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Abstract Interpretation of volumetric medical images represents a rapidly growing proportion of the workload in radiology. However, relatively little is known about the strategies that best guide search behavior when looking for a...
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Abstract Interpretation of volumetric medical images represents a rapidly growing proportion of the workload in radiology. However, relatively little is known about the strategies that best guide search behavior when looking for abnormalities in?volumetric images. Although there is extensive literature on two-dimensional medical image perception, it is an open question whether the conclusions drawn from these images can be generalized to volumetric images. Importantly, volumetric images have distinct characteristics (e.g., scrolling through depth, smooth-pursuit eye-movements, motion onset cues, etc.) that should be considered in future research. In this manuscript, we will review the literature on medical image perception and discuss relevant findings from basic science that can be used to generate predictions about expertise in volumetric image?interpretation. By better understanding search through volumetric images, we may be able to identify common sources of error, characterize the optimal strategies for searching through depth, or develop new training and assessment techniques for radiology residents.
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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical appl...
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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.
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At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme ...
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At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.
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MIAPS (medical image access and presentation system) is a web-based system designed for remotely accessing and presenting DICOM image. MIAPS is accessed with web browser through the Internet. MIAPS provides four features: DICOM im...
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MIAPS (medical image access and presentation system) is a web-based system designed for remotely accessing and presenting DICOM image. MIAPS is accessed with web browser through the Internet. MIAPS provides four features: DICOM image retrieval, maintenance, presentation and output. MIAPS does not intent to replace sophisticated commercial and open source packages, but it provides a web-based solution for teleradiology and medical image sharing. The system has been evaluated by 39 hospitals in China for 10 months.
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This article reviews some of the more recent advances and trends in the area of biomedical imaging. Real-time multimodality imaging and image-guided interventions are presented as well as other fast growing areas of interdisciplin...
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This article reviews some of the more recent advances and trends in the area of biomedical imaging. Real-time multimodality imaging and image-guided interventions are presented as well as other fast growing areas of interdisciplinary research and development. Segmentation, registration and spatial-temporal integration in medical image processing are also discussed.
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Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, ...
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Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database. The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.
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This paper presents work on a PC-based software solution for evaluation of burn wounds, leading to automatic registration of infra-red and visible light images. The algorithm of reference points detection, crucial for the registra...
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This paper presents work on a PC-based software solution for evaluation of burn wounds, leading to automatic registration of infra-red and visible light images. The algorithm of reference points detection, crucial for the registration procedure, is presented in details. Enhancements requested by the physicians are also outlined.
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Purpose: Scatter contamination is detrimental to image quality in dedicated cone‐beam breast CT (CBBCT), resulting in cupping artifacts and loss of contrast in reconstructed images. Such effects impede visualization of breast les...
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Purpose: Scatter contamination is detrimental to image quality in dedicated cone‐beam breast CT (CBBCT), resulting in cupping artifacts and loss of contrast in reconstructed images. Such effects impede visualization of breast lesions and the quantitative accuracy. Previously, we proposed a library‐based software approach to suppress scatter on CBBCT images. In this work, we quantify the efficacy and stability of this approach using datasets from 15 human subjects. Methods: A pre‐computed scatter library is generated using Monte Carlo simulations for semi‐ellipsoid breast models and homogeneous fibroglandular/adipose tissue mixture encompassing the range reported in literature. Projection datasets from 15 human subjects that cover 95 percentile of breast dimensions and fibroglandular volume fraction were included in the analysis. Our investigations indicate that it is sufficient to consider the breast dimensions alone and variation in fibroglandular fraction does not significantly affect the scatter‐to‐primary ratio. The breast diameter is measured from a first‐pass reconstruction; the appropriate scatter distribution is selected from the library; and, deformed by considering the discrepancy in total projection intensity between the clinical dataset and the simulated semi‐ellipsoidal breast. The deformed scatter‐distribution is subtracted from the measured projections for scatter correction. Spatial non‐uniformity (SNU) and contrast‐to‐noise ratio (CNR) were used as quantitative metrics to evaluate the results. Results: On the 15 patient cases, our method reduced the overall image spatial non‐uniformity (SNU) from 7.14%±2.94% (mean ± standard deviation) to 2.47%±0.68% in coronal view and from 10.14%±4.1% to 3.02% ±1.26% in sagittal view. The average contrast to noise ratio (CNR) improved by a factor of 1.49±0.40 in coronal view and by 2.12±1.54 in sagittal view. Conclusion: We demonstrate the robustness and effectiveness of a library‐based scatter correction method using patient datasets with large variability in breast dimensions and composition. The high computational efficiency and simplicity in implementation make this attractive for clinical implementation. Supported partly by NIH R21EB019597, R21CA134128 and R01CA195512.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Purpose: We have developed a method, called respiratory motion guided 4DCBCT (RMG‐4DCBCT), in which the gantry speed and projection frequency are varied in response to the patient's real‐time respiratory signal to eliminate stre...
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Purpose: We have developed a method, called respiratory motion guided 4DCBCT (RMG‐4DCBCT), in which the gantry speed and projection frequency are varied in response to the patient's real‐time respiratory signal to eliminate streaking artifacts and to suppress duplicate projections in 4DCBCT images. In 2015, we realized RMG‐4DCBCT on an Elekta Synergy linear accelerator with a mechanical relay to suppress projections and a potentiometer to adjust the gantry speed in response to the patient's real‐time respiratory signal. The aim of this study was to analyse the image quality to determine what can and cannot be controlled. Methods: Using RMG‐4DCBCT, we acquired 40 (RMG‐4DCBCT_40) and 60 (RMG‐4DCBCT_60) equally spaced projections per respiratory phase of the CIRS dynamic thorax phantom with breathing periods from 2s to 8s and two breathing traces from lung cancer patients. The contrast to noise ratio (CNR) and edge response width (ERW) were used to compare image quality between RMG‐4DCBCT and conventional 4DCBCT. Results: Regardless of the breathing period, for RMG‐4DCBCT, the CNR is approximately 7 and 9 with RMG‐4DCBCT_40 and RMG‐4DCBCT_60 respectively. Conventional 4DCBCT has a CNR dropping from 20 down to 6 as the breathing period drops from 2s to 8s. With RMG‐4DCBCT, the ERW, in the direction of phantom motion, ranges from 2.1mm to 2.5mm as the breathing period drops from 2s to 8s which compares to a higher range of 2.0mm to 2.5mm with conventional 4DCBCT. Images with similar quality to conventional 4DCBCT can be acquired with RMG‐4DCBCT_40 which has a 70% reduction in imaging dose. Conclusion: The image contrast can be controlled with RMG‐4DCBCT regardless of the patients breathing rate. However, although the image sharpness is better with RMG‐4DCBCT, image sharpness has a small dependence on the breathing period; the accuracy of registration and segmentation will therefore vary with the patient's breathing period. This project was supported by a National Health and Medical Research Council (NHMRC) project grant 1034060 and Cancer Australia grant number 1084566.
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