2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)最新文献

An Analytical Model for Compton Cameras Efficiency Estimation 康普顿相机效率估算的解析模型

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875635

Aicha Bourkadi Idrissi, I. D'Adda, L. Buonanno, M. Carminati, C. Fiorini

引用次数: 0

Low-dose Direct PET Image Reconstruction Using Channel Attention for Deep Neural Network 基于通道关注的深度神经网络低剂量直接PET图像重建

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875555

T. Yin, T. Obi

{"title":"Low-dose Direct PET Image Reconstruction Using Channel Attention for Deep Neural Network","authors":"T. Yin, T. Obi","doi":"10.1109/NSS/MIC44867.2021.9875555","DOIUrl":"https://doi.org/10.1109/NSS/MIC44867.2021.9875555","url":null,"abstract":"Positron emission tomography (PET) is a medical imaging approach widely used in various clinical applications. There is significant value in low-dose PET image reconstruction, because radiation risk is reduced when patients are injected with lower dose of radiotracer. However, this results in a high level of noise in emission data, which degrades the quality of activity distribution images. In this paper, we propose a deep neural network for low-dose PET reconstruction. Using time-of-flight (TOF) sinograms as inputs, it generates high-quality quantitative PET images directly. Specifically, we utilize an encoder-decoder to transfer projections in sinogram domain to activity maps in image domain. Then the outputs of previous stage are restored using a deep neural network with channel attention modules. Residual connections allow abundant low-level features to be bypassed, while channel attention blocks (CABs) capture high-level features by extracting channel statistics. We inject supervision to both the initial output after domain transformation and the final output. The loss function is comprised of the mean square error (MSE) of two outputs and their edge losses. The qualitative and quantitative results demonstrate that the proposed approach is capable of preserving fine details. This method shows promise in improving PET image quality with low-dose emission data.","PeriodicalId":347712,"journal":{"name":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115391699","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}

引用次数: 0

Challenges in Optimization of a Stationary Tomographic Molecular Breast Imaging System 静止层析分子乳腺成像系统优化的挑战

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875682

K. Erlandsson, A. Wirth, K. Thielemans, I. Baistow, A. Cherlin, B. Hutton

引用次数: 3

Science and Mission Status of EUSO-SPB2 EUSO-SPB2的科学和任务现状

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875504

V. Scotti

{"title":"Science and Mission Status of EUSO-SPB2","authors":"V. Scotti","doi":"10.1109/NSS/MIC44867.2021.9875504","DOIUrl":"https://doi.org/10.1109/NSS/MIC44867.2021.9875504","url":null,"abstract":"The Extreme Universe Space Observatory on a Super Pressure Balloon II (EUSO-SPB2) is a second-generation stratospheric balloon instrument designed to be a precursor mission for a future space observatory for multi-messenger astrophysics, like the proposed Probe Of Extreme Multi-Messenger Astrophysics (POEMMA). EUSO-SPB2 will study Ultra High Energy Cosmic Rays (UHECRs) via the fluorescence technique and Ultra High Energy (UHE) neutrinos via Cherenkov emission.EUSO-SPB2 will host onboard two Schmidt telescopes, each optimized for their respective observational goals. The Fluorescence Telescope will look downwards onto the atmosphere to study the UV fluorescence emission from UHECRs. The Cherenkov Telescope is designed to detect fast signals (∼10ns) and points near the Earth's limb. This allows for the measurement of Cherenkov light from Extensive Air Showers caused by Earth skimming UHE neutrinos if pointed slightly below the limb or from UHECRs if observing slightly above.The planned launch date of EUSO-SPB2 is 2023 from Wanaka, NZ with a flight duration target of 100 days. Such a long duration flight will provide hundreds of UHECR Cherenkov signals in addition to tens of UHECR fluorescence tracks, and will improve the understanding of potential background signals for both detection techniques.This contribution will provide a short overview of each telescope and the status of the mission as well as its scientific motivation.","PeriodicalId":347712,"journal":{"name":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125665197","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}

引用次数: 0

Internal Dosimetry in Diagnostic Nuclear Medicine Using Monte Carlo Techniques 蒙特卡罗技术在核医学诊断中的应用

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875629

M. Karimipourfard, S. Sina, M. Sadeghi, S. Karimkhani, A. Zabihi

{"title":"Internal Dosimetry in Diagnostic Nuclear Medicine Using Monte Carlo Techniques","authors":"M. Karimipourfard, S. Sina, M. Sadeghi, S. Karimkhani, A. Zabihi","doi":"10.1109/NSS/MIC44867.2021.9875629","DOIUrl":"https://doi.org/10.1109/NSS/MIC44867.2021.9875629","url":null,"abstract":"Patient-specific internal dosimetry with high accuracy is the most significant issue in the field of nuclear medicine. In recent researches has been a dramatic alter in different methods to compute the correct organ doses according to injected radioactivity distribution, admittedly Monte Carlo simulation has brought high accuracy results in voxel-based dosimetry techniques.In this study patient datasets who were injected F18-FDG (2 subjects, normal cases, 60±8.2 kg, injected activity:10 ±0.5 Mbq) were acquired in 3 times sequences (30,60,90 min post-injection). The CT and PET images were used as attenuation maps and activity distribution of patient phantom respectively. Hence the CT and PET images have been registered by 3D Slicer software to achieve the same matrix and pixel sizes. The regions of interest were segmented on the CT images that entailed kidneys, spleen, bladder, lung, pancreas, liver, stomach, gallbladder organs. The segmenting ROI of CT images were reconstructed by MATLAB codes and the voxelized phantom and voxelized source of each patient at specific times were generatedBy way of conclusion, the F18-FDG dose distribution in patient-specific phantom has investigated and bladder, spleen, kidney absorbed the most activity as we expected by PET images, the mean S- factors and absorbed dose were computed as 4.02e-05 mGy/Mbq & 0.67±1.2 mGy, 5.29e-07 mGy/Mbq & 1.67±0.45mGy, 2.23e-07 mGy/Mbq & 0.57±0.52 mGy for bladder, kidney and spleen organs. Monte Carlo methods are shown the best results aspect of accuracy but the essential issue that prevents clinical uses is excessive computational cost and time.","PeriodicalId":347712,"journal":{"name":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115188973","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}

引用次数: 0

In Silico Comparison of Additive and Subtractive Charge Sharing Correction Algorithms at Medically Relevant Fluxes in Pixelated x-ray Photon Counting Multispectral Detectors 像素化x射线光子计数多光谱探测器中医学相关通量的加和减电荷共享校正算法的计算机比较

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875900

O. P. Pickford Scienti, D. Darambara

引用次数: 0

Time-of flight (TOF) Image Synthesis from non-TOF PET Using Deep Learning 基于深度学习的非TOF PET飞行时间(TOF)图像合成

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875931

Amirhossein Sanaat, A. Akhavanallaf, I. Shiri, Y. Salimi, Hossein ARABI, H. Zaidi

{"title":"Time-of flight (TOF) Image Synthesis from non-TOF PET Using Deep Learning","authors":"Amirhossein Sanaat, A. Akhavanallaf, I. Shiri, Y. Salimi, Hossein ARABI, H. Zaidi","doi":"10.1109/NSS/MIC44867.2021.9875931","DOIUrl":"https://doi.org/10.1109/NSS/MIC44867.2021.9875931","url":null,"abstract":"Time-of-flight (TOF) PET technology demonstrated superior image quality and quantitative performance translated into a considerable increase in SNR-gain, noise reduction, and robustness to artefacts, thus improving confidence in clinical diagnosis. This work aimed to assess the performance of TOF PET synthesis from non-TOF PET images using deep learning techniques. One hundred forty 18F-FDG brains PET/CT clinical studies were acquired in list-mode format enabling the generation of non-TOF and TOF sinograms. The TOF sinograms were split into seven time bins (0, ±1, ±2, ±3). An iterative algorithm was used to reconstruct images corresponding to non-TOF and TOF sinograms. A modified cycle-consistent generative adversarial network (CycleGAN) was implemented to predict TOF images from non-TOF images in both sinogram and image domains. In the first approach, a model was trained to predict TOF from non-TOF images, whereas in the second approach, 7 models were trained to synthesize 7 time bin sinograms from the non-TOF sinograms. Quantitative analysis revealed improvement peak signal-to-noise ratio (PSNR) by 9% and 12% in the synthesized TOF images compared to the corresponding non-TOF images in the sinogram and image domains, respectively.","PeriodicalId":347712,"journal":{"name":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792299","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}

引用次数: 0

Deep Learning-based Low-dose Cardiac Gated SPECT: Implementation in Projection Space vs. Image Space 基于深度学习的低剂量心脏门控SPECT:投影空间与图像空间的实现

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875770

Narges Aghakhan Olia, A. Kamali-Asl, S. H. Tabrizi, P. Geramifar, P. Sheikhzadeh, Hossein ARABI, H. Zaidi

{"title":"Deep Learning-based Low-dose Cardiac Gated SPECT: Implementation in Projection Space vs. Image Space","authors":"Narges Aghakhan Olia, A. Kamali-Asl, S. H. Tabrizi, P. Geramifar, P. Sheikhzadeh, Hossein ARABI, H. Zaidi","doi":"10.1109/NSS/MIC44867.2021.9875770","DOIUrl":"https://doi.org/10.1109/NSS/MIC44867.2021.9875770","url":null,"abstract":"The reduction of radiation exposure in SPECT-MPI is an important research topic. However, lowering the injected activity degrades image quality, thus impacting the diagnostic accuracy of this modality. In this study, we enrolled a total of 335 clinical gated SPECT-MPI images from a dedicated cardiac SPECT scanner acquired in list-mode format. All patients underwent a two-day rest/stress protocol and the obtained gated images were retrospectively used to convert low-dose to standard-dose images in both projection and image spaces. A deep generative adversarial network was employed to predict standard-dose images from 50% low-dose images. The proposed network was evaluated using quantitative metrics, such as the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), and structural similarity index measure (SSIM). Moreover, a Pearson correlation coefficient analysis was performed on the half-dose and predicted standard-dose images with respect to the reference standard-dose images. The results demonstrated that the highest PSNR (46.30 ± 2.23) and SSIM (0.98 ± 0.01), and the lowest RMSE (1.32 ± 0.54) were obtained from the image space implementation. Pearson analysis showed that the predicted standard-dose images yielded ρ = 0.960 ± 0.011 and ρ = 0.947 ± 0.027 in the image and projection spaces, respectively. Overall, considering the quantitative metrics, the noise was effectively suppressed in the predicted standard-dose images for both implementations. Yet, standard-dose image estimation in the image space resulted in superior quantitative accuracy and image quality.","PeriodicalId":347712,"journal":{"name":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114488990","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}

引用次数: 1

The Reconstruction Method Using Compressed Sensing and Convolutional Neural Network for PROPELLER MRI in Head 基于压缩感知和卷积神经网络的头部螺旋桨MRI重建方法

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875646

Yuta Matsumoto, Kensuke Hori, K. Tadano, S. Kuhara, Yuta Endo, T. Hashimoto

{"title":"The Reconstruction Method Using Compressed Sensing and Convolutional Neural Network for PROPELLER MRI in Head","authors":"Yuta Matsumoto, Kensuke Hori, K. Tadano, S. Kuhara, Yuta Endo, T. Hashimoto","doi":"10.1109/NSS/MIC44867.2021.9875646","DOIUrl":"https://doi.org/10.1109/NSS/MIC44867.2021.9875646","url":null,"abstract":"PROPELLER MRI is a method of reconstruction from the collected data of rectangular regions (blades) rotating around the origin of the k-space. This method can compensate for the motion of the subject by using the phase and rotation between the blades. Collecting more blades will improve the accuracy of the correction but will increase the imaging time. On the other hand, reducing the number of phase encodings and blades for shortening the imaging time results in streak artifacts. For this study, we attempted to improve the image quality of the PROPELLER MRI in head by reconstructing using less data with compressed sensing (CS) and a convolutional neural network (CNN), which is a deep-learning method. For k-space data with sampling rates of 11% to 54% by varying the width and number of blades, we compared three patterns of the reconstruction method: A) with only CS, B) with only CNN, and C) with both CS and CNN. For all sampling rates, the method with CS and CNN yielded the best evaluation value; therefore, it is suggested that the image quality could be improved by reconstructing using CS and CNN when the sampling rate is low.","PeriodicalId":347712,"journal":{"name":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121844872","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}

引用次数: 1

Post-Reconstruction PET Resolution Modelling by Synthesised Image Reconstruction 基于合成图像重建的重建后PET分辨率建模

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Pub Date : 2021-10-16 DOI: 10.1109/NSS/MIC44867.2021.9875478

L. Vass, A. Reader

{"title":"Post-Reconstruction PET Resolution Modelling by Synthesised Image Reconstruction","authors":"L. Vass, A. Reader","doi":"10.1109/NSS/MIC44867.2021.9875478","DOIUrl":"https://doi.org/10.1109/NSS/MIC44867.2021.9875478","url":null,"abstract":"Resolution recovery techniques in PET aim to improve spatial resolution, signal-to-noise ratio and quantitative accuracy. Whilst several factors are responsible for the degradation of spatial resolution, image-space point spread function (PSF) modelling has been developed to compensate for resolution losses. For example, the widely used Richardson-Lucy (RL) algorithm can be used to model the PSF and improve spatial resolution. However, when the RL algorithm is applied to post-reconstruction resolution recovery, noise rapidly develops even in early iterations. On the other hand, incorporation of the PSF directly into iterative reconstruction algorithms has shown potential benefits (depending on the task), with less rapid noise accumulation compared to RL, and with resolution improvements compared to conventional reconstruction with no PSF modelling. However, PSF-based reconstruction requires raw projection data and knowledge of the forward/back-projectors relating to the scanner's geometry, and if these are unavailable the technique becomes infeasible. In this proof-of-concept work, we propose a novel post-reconstruction resolution recovery technique based on synthesising an image reconstruction problem. Tomographic data are synthesised from the supplied image and a new inverse problem with embedded PSF resolution modelling is then solved. This work evaluates the proposed method in 2D using a simulated phantom at various count levels. We compare the performance of the proposed method with the RL algorithm and PSF-based maximum likelihood expectation maximisation (MLEM) reconstruction. In conditions which match typical clinical scans (e.g. low iteration numbers and counts), the proposed method achieves a substantially lower root mean square error than the RL algorithm. The performance of the proposed method is comparable to PSF-based reconstruction despite the proposed method having no access to the original sinogram data.","PeriodicalId":347712,"journal":{"name":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128436879","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}

引用次数: 1