Trends in PACS architecture

Bellon, Erwin; Féron, Michel; Deprez, Tom; Reynders, Reinoud A.; Bosch, Bart Van den

doi:10.1016/j.ejrad.2010.05.025

Cited by 17 publications

(8 citation statements)

References 6 publications

(5 reference statements)

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“…These systems are more efficient than film and paper, and are more secure because they incorporate safeguards to limit access and sophisticate auditing systems to track scanned data. Although radiologists prefer efficient access to the comprehensive imaging records of their patients, there are no reliable methods to discover or obtain access to similar records that might be stored elsewhere [31,32,35].…”

Section: Challenges In Medical Image Exchangingmentioning

confidence: 99%

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Accessing medical image file with co-allocation HDFS in cloud

Yang

Shih

Chen

et al. 2015

Future Generation Computer Systems

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Section: Challenges In Medical Image Exchangingmentioning

confidence: 99%

“…Current virtualization fault tolerance response plan, focusing on recovery failure, usually relies on a checkpoint/restart mechanism. However, in today's systems, node failures can often be predicted by detecting the deterioration of health conditions [22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Accessing medical image file with co-allocation HDFS in cloud

Yang

Shih

Chen

et al. 2015

Future Generation Computer Systems

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“…This study, while aiming for generalizability, was motivated by the question of decision making in PACS design. A PACS is a very complex system which covers all the aspects of image workflow in one or more medical institutions [4], including mobile medicine and telemedicine applications [29]. PACS is the dominant paradigm in the medical field and remains so, despite occasional criticism [26].…”

Section: Motivationmentioning

confidence: 99%

An empirical study of data visualization techniques in PACS design

et al. 2019

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The paper presents an empirical study of multidimensional visualization techniques. The study is motivated by the problem of decision making in PACS (Picture Archiving and Communications System) design. A comprehensive survey of visualizations used in literature is performed and these survey results are then used to produce the final set of considered visualizations: tables (as control), scatterplots, parallel coordinates, and star plots. An electronic testing tool is developed to present visualizations to three sets of experimental subjects in order to determine which visualization technique allows users to make the correct decision in a sample decision making problem based on real-world data. Statistical analysis of the results demonstrates that visualizations show better results in decision support than tables. Further, when number of dimensions is large, 2D parallel coordinates show the best results in accuracy. The contribution of the presented research operates on two levels of abstraction. On the object level, it provides useful data regarding the relative merits of visualization techniques for the considered narrow use-case, which can then be generalized to other similar problem sets. On the meta level above, it contributes an enhanced methodology to the area of empirical visualization evaluation methods.

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“…The Picture Archiving and Communications System (PACS) is currently the standard platform to manage medical images [ 1 ] but lacks analytical and quantification capabilities [ 2 , 3 ]. Staying within the PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that enables analytical procedures to be applied to the data while not perturbing the system’s daily operation.…”

Section: Introductionmentioning

confidence: 99%

Automatically measuring brain ventricular volume within PACS using artificial intelligence

2018

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The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system’s daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

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Trends in PACS architecture

Cited by 17 publications

References 6 publications

Accessing medical image file with co-allocation HDFS in cloud

Accessing medical image file with co-allocation HDFS in cloud

An empirical study of data visualization techniques in PACS design

Automatically measuring brain ventricular volume within PACS using artificial intelligence

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