Advanced image processing in the clinical arena: issues to consider

Towards a repository for standardized medical image and signal case data annotated with ground truth

Validation of medical signal and image processing systems requires quality-assured, representative and generally acknowledged databases accompanied by appropriate reference (ground truth) and clinical metadata, which are composed laboriously for each project and are not shared with the scientific community. In our vision, such data will be stored centrally in an open repository. We propose an architecture for a standardized case data and ground truth information repository supporting the evaluation and analysis of computer-aided diagnosis based on (a) the Reference Model for an Open Archival Information System (OAIS) provided by the NASA Consultative Committee for Space Data Systems (ISO 14721:2003), (b) the Dublin Core Metadata Initiative (DCMI) Element Set (ISO 15836:2009), (c) the Open Archive Initiative (OAI) Protocol for Metadata Harvesting, and (d) the Image Retrieval in Medical Applications (IRMA) framework. In our implementation, a portal bunches all of the functionalities that are needed for data submission and retrieval. The complete life cycle of the data (define, create, store, sustain, share, use, and improve) is managed. Sophisticated search tools make it easier to use the datasets, which may be merged from different providers. An integrated history record guarantees reproducibility. A standardized creation report is generated with a permanent digital object identifier. This creation report must be referenced by all of the data users. Peer-reviewed e-publishing of these reports will create a reputation for the data contributors and will form de-facto standards regarding image and signal datasets. Good practice guidelines for validation methodology complement the concept of the case repository. This procedure will increase the comparability of evaluation studies for medical signal and image processing methods and applications.

A parallel method to improve medical image transmission

The staggering number of images acquired by modern modalities requires new approaches for medical data transmission. There have been several attempts to improve data transmission time between medical imaging systems. These attempts were mostly based on compression. Although the compression methods can help in many cases, they are sometimes ineffectual in high-speed networks. This paper introduces parallelism to provide an effective method of medical data transmission over both local area network (LAN) and wide area network (WAN). It is based on the Digital Imaging and Communications in Medicine (DICOM) protocol and uses parallel TCP connections in storage services within the protocol. Using the proposed interface in our method, current medical imaging applications can take advantage of parallelism without any modification. Experimental results show a speedup of about 1.3 to 1.5 for CT images and relatively high speedup of about 2.2 to 3.5 times for magnetic resonance (MR) images over LAN. The transmission time is improved drastically over WAN. The speedup is about 16.1 for CT images and about 5.6 to 11.5 for MR images.

Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day

The technology revolution in image acquisition, instrumentation, and methods has resulted in vast data sets that far outstrip the human observers' ability to view, digest, and interpret modern medical images by using traditional methods. This may require a paradigm shift in the radiologic interpretation process. As human observers, radiologists must search for, detect, and interpret targets. Potential interventions should be based on an understanding of human perceptual and attentional abilities and limitations. New technologies and tools already in use in other fields can be adapted to the health care environment to improve medical image analysis, visualization, and navigation through large data sets. This historical psychophysical and technical review touches on a broad range of disciplines but focuses mainly on the analysis, visualization, and navigation of image data performed during the interpretive process. Advanced postprocessing, including three-dimensional image display, multimodality image fusion, quantitative measures, and incorporation of innovative human-machine interfaces, will likely be the future. Successful new paradigms will integrate image and nonimage data, incorporate workflow considerations, and be informed by evidence-based practices. This overview is meant to heighten the awareness of the complexities and limitations of how radiologists interact with images, particularly the large image sets generated today. Also addressed is how human-machine interface and informatics technologies could combine to transform the interpretation process in the future to achieve safer and better quality care for patients and a more efficient and effective work environment for radiologists.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11091276/-/DC1.