IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium最新文献

Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization. 利用对比性对抗领域泛化技术实现受试者感知的 PET 去噪。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2024-10-01 Epub Date: 2024-09-25 DOI: 10.1109/nss/mic/rtsd57108.2024.10656150

X Liu, T Marin, S Vafay Eslahi, A Tiss, Y Chemli, K A Johson, G El Fakhri, J Ouyang

{"title":"Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization.","authors":"X Liu, T Marin, S Vafay Eslahi, A Tiss, Y Chemli, K A Johson, G El Fakhri, J Ouyang","doi":"10.1109/nss/mic/rtsd57108.2024.10656150","DOIUrl":"https://doi.org/10.1109/nss/mic/rtsd57108.2024.10656150","url":null,"abstract":"Recent advances in deep learning (DL) have greatly improved the performance of positron emission tomography (PET) denoising performance. However, DL model performance can vary a lot across subjects, due to the large variability of the count levels and spatial distributions. A generalizable DL model that mitigates the subject-wise variations is highly expected toward a reliable and trustworthy system for clinical application. In this work, we propose a contrastive adversarial learning framework for subject-wise domain generalization (DG). Specifically, we configure a contrastive discriminator in addition to the UNet-based denoising module to check the subject-related information in the bottleneck feature, while the denoising module is adversarially trained to enforce the extraction of subject-invariant features. The sampled low-count realizations from the list-mode data are used as anchor-positive pairs to be close to each other, while the other subjects are used as negative samples to be distributed far away. We evaluated on 97 18F-MK6240 tau PET studies, each having 20 noise realizations with 25% fractions of events. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes in a subject-independent manner. The proposed contrastive adversarial DG demonstrated superior denoising performance than conventional UNet without subject-wise DG and cross-entropy-based adversarial DG.","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514168","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}

引用次数: 0

Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising. 带变压器骨干的扩散模型对低计数 PET 去噪的消融研究

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2024-10-01 Epub Date: 2024-09-25 DOI: 10.1109/nss/mic/rtsd57108.2024.10655179

Y Huang, X Liu, T Miyazaki, S Omachi, G El Fakhri, J Ouyang

{"title":"Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising.","authors":"Y Huang, X Liu, T Miyazaki, S Omachi, G El Fakhri, J Ouyang","doi":"10.1109/nss/mic/rtsd57108.2024.10655179","DOIUrl":"https://doi.org/10.1109/nss/mic/rtsd57108.2024.10655179","url":null,"abstract":"Diffusion models (DM) built from a hierarchy of denoising autoencoders have achieved remarkable progress in image generation, and are increasingly influential in the field of image restoration (IR) tasks. In the meantime, its backbone of autoencoders also evolved from UNet to vision transformer, e.g. Restormer. Therefore, it is important to disentangle the contribution of backbone networks and the additional generative learning scheme. Notably, DM shows varied performance across IR tasks, and the performance of recent advanced transformer-based DM on PET denoising is under-explored. In this study, we further raise an intuitive question, \"{if we have a sufficiently powerful backbone, whether DM can be a general add-on generative learning scheme to further boost PET denoising}\". Specifically, we investigate one of the best-in-class IR models, i.e., DiffIR, which is a latent DM based on the Restormer backbone. We provide a qualitative and quantitative comparison with UNet, SR3 (UNet+pixel DM), and Restormer, on the 25% low dose 18F-FDG whole-body PET denoising task, aiming to identify the best practices. We trained and tested on 93 and 12 subjects, and each subject has 644 slices. It appears that Restormer outperforms UNet in terms of PSNR and MSE. However, additional latent DM over Restormer does not contribute to better MSE, SSIM, or PSNR in our task, which is even inferior to the conventional UNet. In addition, SR3 with pixel space DM is not stable to synthesize satisfactory results. The results are consistent with the natural image super-resolution tasks, which also suffer from limited spatial information. A possible reason would be the denoising iteration at latent feature space cannot well support detailed structure and texture restoration. This issue is more crucial in the IR tasks taking inputs with limited details, e.g., SR and PET denoising.","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514166","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}

引用次数: 0

Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model. 利用方框提示医学分段 Anything 模型进行点监督脑肿瘤分段

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2024-10-01 Epub Date: 2024-09-25 DOI: 10.1109/nss/mic/rtsd57108.2024.10656071

X Liu, J Woo, C Ma, J Ouyang, G El Fakhri

{"title":"Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model.","authors":"X Liu, J Woo, C Ma, J Ouyang, G El Fakhri","doi":"10.1109/nss/mic/rtsd57108.2024.10656071","DOIUrl":"10.1109/nss/mic/rtsd57108.2024.10656071","url":null,"abstract":"Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods.","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514167","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}

引用次数: 0

Calibration Methodology of an Edgeless PET System Prototype. 无边沿PET系统样机的标定方法。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2020-10-01 Epub Date: 2021-08-12 DOI: 10.1109/NSS/MIC42677.2020.9508042

Marta Freire, Andrea Gonzalez-Montoro, Gabriel Cañizares, Stuart S Berr, Luis F Vidal, Liczandro Hernandez, Antonio J Gonzalez

{"title":"Calibration Methodology of an Edgeless PET System Prototype.","authors":"Marta Freire, Andrea Gonzalez-Montoro, Gabriel Cañizares, Stuart S Berr, Luis F Vidal, Liczandro Hernandez, Antonio J Gonzalez","doi":"10.1109/NSS/MIC42677.2020.9508042","DOIUrl":"https://doi.org/10.1109/NSS/MIC42677.2020.9508042","url":null,"abstract":"Instrumentation research in small animal Positron Emission Tomography (PET) imaging is driven by improving timing, spatial resolution and sensitivity. Conventional PET scanners are built of multiple detectors placed in a cylindrical geometry with gaps between them in both the transaxial and axial planes. These gaps decrease sensitivity and degrade spatial resolution towards the edges of the system field of view (FOV). To mitigate these problems, we have designed and validated an edgeless pre-clinical PET system based on a single LYSO annulus with an inner diameter of 62 mm and 10 outer facets of 26 × 52 mm2 each. The scintillation light is read out using the row and columns of Silicon Photomultipliers (SiPMs) mounted in magnetic-field compatible PCBs. The objective of this work is to provide a calibration method for this system. The particular design of the annulus produces some undesirable effects in the light distributions (LD) at the module joints, which needs to be addressed. Nevertheless, after calibration, the system allows one to properly retrieve both, the energy and 3D photon impact positions.","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":"2020 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667022/pdf/nihms-1736930.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39726358","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}

引用次数: 1

Unsupervised Learning in PET Radiomics. PET放射组学中的无监督学习。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2017-10-01 Epub Date: 2018-11-15 DOI: 10.1109/NSSMIC.2017.8532959

G Liu, S-Y Huang, B Franc, Y Seo, D Mitra

引用次数: 0

Tensor Tomography of Dark Field Scatter using X-ray Interferometry with Bi-prisms. 双棱镜x射线干涉法暗场散射张量层析成像。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2017-10-01 Epub Date: 2018-11-15 DOI: 10.1109/NSSMIC.2017.8533088

Grant T Gullberg, Michael Fuller, Uttam Shrestha, Youngho Seo

引用次数: 0

Test of a 32-channel Prototype ASIC for Photon Counting Application. 光子计数32通道ASIC原型测试。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2015-10-01 Epub Date: 2016-10-06 DOI: 10.1109/NSSMIC.2015.7582272

Y Chen, Y Cui, P O'Connor, Y Seo, G S Camarda, A Hossain, U Roy, G Yang, R B James

{"title":"Test of a 32-channel Prototype ASIC for Photon Counting Application.","authors":"Y Chen, Y Cui, P O'Connor, Y Seo, G S Camarda, A Hossain, U Roy, G Yang, R B James","doi":"10.1109/NSSMIC.2015.7582272","DOIUrl":"https://doi.org/10.1109/NSSMIC.2015.7582272","url":null,"abstract":"A new low-power application-specific integrated circuit (ASIC) for Cadmium Zinc Telluride (CZT) detectors for single-photon emission computed tomography (SPECT) application is being developed at BNL. As the first step, a 32-channel prototype ASIC was designed and tested recently. Each channel has a preamplifier followed by CR-RC3 shaping circuits and three independent energy bins with comparators and 16-bit counters. The ASIC was fabricated with TSMC 0.35-μm complementary metal-oxide-semiconductor (CMOS) process and tested in laboratories. The power consumption is around 1 mW/ch with a 2.5-V supply. With a gain of 400 mV/fC and the peaking time of 500 ns, the equivalent noise charge (ENC) of 360 e- has been measured in room temperature while the crosstalk rate is less than 0.3%. The 10-bit DACs for global thresholds have an integral nonlinearity (INL) less than 0.56% and differential nonlinearity (DNL) less than 0.33%. In the presentation, we will report the detailed test results with this ASIC.","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":"2015 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/NSSMIC.2015.7582272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35098397","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}

引用次数: 0

Parallelization of Iterative Reconstruction Algorithms in Multiple Modalities. 多模态迭代重构算法的并行化。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2014-11-01 DOI: 10.1109/NSSMIC.2014.7430944

Debasis Mitra, Hui Pan, Fares Alhassen, Youngho Seo

引用次数: 5

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation. 医学成像中的大数据处理:大规模自动并行计算的迭代重建。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2014-11-01 DOI: 10.1109/NSSMIC.2014.7430758

Jae H Lee, Yushu Yao, Uttam Shrestha, Grant T Gullberg, Youngho Seo

引用次数: 3

Quantitative Signature of Coronary Steal in a Patient with Occluded Coronary Arteries Supported by Collateral Circulation Using Dynamic SPECT. 动态SPECT对侧支循环支持下冠状动脉闭塞患者冠状动脉偷窃的定量分析。

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2014-11-01 DOI: 10.1109/NSSMIC.2014.7430923

Uttam Shrestha, Elias H Botvinick, Yerem Yeghiazarians, Youngho Seo, Grant T Gullberg

引用次数: 1