A High-Performance System Architecture for Medical Imaging

Computational fluid dynamics

Purpose: The purpose of this article is to offer an overview of the recent applications of cloud computing in medical imaging and their advances in the field. It reviews existing scientific and academic literature on cloud computing-based platforms and solutions for medical image analysis and storage of medical imaging data. Materials and methods: This article uses available scientific literature on the applications of cloud computing in medical imaging from PubMed, Google Scholar and ScienceDirect. Results: The review shows that interest and research in cloud computing applications in medical imaging has increased in recent years. This has led to new and more effective platforms and solutions for medical image analysis and storage of medical imaging data. Innovative applications of cloud computing in medical imaging try to address ethical and security concerns using authentication, encryption, anonymization and access controls. Conclusions: Cloud computing offers promising applications in medical imaging. Notwithstanding, further research is necessary to demonstrate their effectiveness, safety and security in a medical setting.

BI&T Tell me about the Medical Imaging and Technology Alliance. David Fisher The Medical Imaging & Technology Alliance (MITA), a division of the National Electrical Manufacturers Association (NEMA), is the leading organization and collective voice of medical imaging equipment manufacturers, innovators, and product developers. It represents companies whose sales comprise more than 90 percent of the global market for medical imaging technology. The goals of MITA are to: • Increase the awareness and understanding of the value of medical imaging • Achieve efficient and reasonable regulation of medical imaging technologies • Interact with appropriate government agencies on reimbursement and technology assessment policies • Expand the global acceptance of the digital communications standard (DICOM) that allows digital imaging technologies to interact seamlessly • Improve regulatory harmonization of the global market for medical imaging products • Develop and represent industry positions in technical, trade and other issues • Provide market data unique to this industry

During the 1990s the radiation oncology community embraced the 3D conformal radiation therapy (3DCRT; Perez 1995; Purdy 1997) as a standard of care for many treatment sites. This acceptance stems from the postulate that 3DCRT allows for dose escalation to tumor volumes while preserving tolerance doses for normal structures. It is believed that 3DCRT improves outcomes while minimizing complications and side effects. Researchers and manufacturers have developed a variety of treatment planning and delivery devices for 3DCRT. These efforts have culminated in widespread use of intensity modulated radiation therapy (IMRT; Ezzel et al. 2003), a delivery of nonuniform radiation beam intensities that have been calculated by a computer-based optimization technique. Modern treatment planning systems can relatively effi ciently calculate optimized treatment plans to complex target volumes and linear accelerators can deliver these treatment plans with high precision in short treatment times. One of the fundamental components of the 3DCRT and IMRT process is a volumetric patient scan. This scan is the basis for the treatment plan and the main guide for the design of treatment portals and dose distributions. The image study is used to delineate target volumes and normal structures. The quality of the study and the data contained in the study have a direct impact on the patient treatment and potentially on the outcomes and complications. The 3DCRT process based on X-ray computed tomography (CT) imaging was fi rst described in the 1980s (Goitein and Abrams 1983; Goitein et al. 1983; Sherouse et al. 1990). Since then, CT has remained the primary imaging modality in radiation therapy. X-ray computed tomography offers excellent spatial integrity, which is important for accurate patient treatments (Mutic et al. 2003a). Computed tomography also provides radiation interaction properties of imaged tissues for heterogeneity based dose calculations and digitally reconstructed radiographs (DRRs) can be calculated from volumetric CT data sets (Sherouse et al. 1990). The three main limitation of CT are relatively poor soft tissue contrast, the fact that motion information is generally not appreciated as CT images are acquired as snapshots in time, and limitation to record functional properties of the imaged tissues. Magnetic resonance imaging (MRI), nuclear medicine imaging (single photon emission tomography (SPECT), and positron emission tomography (PET)) can provide certain advantages over CT with respect to these limitations. With regard to the fi rst limitation, MRI has a superior soft tissue contrast to CT and can provide often more useful anatomic information (Fig. 11.1). The MRI is often preferred for treatment planning of the central nervous system tumors and some other treatment sites. The two main MRI limitations are susceptibility to spatial distortions and the small size of scanner openings which in turn limits the size of CONTENTS

Medical image super-resolution using a relativistic average generative adversarial network

Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

Terahertz (THz) is an electromagnetic spectrum with a frequency range from 0.1 to 10 THz, which is located in between the microwave and infrared regions. The unique features of THz waves make them eligible for use in various medical applications. The THz imaging is one of them, which is mainly based on the analysis and processing of the transmission and reflection spectrum information of the sample. This chapter mainly presents the research status and prospects of several THz medical imaging systems and their applications for medical imaging in biological tissues. As the demand for technology grows, high-performance THz imaging systems are becoming indispensable. The ability of the THz time-domain spectroscopy (THz-TDS) system to extract spectra from amplitude and phase information opens virtually unlimited possibilities for imaging applications. The performance achievements of THz-TDS-based imaging with potential research on its fast-imaging components for solving the existing limitations of imaging speed are also presented. Furthermore, the latest developments of several THz-TDS-based imaging methods, including tomography imaging and near-field imaging, are highlighted with their performance improvement. Additionally, the rapid development of THz-TDS as a highly versatile analytical tool for the characterisation of pharmaceutical materials has drawn considerable attention. One of the greatest biomedical potentials of THz imaging is the use of molecular spectroscopy for diagnostics, which is exponentially advanced and moving closer to progress. It is now evident that different types of biomolecules leave distinctive spectral fingerprints in the THz region, which considerably widens the coverage of its technology application including in-vitro and in-vivo measurements of small molecules of clinical importance in point of care and diagnostic systems. In-vivo molecular imaging is considered the next frontier in medical diagnostics, which would be ideally performed non-invasively. Recent achievements in the field of medical imaging have dramatically enhanced the early detection and treatment of many pathological conditions. THz imaging systems can help in detecting early cancer before it is visible or sensitive to any other identification resources. The THz images can distinguish between healthy tissue and basal cell carcinoma and therefore help in mapping the exact margins of early-stage tumours. By obtaining both frequency and time-domain information, the THz imaging can ensure enhanced detection of cancer and provide sharper imaging and molecular fingerprinting. THz biomedical imaging has become a modality of interest due to its ability to simultaneously acquire both image and spectral information. Advanced digital image processing algorithms are greatly needed to assist in screening, diagnosis and treatment. Finally, we summarise the obstacles in the way of the application of THz biomedical imaging application technology in clinical detection, which need to be investigated and overcome in the future.

Artificial intelligence, including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic concepts in deep learning and provides an overview of its recent history and its application in tomographic reconstruction as well as other applications in medical imaging to reduce patient radiation dose, as well as a brief description of previous tomographic reconstruction techniques. This review also describes the commonly used deep learning techniques as applied to tomographic reconstruction and draws parallels to current reconstruction techniques. Finally, this paper reviews some of the estimated dose reductions in CT and positron emission tomography in the recent literature enabled by deep learning, as well as some of the potential problems that may be encountered such as the obscuration of pathology, and highlights the need for additional clinical reader studies from the imaging community.

Medical imaging has played an essential role in medicine and disease diagnosis. Medical images from a single modality contain limited data about the organ. On the other hand, images from different modalities contain valuable structural and functio nal data about an organ. Medical image fusion (MIF) strategies integrate complementary information from two medical images captured using distinct modalities. This paper offered a new multimodal MIF approach using the parameter-adaptive pulse-coupled neural networks (PA-PCNN) within the non-subsampled contourlet transform (NSCT). The NSCT decomposes those images into high- and low-frequency bands. PA-PCNN combines those bands. The fused image was created using the inverse of the NSCT approach. To prove the proposed approach’s performance, we appoint a variety of medical images like computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission CT (SPECT), and positron emission tomography (PET). Our experiments use five fusion metrics to validate the proposed approach’s performance, such as entropy (EN), mutual information (MI), weighted edge information (QAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{AB/F}$$\\end{document}), nonlinear correlation information entropy (Qncie\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{ncie}$$\\end{document}), and average gradient (AG). Outcomes show that the proposed approach achieves high overall performance in visual and objective characteristics when compared with five well-known MIF methods. The average values for EN, MI, QAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{AB/F}$$\\end{document}, Qncie\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{ncie}$$\\end{document}, and AG with the proposed approach are 5.2144,3.1282,.6600,.8071, and 8.9874, respectively.

The medical image can be divided into two major classes that are anatomical image and functional image. Magnetic resonance image (MRI) and computed tomography (CT) belong to the anatomical image, which can show an outline of an organ and a tissue clearly. The functional image can show the image of the organ and the tissue metabolism situation. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are also part of the functional image. In this paper, we use 4-[18F]-ADAM to measure serotonin transporter changes and find the relation with major depressive disorder in CT / PET three-dimensional image. In clinical diagnosis, based on the video imaging methods can be divided into the anatomy and function of these two categories. Therefore, the combination of anatomical and functional imaging can provide more useful information. In the study, we use CT image to get the reference position, and combine with isotope metabolic situation from PET. We use the integrated image to do 3D image and count serotonin transporter with different points in time, and measure the variance about serotonin transporter changes in the organization of the metabolism and distribution. We use the variance to find the relation and compare the score using Hamilton Depression Rating Scale by experienced psychiatrists. After the test case, measuring the size distribution of serotonin transporter to depression patients can do a preliminary classification.

2-[18F]fluoro-D-glucose ([18F]FDG) is a versatile molecule in nuclear medicine that has evolved into a vital radiotracer in medical imaging applications via positron emission tomography (PET) [18F]FDG is derived from its derivative, 2-deoxyD-glucose (2-DG), where the triflate group is attached to carbon-2 [18F]FDG serves as a crucial non-invasive diagnostic tool and is prominently utilized in non-invasive imaging of various metastatic diseases, particularly cancer imaging. Its importance as a tracer has been further enhanced by its unexpected attribute of generating a low body background through excretion, leading to its effective application in PET/CT for highly-sensitive and specific tumor detection. This chapter provides insight into the synthesis of [18F]FDG, employing various reaction protocols such as electrophilic and nucleophilic processes. This chapter also summarized the purification and their quality assurance methods and highlighted the distinct challenges associated with each. The nucleophilic technique produces [18F]FDG with a higher yield and purity than the electrophilic method for routine manufacture. Commercially devoted automated modules for FDG production use this method, demonstrating its widespread use in clinical imaging. Nucleophilic reactions of [18F]fluoride ions attacking the C-2 position of mannose triflate to produce FDG are routine in clinical imaging. The final [18F]FDG product satisfies safety, purity, and efficacy standards through rigorous quality control and assurance. The trajectory from glucose discovery to the development of [18F]FDG exemplifies the continuing advancement of medical imaging methods. FDG's accomplishment shows how biology, chemistry, and medical technology are interrelated, providing a better understanding and treatment of complicated diseases like cancer.

Chronic Periaortitis

Speckle Noise is a natural characteristic of medical ultrasound images. It is a term used for the granular form that appears in B-Scan and can be considered as a kind of multiplicative noise. Speckle Noise reduces the ability of an observer to distinguish fine details in diagnostic testing. It also limits the effective implementation of image processing such as edge detection, segmentation and volume rendering in 3 D. Therefore; treatment methods of speckle noise were sought to improve the image quality and to increase the capacity of diagnostic medical ultrasound images. Such as median filters, Wiener and linear filters (Persona & Malik, SRAD ... ..).The method used in this work is 2-D translation invariant forward wavelet transform, it is used in image processing, including noise reduction applications in medical imaging.

Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging. DatasetGAN is a recently proposed framework based on modern GANs that can synthesize high-quality segmented images while requiring only a small set of annotated training images. The synthesized annotated images could be potentially employed for many medical imaging applications, where images with segmentation information are required. However, to the best of our knowledge, there are no published studies focusing on its applications to medical imaging. In this work, preliminary studies were conducted to investigate the utility of DatasetGAN in medical imaging. Three improvements were proposed to the original DatasetGAN framework, considering the unique characteristics of medical images. The synthesized segmented images by DatasetGAN were visually evaluated. The trained DatasetGAN was further analyzed by evaluating the performance of a pre-defined image segmentation technique, which was trained by the use of the synthesized datasets. The effectiveness, concerns, and potential usage of DatasetGAN were discussed.

The usefulness and complementarity of gallium (67Ga) scintigraphy and computed tomography (CT) in the management of patients with lymphoma have been extensively demonstrated. Owing to a lack of anatomical landmarks and physiological distribution of the tracer, precise localisation of abnormalities on 67Ga scintigraphy can be difficult. As fusion imaging techniques between single-photon emission tomography (SPET) and CT have been developed recently, we investigated whether use of CT/67Ga SPET fusion imaging could help in the interpretation of 67Ga scintigraphy. From November 1999 to May 2001, 52 consecutive fusion studies were performed in 38 patients [22 patients with Hodgkin's disease (HD) and 16 patients with non-Hodgkin's lymphoma (NHL)] as part of pre-treatment staging (n=13), treatment evaluation (n=20) or evaluation of suspected recurrence (n=19). 67Ga scintigraphy was carried out 2 and 6 days following the injection of 185-220 MBq 67Ga citrate. On day 2, 67Ga SPET and CT were performed, focussing on the chest and/or the abdomen/pelvis. Data from each imaging method were co-registered using external markers. 67Ga scintigraphy and CT were initially interpreted independently by nuclear medicine physicians and radiologists. CT/67Ga SPET fusion studies were then jointly interpreted and both practitioners indicated when fusion provided additional information in comparison with CT and 67Ga SPET alone. Image fusion was considered to be of benefit in 12/52 (23%) studies which were performed for initial staging (n=4), treatment evaluation (n=4) or evaluation of suspected recurrence (n=4). In these cases, image fusion allowed either confirmation and/or localisation of pathological gallium uptake (n=10) or detection of lesions not visible on CT scan (n=2). Fusion was relevant for discrimination between osseous lesions and lymph node involvement adjacent to bone, especially in the thoracic and lumbar spine and pelvis. In the abdomen and pelvis, fusion helped to differentiate physiological bowel elimination from abnormal uptake, and assisted in precisely locating uptake in neighbouring viscera of the left hypochondrium, including the spleen, left liver lobe, coeliac area, stomach wall and even the splenic flexure. At the thoracic level, fusion also proved useful for demonstrating clearly the relationships of abnormal foci to the pleura, hepatic dome, mediastinum, ribs or thoracic spine. Clinical management was altered by fusion imaging in one patient (chemotherapy was given instead of radiotherapy) and was potentially affected in three other patients (in that, in conjunction with other factors, the results of fusion imaging had an influence on the decision regarding use of irradiation and especially the treatment volume). In conclusion, CT/67Ga SPET fusion imaging allowed precise localisation of gallium uptake and correct attribution to the involved viscera, thereby altering the diagnosis in 20%-25% of studies in comparison with CT and 67Ga SPET analyses alone. CT/67Ga SPET fusion therefore appears valuable in facilitating the interpretation of 67Ga scintigraphy and we recommend its use in patients with lymphoma when CT and 67Ga scintigraphy are planned.