A system for processing a radiograph such as an x-ray includes a scanner, a computer with monitor for displaying a digital copy of the radiograph and pet-forming a number of enhancements. The computer stores the digitized image in memory in such a way that it cannot be modified, stores temporarily a second copy of the image in random access memory for display, and stores the enhancements separately in a data file so that, each time the user wishes to see an enhanced image, the enhancements are applied to the displayed digital image as an overlay, rather than being stored as a copy of the enhanced image. Less space is required for storage when only the unenhanced image and the enhancements are stored than if the unenhanced and the enhanced images are stored. Furthermore, not only is the unenhanced image available for a variety of purposes (stored in such a way that it cannot be modified), but the enhancements that are made to produce the enhanced, displayed image are reproduced the same way each time.

Radiographic image enhancement comparison and storage requirement reduction system

The objective of radiologic image compression is to reduce the data volume of and to achieve a low bit rate in the digital representation of radiologic images without perceived loss of image quality. However, the demand for transmission bandwidth and storage space in the digital radiology environment, especially picture archiving and communication systems (PACS) and teleradiology, and the proliferating use of various imaging modalities, such as magnetic resonance imaging, computed tomography, ultrasonography, nuclear medicine, computed radiography, and digital subtraction angiography, continue to outstrip the capabilities of existing technologies. The availability of lossy coding techniques for clinical diagnoses further implicates many complex legal and regulatory issues. This paper reviews the recent progress of lossless and lossy radiologic image compression and presents the legal challenges of using lossy compression of medical records. To do so, we first describe the fundamental concepts of radiologic imaging and digitization. Then, we examine current compression technology in the field of medical imaging and discuss important regulatory policies and legal questions facing the use of compression in this field. We conclude with a summary of future challenges and research directions. >

Radiologic image compression-a review

Compressing a digital image can facilitate its transmission, storage, and processing. As radiology departments become increasingly digital, the quantities fo their imaging data are forcing consideration of compression in picture archiving and communication systems. Significant compression is achievable anly by lossy algorithms, which do not permit the exact recovery of the original images

Evaluating quality of compressed medical images : SNR, subjective rating, and diagnostic accuracy : Data compression

Compressing a digital image can facilitate its transmission, storage, and processing. As radiology departments become increasingly digital, the quantities of their imaging data are forcing consideration of compression in picture archiving and communication systems (PACS) and evolving teleradiology systems. Significant compression is achievable only by lossy algorithms, which do not permit the exact recovery of the original image. This loss of information renders compression and other image processing algorithms controversial because of the potential loss of quality and consequent problems regarding liability, but the technology must be considered because the alternative is delay, damage, and loss in the communication and recall of the images. How does one decide if an image is good enough for a specific application, such as diagnosis, recall, archival, or educational use? The authors describe three approaches to the measurement of medical image quality: signal-to-noise ratio (SNR), subjective rating, and diagnostic accuracy. They compare and contrast these measures in a particular application, consider in some depth recently developed methods for determining diagnostic accuracy of lossy compressed medical images and examine how good the easily obtainable distortion measures like SNR are at predicting the more expensive subjective and diagnostic ratings. The examples are of medical images compressed using predictive pruned tree-structured vector quantization, but the methods can be used for any digital image processing that produces images different from the original for evaluation. >

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Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy

MRI is one of the most dynamic and safe imaging techniques available in the clinic today. However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI. The limited body of research evaluating the effects of CS on MR images has been mostly positive with regards to its potential as a clinical tool. Studies have successfully accelerated MRI with this technology, with varying degrees of success. However, more must be performed before its diagnostic efficacy and benefits are clear. Studies involving a greater number radiologists and images must be completed, rating CS based on its diagnostic efficacy. Also, standardized methods for determining optimal imaging parameters must be developed.

/pdf/compressed-sensing-mri-a-review-of-the-clinical-literature-1kzini02kd.pdf

Compressed sensing MRI: a review of the clinical literature

Implementation of a Picture Archiving and Communication System (PACS) is a system integration task and requires the knowledge of multidisciplinary fields. This paper reviews current PACS development with emphasis on radiological images. The following topics are covered: methods of image acquisition, image compression, storage, display, communication, and image database structure. Methods of implementation of PACS in a clinical environment as well as current operational PACS in hospitals are reviewed. A survey of private industry participating in PACS research and development are also given.

Picture archiving and communication systems (PACS) for radiological images: state of the art.

Compression algorithms based on full-frame discrete cosine transforms have achieved compression ratios as high as 10:1 to 20:1 with almost imperceptible image degradation, when applied to projection radiographs digitized with 2,048 X 2,048 X 8-bit matrices. Compared with such radiographs, images obtained with computed tomography (CT) and magnetic resonance (MR) are smaller in size, have lower signal-to-noise ratios, and, in the case of CT, have a larger dynamic range. These differences result in qualitatively different spectral properties. The authors studied the efficiency of the full-frame technique when applied to CT and MR images. They achieved excellent results, with compression ratios in the neighborhood of 5:1. The study was performed with the use of a hardware implementation of the authors' algorithm, which can compress a 512 X 512 X 12-bit image in less than 1.5 seconds.

Full-frame transform compression of CT and MR images.

Radiological image compression using full-frame cosine transform with adaptive bit-allocation.

Radiologic image communication methods.

We performed volume compression on CT and MR data sets, each consisting of 256 X 256 X 64 or 32 images, using three-dimensional (3D) DCT followed by quantization, adaptive bit-allocation, and Huffman encoding. Cuberille based surface rendering and oblique angle slicing was performed on the reconstructed compression data using a multi-stream vector processor. For CT images 3D-DCT was found to be successful in exploiting the additional degree of voxel correlations between image frames, resulting in compression efficiency greater than 2D-DCT of individual images. During rendering operations, a substantial amount of thresholding, resampling, and filtering operations are performed on the data. At compression ratios in the range 6 - 15:1, 3D compression was not found to have any adverse visual impact on rendered output. Of these two methods, oblique angle slicing, which involves the fewest operations was found to be the most demanding of small compression errors. We conclude that 3D-DCT compression is a viable technique for efficiently reducing the size of data volumes which must be analyzed with various rendering methods.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Kelby K. Chan

Papers

Picture archiving and communication systems (PACS) for radiological images: state of the art.

Full-frame transform compression of CT and MR images.

Radiological image compression using full-frame cosine transform with adaptive bit-allocation.

Radiologic image communication methods.

Visualization and volumetric compression