Medical Imaging 2019: Physics of Medical Imaging最新文献

Patient-specific noise power spectrum measurement via generative adversarial networks (Conference Presentation) 基于生成对抗网络的患者噪声功率谱测量(会议报告)

Medical Imaging 2019: Physics of Medical Imaging Pub Date : 2019-03-14 DOI: 10.1117/12.2513207

Chengzhu Zhang, D. Gomez-Cardona, Yinsheng Li, J. Montoya, Guang-Hong Chen

{"title":"Patient-specific noise power spectrum measurement via generative adversarial networks (Conference Presentation)","authors":"Chengzhu Zhang, D. Gomez-Cardona, Yinsheng Li, J. Montoya, Guang-Hong Chen","doi":"10.1117/12.2513207","DOIUrl":"https://doi.org/10.1117/12.2513207","url":null,"abstract":"A deep learning Generative Adversarial Networks (GANs) were developed and validated to provide an accurate way of direct NPS estimation from a single patient CT scan. GANs were utilized to map a white noise input to a CT noise realization with correct CT noise correlations specific to a single local uniform ROI. To achieve this, a two-stage strategy was developed. In the pre-training stage, ensembles of 64x64 MBIR noise-only images of a quality assurance phantom were used as training samples to jointly train the generator and discriminator. They were fined-tuned using training samples from a single 101x101 ROI of an abdominal anthropomorphic phantom. Results from GANs and physical scans were compared in terms of its mean frequency and radial averaged NPS. This workflow was extended to a patient case where reference dose and 25% of reference dose CT scans were provided for fine-tuning. GANs generated noise-only image samples that are indistinguishable from physical measurement. The overall mean frequency discrepancy between NPS generated from GANs and those from physically acquired data was 0.2% for anthropomorphic phantom validation. The KL divergence for 1D radial averaged NPS profile of these two NPS acquisitions was 2.2×10^(-3). Statistical test indicates trained GANs generated equivalent NPS to physical scans. In clinical patient-specific NPS studies, it showed a distinction between the reference dose case and 25% of reference dose case. It was demonstrated the GANs characterized the properties of CT noise in terms of its mean frequency and 1D NPS profile shape.","PeriodicalId":151764,"journal":{"name":"Medical Imaging 2019: Physics of Medical Imaging","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114524333","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

Volumetric scout CT images reconstructed from conventional two-view radiograph localizers using deep learning (Conference Presentation) 利用深度学习从传统的双视图x线片定位器重建体积侦察CT图像(会议报告)

Medical Imaging 2019: Physics of Medical Imaging Pub Date : 2019-03-14 DOI: 10.1117/12.2513133

J. Montoya, Chengzhu Zhang, Ke Li, Guang-Hong Chen

{"title":"Volumetric scout CT images reconstructed from conventional two-view radiograph localizers using deep learning (Conference Presentation)","authors":"J. Montoya, Chengzhu Zhang, Ke Li, Guang-Hong Chen","doi":"10.1117/12.2513133","DOIUrl":"https://doi.org/10.1117/12.2513133","url":null,"abstract":"In this work, a deep neural network architecture was developed and trained to reconstruct volumetric CT images from two-view radiograph scout localizers. In clinical CT exams, each patient will receive a two-view scout scan to generate both lateral (LAT) and anterior-posterior (AP) radiographs to help CT technologist to prescribe scanning parameters. After that, patients go through CT scans to generate CT images for clinical diagnosis. Therefore, for each patient, we will have two-view radiographs as input data set and the corresponding CT images as output to form our training data set. In this work, more than 1.1 million diagnostic CT images and their corresponding projection data from 4214 clinically indicated CT studies were retrospectively collected. The dataset was used to train a deep neural network which inputs the AP and LAT projections and outputs a volumetric CT localizer. Once the model was trained, 3D localizers were reconstructed for a validation cohort and results were analyzed and compared with the standard MDCT images. In particular, we were interested in the use of 3D localizers for the purpose of optimizing tube current modulation schemes, therefore we compared water equivalent diameters (Dw), radiologic paths and radiation dose distributions. The quantitative evaluation yields the following results: The mean±SD percent difference in Dw was 0.6±4.7% in 3D localizers compared to the Dw measured from the conventional CT reconstructions. 3D localizers showed excellent agreement in radiologic path measurements. Gamma analysis of radiation dose distributions indicated a 97.3%, 97.3% and 98.2% of voxels with passing gamma index for anatomical regions in the chest, abdomen and pelvis respectively. These results demonstrate the great success of the developed deep learning reconstruction method to generate volumetric scout CT image volumes.","PeriodicalId":151764,"journal":{"name":"Medical Imaging 2019: Physics of Medical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131330855","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: 4

Frequency-dependent MTF and DQE of photon-counting x-ray imaging detectors (Conference Presentation) 光子计数x射线成像探测器的频率相关MTF和DQE(会议报告)

Medical Imaging 2019: Physics of Medical Imaging Pub Date : 2019-03-14 DOI: 10.1117/12.2512964

J. Tanguay, N. Mantella, I. Cunningham

{"title":"Frequency-dependent MTF and DQE of photon-counting x-ray imaging detectors (Conference Presentation)","authors":"J. Tanguay, N. Mantella, I. Cunningham","doi":"10.1117/12.2512964","DOIUrl":"https://doi.org/10.1117/12.2512964","url":null,"abstract":"Theoretical modeling of the performance of x-ray imaging detectors enables understanding relationships between the physics of x-ray detection and x-ray image quality, and enables theoretical optimization of novel x-ray imaging techniques and technologies. We present an overview of a framework for theoretical modeling of the frequency-dependent signal and noise properties of single-photon-counting (SPC) and energy-resolving x-ray imaging detectors. We show that the energy-response function, large-area gain, modulation transfer function (MTF), noise power spectrum (NPS) (including spatio-energetic noise correlations) and detective quantum efficiency (DQE) of SPC and energy-resolving x-ray imaging detectors are related through the probability density function (PDF) describing the number electron-hole (e-h) pairs collected in detector elements following individual x-ray interactions. We demonstrate how a PDF-transfer approach can be used to model analytically the MTF and NPS, including spatio-energetic noise correlations, of SPC and energy-resolving x-ray imaging detectors. Our approach enables modeling the combined effects of stochastic conversion gain, electronic noise, characteristic emission, characteristic reabsorption, coulomb repulsion and diffusion of e-h pairs and energy thresholding on the MTF and NPS. We present applications of this framework to (1) analysis of the frequency-dependent DQE of SPC systems that use cadmium telluride (CdTe) x-ray converters, and (2) analysis of spatio-energetic noise correlations in CdTe energy-resolving x-ray detectors. The developed framework provides a platform for theoretical optimization of next-generation SPC and energy-resolving x-ray imaging detectors.","PeriodicalId":151764,"journal":{"name":"Medical Imaging 2019: Physics of Medical Imaging","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133245518","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

World’s deepest-penetration and fastest optical cameras: photoacoustic tomography and compressed ultrafast photography (Conference Presentation) 世界上穿透最深、速度最快的光学相机:光声断层成像和压缩超快摄影(会议报告)

Medical Imaging 2019: Physics of Medical Imaging Pub Date : 2019-03-14 DOI: 10.1117/12.2516381

Lihong V. Wang

{"title":"World’s deepest-penetration and fastest optical cameras: photoacoustic tomography and compressed ultrafast photography (Conference Presentation)","authors":"Lihong V. Wang","doi":"10.1117/12.2516381","DOIUrl":"https://doi.org/10.1117/12.2516381","url":null,"abstract":"We developed photoacoustic tomography to peer deep into biological tissue. Photoacoustic tomography (PAT) provides in vivo omniscale functional, metabolic, molecular, and histologic imaging across the scales of organelles through organisms. We also developed compressed ultrafast photography (CUP) to record 10 trillion frames per second, 10 orders of magnitude faster than commercially available camera technologies. CUP can tape the fastest phenomenon in the universe, namely, light propagation, and can be slowed down for slower phenomena such as combustion.\u0000\u0000PAT physically combines optical and ultrasonic waves. Conventional high-resolution optical imaging of scattering tissue is restricted to depths within the optical diffusion limit (~1 mm in the skin). Taking advantage of the fact that ultrasonic scattering is orders of magnitude weaker than optical scattering per unit path length, PAT beats this limit and provides deep penetration at high ultrasonic resolution and high optical contrast by sensing molecules. Broad applications include early-cancer detection and brain imaging. The annual conference on PAT has become the largest in SPIE’s 20,000-attendee Photonics West since 2010.\u0000\u0000CUP can image in 2D non-repeatable time-evolving events. CUP has a prominent advantage of measuring an x, y, t (x, y, spatial coordinates; t, time) scene with a single exposure, thereby allowing observation of transient events occurring on a time scale down to 100 femtoseconds, such as propagation of a light pulse. Further, akin to traditional photography, CUP is receive-only—avoiding specialized active illumination required by other single-shot ultrafast imagers. CUP can be coupled with front optics ranging from microscopes to telescopes for widespread applications in both fundamental and applied sciences.","PeriodicalId":151764,"journal":{"name":"Medical Imaging 2019: Physics of Medical Imaging","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123492902","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

Ultra-low dose PET reconstruction using generative adversarial network with feature matching (Conference Presentation) 基于特征匹配的生成对抗网络的超低剂量PET重建(会议报告)

Medical Imaging 2019: Physics of Medical Imaging Pub Date : 2019-03-14 DOI: 10.1117/12.2512946

J. Ouyang

引用次数: 4