering its expected Gaussian shape into a profile resembling a 1/r function, thereby reducing its contribution to spatial resolution loss. This suggests that the conventional theoretical limit of PET spatial resolution could be overcome if sufficient TOF resolution can be achieved. However, the requirements to achieve an observable gain in spatial resolution have yet to be explored. We propose an investigation of these requirements for whole-body and long axial FOV scanners, in terms of TOF resolution and count statistics. Using a fictive 81-cm diameter scanner with 2-mm wide detectors, we show that ultrafast TOF resolution—13 ps FWHM—enables an observable gain in spatial resolution for a range of count statistics. In addition, we show that lower TOF resolutions (i.e., higher TOF values of 27 or 67 ps) could mitigate APA for the oblique tubes of response of long axial FOV systems subjected to larger APA blurring. This last observation is of particular interest as it suggests that the nonstationary nature of spatial resolution in PET imaging can be further mitigated when such TOF precision is achieved.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"210-217"},"PeriodicalIF":3.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116912","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}
time-of-flight positron emission tomography (TOF-PET) and proton range verification (PRV) in proton therapy are based on the detection of gamma photons. Despite the difference in the ultimate goal and status of each of these two modalities, both heavily rely on the gamma detectors used in associated imaging systems. The emission of Cherenkov light has been studied extensively over the last decade as a gamma-detection signature in different detector configurations for TOF-PET and PRV. This review aims at: 1) capturing the breadth of works that report on using Cherenkov light for these applications from a detector instrumentation perspective and 2) summarizing barriers encountered by these approaches in their path toward commercial adoption. This review is structured in seven sections: I) brief introduction of TOF-PET and PRV needs that might be addressed with Cherenkov-based gamma detectors; II) physics of Cherenkov emission, propagation, and detection; experimental efforts in detector characterization grouped by the nature of the signals involved in the detector, i.e., III) simultaneous emission of Cherenkov and scintillation light; IV) pure Cherenkov emitters; and V) semiconductor detectors with simultaneous Cherenkov emission; Section VI consolidates the information with a special attention to challenges and potential strategies to overcome them; and Section VII concludes with a short paragraph. We hope this comprehensive review of the extensive work of researchers in this field in the last decade triggers further discussion and sparks inspiration among the community.
{"title":"Current Status of Cherenkov-Based Gamma Detectors for TOF-PET and Proton Range Verification","authors":"Gerard Ariño-Estrada;Nicolaus Kratochwil;Stefan Gundacker;Emilie Roncali","doi":"10.1109/TRPMS.2025.3579673","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3579673","url":null,"abstract":"time-of-flight positron emission tomography (TOF-PET) and proton range verification (PRV) in proton therapy are based on the detection of gamma photons. Despite the difference in the ultimate goal and status of each of these two modalities, both heavily rely on the gamma detectors used in associated imaging systems. The emission of Cherenkov light has been studied extensively over the last decade as a gamma-detection signature in different detector configurations for TOF-PET and PRV. This review aims at: 1) capturing the breadth of works that report on using Cherenkov light for these applications from a detector instrumentation perspective and 2) summarizing barriers encountered by these approaches in their path toward commercial adoption. This review is structured in seven sections: I) brief introduction of TOF-PET and PRV needs that might be addressed with Cherenkov-based gamma detectors; II) physics of Cherenkov emission, propagation, and detection; experimental efforts in detector characterization grouped by the nature of the signals involved in the detector, i.e., III) simultaneous emission of Cherenkov and scintillation light; IV) pure Cherenkov emitters; and V) semiconductor detectors with simultaneous Cherenkov emission; <xref>Section VI</xref> consolidates the information with a special attention to challenges and potential strategies to overcome them; and <xref>Section VII</xref> concludes with a short paragraph. We hope this comprehensive review of the extensive work of researchers in this field in the last decade triggers further discussion and sparks inspiration among the community.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"1-15"},"PeriodicalIF":3.5,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11036331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145859875","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}
The parallax error (PE) significantly deteriorates the spatial resolution and imaging quality of positron emission tomography (PET) scanners. Existing PE correction methods either rely on depth decoding detectors in hardware which increases development costs, or optimize the system response matrix (SRM) in software providing limited compensation for PE. This work proposed a novel PE correction method in projection space based on deep learning (DL), consisting of two steps. First, the sinogram affected by PE was processed by a neural network (PEC-Net). The corrected sinogram output from the PEC-Net was then reconstructed to an improved image. To generate ideal PE-corrected labels, we synthesized training data using Monte Carlo (MC) simulation-based SRMs as forward projectors. The proposed method was validated using simulation data and real data. Experimental results show that the proposed method effectively eliminated artifacts caused by PE, and the reconstructed images of simulation data outperformed those obtained at 4 mm depth of interaction (DOI) resolution in terms of structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). The PEC-Net may provide a low-cost, high-performance, software-based PE correction method for PET scanners without DOI measurement.
{"title":"Deep-Learning-Based PET Parallax Error Correction: A 2-D Simulation and Phantom Study","authors":"Yu Liu;Jiayou Lan;Ran Cheng;Qingguo Xie;Xiaoping Wang;Bensheng Qiu;Xun Chen;Peng Xiao","doi":"10.1109/TRPMS.2025.3577903","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3577903","url":null,"abstract":"The parallax error (PE) significantly deteriorates the spatial resolution and imaging quality of positron emission tomography (PET) scanners. Existing PE correction methods either rely on depth decoding detectors in hardware which increases development costs, or optimize the system response matrix (SRM) in software providing limited compensation for PE. This work proposed a novel PE correction method in projection space based on deep learning (DL), consisting of two steps. First, the sinogram affected by PE was processed by a neural network (PEC-Net). The corrected sinogram output from the PEC-Net was then reconstructed to an improved image. To generate ideal PE-corrected labels, we synthesized training data using Monte Carlo (MC) simulation-based SRMs as forward projectors. The proposed method was validated using simulation data and real data. Experimental results show that the proposed method effectively eliminated artifacts caused by PE, and the reconstructed images of simulation data outperformed those obtained at 4 mm depth of interaction (DOI) resolution in terms of structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). The PEC-Net may provide a low-cost, high-performance, software-based PE correction method for PET scanners without DOI measurement.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"218-228"},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11028920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116930","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}
This article presents a novel image reconstruction pipeline for three-gamma (3-$gamma $ ) positron emission tomography (PET) aimed at improving spatial resolution and reducing noise in nuclear medicine. The proposed Direct$3gamma $ pipeline addresses the inherent challenges in 3-$gamma $ PET systems, such as detector imperfections and uncertainty in photon interaction points. A key feature of the pipeline is its ability to determine the order of interactions through a model trained on Monte Carlo (MC) simulations using the Geant4 Application for Tomography Emission (GATE) toolkit, thus providing the necessary information to construct Compton cones which intersects with the line of response (LOR) to provide an estimate of the emission point. The pipeline processes 3-$gamma $ PET raw data, reconstructs histoimages by propagating energy and spatial uncertainties along the LOR, and applies a 3-D convolutional neural network (CNN) to refine these intermediate images into high-quality reconstructions. To further enhance image quality, the pipeline leverages both supervised learning and adversarial losses, the latter preserving fine structural details. Experimental results show that Direct$3gamma $ consistently outperforms conventional 200-ps time-of-flight (TOF) PET in terms of structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR).
{"title":"Direct 3γ: A Pipeline for Direct Three-Gamma PET Image Reconstruction","authors":"Youness Mellak;Alexandre Bousse;Thibaut Merlin;Debora Giovagnoli;Dimitris Visvikis","doi":"10.1109/TRPMS.2025.3577810","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3577810","url":null,"abstract":"This article presents a novel image reconstruction pipeline for three-gamma (3-<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>) positron emission tomography (PET) aimed at improving spatial resolution and reducing noise in nuclear medicine. The proposed Direct<inline-formula> <tex-math>$3gamma $ </tex-math></inline-formula> pipeline addresses the inherent challenges in 3-<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula> PET systems, such as detector imperfections and uncertainty in photon interaction points. A key feature of the pipeline is its ability to determine the order of interactions through a model trained on Monte Carlo (MC) simulations using the Geant4 Application for Tomography Emission (GATE) toolkit, thus providing the necessary information to construct Compton cones which intersects with the line of response (LOR) to provide an estimate of the emission point. The pipeline processes 3-<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula> PET raw data, reconstructs histoimages by propagating energy and spatial uncertainties along the LOR, and applies a 3-D convolutional neural network (CNN) to refine these intermediate images into high-quality reconstructions. To further enhance image quality, the pipeline leverages both supervised learning and adversarial losses, the latter preserving fine structural details. Experimental results show that Direct<inline-formula> <tex-math>$3gamma $ </tex-math></inline-formula> consistently outperforms conventional 200-ps time-of-flight (TOF) PET in terms of structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR).","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"181-191"},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116885","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 : 2025-06-09DOI: 10.1109/TRPMS.2025.3577309
Juan E. Arco;Carmen Jiménez-Mesa;Andrés Ortiz;Javier Ramírez;Johannes Levin;Juan M. Górriz
Medical imaging fusion combines complementary information from multiple modalities to enhance diagnostic accuracy. However, evaluating the quality of fused images remains challenging, with many studies relying solely on classification performance, which may lead to incorrect conclusions. We introduce a novel framework for improving image fusion, focusing on preserving fine-grained details. Our model uses a siamese autoencoder to process T1-MRI and FDG-PET images in the context of Alzheimer’s disease (AD). The framework optimizes fusion by minimizing reconstruction error between generated and input images, while maximizing differences between modalities through cosine distance. Additionally, we propose a supervised variant, incorporating binary cross-entropy loss between diagnostic labels and probabilities. Fusion quality is rigorously assessed through three tests: 1) classification of AD patients and controls using fused images; 2) an atlas-based occlusion test for identifying regions relevant to cognitive decline; and 3) analysis of structural–functional relationships via Euclidean distance. Results show an AUC of 0.92 for AD detection, reveal the involvement of brain regions linked to preclinical AD stages, and demonstrate preserved structural–functional brain networks, indicating that subtle differences are successfully captured through our fusion approach.
{"title":"Explainable Intermodality Medical Information Transfer Using Siamese Autoencoders","authors":"Juan E. Arco;Carmen Jiménez-Mesa;Andrés Ortiz;Javier Ramírez;Johannes Levin;Juan M. Górriz","doi":"10.1109/TRPMS.2025.3577309","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3577309","url":null,"abstract":"Medical imaging fusion combines complementary information from multiple modalities to enhance diagnostic accuracy. However, evaluating the quality of fused images remains challenging, with many studies relying solely on classification performance, which may lead to incorrect conclusions. We introduce a novel framework for improving image fusion, focusing on preserving fine-grained details. Our model uses a siamese autoencoder to process T1-MRI and FDG-PET images in the context of Alzheimer’s disease (AD). The framework optimizes fusion by minimizing reconstruction error between generated and input images, while maximizing differences between modalities through cosine distance. Additionally, we propose a supervised variant, incorporating binary cross-entropy loss between diagnostic labels and probabilities. Fusion quality is rigorously assessed through three tests: 1) classification of AD patients and controls using fused images; 2) an atlas-based occlusion test for identifying regions relevant to cognitive decline; and 3) analysis of structural–functional relationships via Euclidean distance. Results show an AUC of 0.92 for AD detection, reveal the involvement of brain regions linked to preclinical AD stages, and demonstrate preserved structural–functional brain networks, indicating that subtle differences are successfully captured through our fusion approach.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"192-209"},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11029061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116837","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}
225Ac-based radiopharmaceuticals for targeted alpha therapy (TAT) have shown positive outcomes in recent clinical trials and preclinical studies, and it has emerged as a promising solution for future cancer treatments. Small-animal in-vivo imaging is critical to better understand 225Ac radiopharmaceuticals biokinetics and to accelerate evaluation and discovery of new 225Ac radiopharmaceuticals. However, gamma-ray imaging of 225Ac and its daughters is challenging due to the extremely low injected activities, the low branching ratios of the emitted $gamma $ rays, and their broad range of energies. State-of-the-art scanners for single-photon emission computed tomography (SPECT) have sensitivity limitations when imaging such low activities, and imaging sessions of several hours are necessary, precluding in-vivo studies. We propose Compton imaging as an alternative to traditional SPECT imagers in order to enable a higher sensitivity and to decrease the minimum imageable activities of current systems. In this study, we explore a 3D-positioning cadmium zinc telluride (CZT) camera (M400, H3D) to achieve highly sensitive Compton imaging of 225Ac daughters at both high-energy (440 keV from 213Bi) and low-energy gamma rays (218 keV from 221Fr). The Compton sensitivity of the imaging system with a source as close as possible from the detector (7 mm) were 1014(33) cps/MBq and 467(23) cps/MBq for 213Bi and 221Fr, respectively. We studied the response of the camera using 225Ac point sources, including the demonstration of simultaneous imaging of 213Bi and 221Fr from multiple 225Ac sources at sub-$mu $ Ci activity levels, ranging from 7.4 to 25.9 kBq, in a 18-min imaging session. Furthermore, we performed a mouse phantom experiment to demonstrate that we could form high-sensitive Compton images of 213Bi and 221Fr, concluding that we can image a mouse phantom with an activity of ~0.55 MBq in just 9 and 36 s for 213Bi and 221Fr, respectively, with a single detector head and in a single bed position. This is equivalent to imaging an activity of 3.7 kBq, a typical tumor uptake in mouse experiments with 225Ac, in 23 min for 213Bi and 90 min for 221Fr with a small 5.7 cm $times 5$ .7 cm area prototype. Increasing angular coverage would further increase sensitivity. Finally, we also compared Compton imaging with collimated imaging.
{"title":"Compton Imaging of Ac-225 in Preclinical Phantoms With a 3D-positioning CZT Camera","authors":"Biswajit Das;Baharak Mehrdel;David Goodman;Michael Streicher;Youngho Seo;Javier Caravaca","doi":"10.1109/TRPMS.2025.3577212","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3577212","url":null,"abstract":"225Ac-based radiopharmaceuticals for targeted alpha therapy (TAT) have shown positive outcomes in recent clinical trials and preclinical studies, and it has emerged as a promising solution for future cancer treatments. Small-animal in-vivo imaging is critical to better understand 225Ac radiopharmaceuticals biokinetics and to accelerate evaluation and discovery of new 225Ac radiopharmaceuticals. However, gamma-ray imaging of 225Ac and its daughters is challenging due to the extremely low injected activities, the low branching ratios of the emitted <inline-formula> <tex-math>$gamma $ </tex-math></inline-formula> rays, and their broad range of energies. State-of-the-art scanners for single-photon emission computed tomography (SPECT) have sensitivity limitations when imaging such low activities, and imaging sessions of several hours are necessary, precluding in-vivo studies. We propose Compton imaging as an alternative to traditional SPECT imagers in order to enable a higher sensitivity and to decrease the minimum imageable activities of current systems. In this study, we explore a 3D-positioning cadmium zinc telluride (CZT) camera (M400, H3D) to achieve highly sensitive Compton imaging of 225Ac daughters at both high-energy (440 keV from 213Bi) and low-energy gamma rays (218 keV from 221Fr). The Compton sensitivity of the imaging system with a source as close as possible from the detector (7 mm) were 1014(33) cps/MBq and 467(23) cps/MBq for 213Bi and 221Fr, respectively. We studied the response of the camera using 225Ac point sources, including the demonstration of simultaneous imaging of 213Bi and 221Fr from multiple 225Ac sources at sub-<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>Ci activity levels, ranging from 7.4 to 25.9 kBq, in a 18-min imaging session. Furthermore, we performed a mouse phantom experiment to demonstrate that we could form high-sensitive Compton images of 213Bi and 221Fr, concluding that we can image a mouse phantom with an activity of ~0.55 MBq in just 9 and 36 s for 213Bi and 221Fr, respectively, with a single detector head and in a single bed position. This is equivalent to imaging an activity of 3.7 kBq, a typical tumor uptake in mouse experiments with 225Ac, in 23 min for 213Bi and 90 min for 221Fr with a small 5.7 cm <inline-formula> <tex-math>$times 5$ </tex-math></inline-formula>.7 cm area prototype. Increasing angular coverage would further increase sensitivity. Finally, we also compared Compton imaging with collimated imaging.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"112-125"},"PeriodicalIF":3.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861208","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}
Despite advancements in lung cancer therapy, the prognosis for advanced or metastatic patients remains poor, yet many patients eventually develop resistance to standard treatments leading to disease progression and poor survival. Here, we described a combination of cold atmosphere plasma (CAP) and nanoparticles [ZrO2 NPs (zirconium oxide nanoparticle) and 3Y-TZP NPs (3% mol yttria tetragonal zirconia polycrystal nanoparticle)] for lung cancer therapy. We found that $mathrm {ZrO_{2}}$ NPs caused obvious damage to the inside of the lung cancer cells. CAP and $mathrm {ZrO_{2}}$ NPs mainly affected the mitochondria function, leading to a decrease in mitochondrial membrane potential and ATP levels, also causing endoplasmic reticulum stress and cell nucleus internal DNA damage, etc. CAP combined with $mathrm {ZrO_{2}}$ NPs (CAP@ZrO2) induced lung cancer cell apoptosis by activating the TGF-$beta $ pathway. However, 3Y-TZP NPs showed beneficial effects for cancer cells, promoting their proliferation. This contrasting finding highlights that not all zirconia nanoparticles may be appropriate for lung cancer treatment in general. CAP@ZrO2 offers a new therapy for the clinical treatment of lung cancer.
{"title":"Cold Atmospheric Plasma Combines With Zirconia Nanoparticles for Lung Cancer Therapy via TGF- β Signaling Pathway","authors":"Yueye Huang;Rui Zhang;Xiao Chen;Fei Cao;Qiujie Fang;Qingnan Xu;Shicong Huang;Yufan Wang;Guojun Chen;Zhitong Chen","doi":"10.1109/TRPMS.2025.3576730","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3576730","url":null,"abstract":"Despite advancements in lung cancer therapy, the prognosis for advanced or metastatic patients remains poor, yet many patients eventually develop resistance to standard treatments leading to disease progression and poor survival. Here, we described a combination of cold atmosphere plasma (CAP) and nanoparticles [ZrO2 NPs (zirconium oxide nanoparticle) and 3Y-TZP NPs (3% mol yttria tetragonal zirconia polycrystal nanoparticle)] for lung cancer therapy. We found that <inline-formula> <tex-math>$mathrm {ZrO_{2}}$ </tex-math></inline-formula> NPs caused obvious damage to the inside of the lung cancer cells. CAP and <inline-formula> <tex-math>$mathrm {ZrO_{2}}$ </tex-math></inline-formula> NPs mainly affected the mitochondria function, leading to a decrease in mitochondrial membrane potential and ATP levels, also causing endoplasmic reticulum stress and cell nucleus internal DNA damage, etc. CAP combined with <inline-formula> <tex-math>$mathrm {ZrO_{2}}$ </tex-math></inline-formula> NPs (CAP@ZrO2) induced lung cancer cell apoptosis by activating the TGF-<inline-formula> <tex-math>$beta $ </tex-math></inline-formula> pathway. However, 3Y-TZP NPs showed beneficial effects for cancer cells, promoting their proliferation. This contrasting finding highlights that not all zirconia nanoparticles may be appropriate for lung cancer treatment in general. CAP@ZrO2 offers a new therapy for the clinical treatment of lung cancer.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"144-158"},"PeriodicalIF":3.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861204","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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