Craig A. Morioka

A Review of Medical Imaging Informatics

Abstract: This review of medical imaging informatics is a survey of current developments in an exciting field. The focus is on informatics issues rather than traditional data processing and information systems, such as picture archiving and communications systems (PACS) and image processing and analysis systems. In this review, we address imaging informatics issues within the requirements of an informatics system defined by the American Medical Informatics Association. With these requirements as a framework, we review, in four sections: (1) Methods to present imaging and associated data without causing an overload, including image study summarization, content‐based medical image retrieval, and natural language processing of text data. (2) Data modeling techniques to represent clinical data with focus on an image data model, including general‐purpose time‐based multimedia data models, health‐care‐specific data models, knowledge models, and problem‐centric data models. (3) Methods to integrate medical data information from heterogeneous clinical data sources. Advances in centralized databases and mediated architectures are reviewed along with a discussion on our efforts at data integration based on peer‐to‐peer networking and shared file systems. (4) Visualization schemas to present imaging and clinical data: the large volume of medical data presents a daunting challenge for an efficient visualization paradigm. In this section we review current multimedia visualization methods including temporal modeling, problem‐specific data organization, including our problem‐centric, context and user‐specific visualization interface.

openSourcePACS: An Extensible Infrastructure for Medical Image Management

The development of comprehensive picture archive and communication systems (PACS) has mainly been limited to proprietary developments by vendors, though a number of freely available software projects have addressed specific image management tasks. The openSourcePACS project aims to provide an open source, common foundation upon which not only can a basic PACS be readily implemented, but to also support the evolution of new PACS functionality through the development of novel imaging applications and services. openSourcePACS consists of four main software modules: 1) image order entry, which enables the ordering and tracking of structured image requisitions; 2) an agent-based image server framework that coordinates distributed image services including routing, image processing, and querying beyond the present digital image and communications in medicine (DICOM) capabilities; 3) an image viewer, supporting standard display and image manipulation tools, DICOM presentation states, and structured reporting; and 4) reporting and result dissemination, supplying web-based widgets for creating integrated reports. All components are implemented using Java to encourage cross-platform deployment. To demonstrate the usage of openSourcePACS, a preliminary application supporting primary care/specialist communication was developed and is described herein. Ultimately, the goal of openSourcePACS is to promote the wide-scale development and usage of PACS and imaging applications within academic and research communities

DataServer: an infrastructure to support evidence-based radiology.

Following a requirements analysis for development of an information infrastructure supporting evidence-based radiology, the objective of this study was the development of a data gateway to support flexible access to the totality of a patients electronic medical records through a single, uniform representation, regardless of the underlying data sources (eg, hospital information systems [HIS], radiology information systems [RIS], picture archiving and communication systems [PACS]). XML-based (eXtensible Markup Language) technologies were employed to create an application framework permitting querying of different clinical databases. The contents of different data sources were represented by using XML. On the basis of these representations, users can specify queries. The system transforms the XML queries into a query format understood by the specific databases, processes the query, and transforms the results back into an XML format. XML results can then be transformed in accordance to different data-formatting standards. Access to several different data sources, including HIS, RIS, and PACS, has been accomplished with this framework. The extensible nature of the XML data gateway enables data sources to be readily added. The framework also provides a means by which data can be systematically de-identified to protect patient confidentiality, thus supporting research endeavors.

Visualization and volumetric compression

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.

Simulating coronary arteries in x-ray angiograms.

Clinical validation of quantitative coronary angiography (QCA) algorithms is difficult due to the lack of a simple alternative method for accurately measuring in vivo vessel dimensions. We address this problem by embedding simulated coronary artery segments with known geometry in clinical angiograms. Our vessel model accounts for the profile of the vessel, x-ray attenuation in the original background, and noise in the imaging system. We have compared diameter measurements of our computer simulated arteries with measurements of an x-ray Telescopic-Shaped Phantom (XTSP) with the same diameters. The results show that for both uniform and anthropomorphic backgrounds there is good agreement in the measured diameters of XTSP compared to the simulated arteries (Pearsons correlation coefficient 0.99). In addition, the difference in accuracy and precision of the true diameter measures compared to the XTSP and simulated artery diameters was small (mean absolute error across all diameters was < or = 0.11 mm +/- 0.09 mm).

Accuracy assessment of layer decomposition using simulated angiographic image sequences

Layer decomposition is a promising method for obtaining accurate densitometric profiles of diseased coronary artery segments. This method decomposes coronary angiographic image sequences into moving densitometric layers undergoing translation, rotation, and scaling. In order to evaluate the accuracy of this technique, the authors have developed a technique for embedding realistic simulated moving stenotic arteries in real clinical coronary angiograms. They evaluate the accuracy of layer decomposition in two ways. First, they compute tracking errors as the distance between the true and estimated motion of a reference point in the arterial lesion. The authors find that noise-weighted phase correlation and layered background subtraction are superior to cross correlation and fixed mask subtraction, respectively. Second, they compute the correlation coefficient between the true vessel profile and the raw and processed images in the region of the stenosis. They find that layer decomposition significantly improves the correlation coefficient.

Structured Reporting in Neuroradiology

Abstract: We have developed a system to structure free‐text neuroradiology reports using a natural language processing program and formatted the output into the digital image and communication in medicine (DICOM) standard for structured reporting (SR). DICOM SR formats the correspondence of pertinent diagnostic images to the radiologists dictated report of clinical findings. In addition, DICOM SR allows the information to be organized into a tree structure. Individual nodes of the tree can contain individual items or lists. Structuring the content of free‐text information allows the creation of hierarchies with defined relationships between the concepts contained within the report.

Automatic correction of biplane projection imaging geometry.

A novel method is presented for correcting errors in measurements of biplane projection imaging geometry without prior identification of corresponding points in the two images. For imaged objects that project onto both images, a constraint equation is obtained that relates weighted integrals along corresponding epipolar lines. The integrals are computed to first order in the angular beamwidth, which is assumed to be small. Starting from measured or estimated values, geometrical parameters are computed iteratively in order to maximize the correlation between epipolar line integrals in the two images. Improvement in the computation of corresponding epipolar lines is demonstrated on images of a wire phantom. The root mean square distance of the epipolar lines from the corresponding reference points is improved from 15 pixel widths to less than 4 pixel widths (1.3 mm). Convergence is demonstrated on phantom images for individual parameter variations up to 70% in relative magnification, a relative shift of the imaging planes by 50 pixels, or a relative rotation of at least 35 degrees around either of two axes. Applicability to clinical images is demonstrated by using a biplane angiogram of a pig to align corresponding points determined from images of a Perspex cube acquired with the same geometry.

A methodology to integrate clinical data for the efficient assessment of brain-tumor patients

Careful examination of the medical record of brain-tumor patients can be an overwhelming task for the neuroradiologist. The number of clinical documents alone may approach 100 for a patient that has a 3-year-old brain tumor. The neuroradiologists evaluation of a patients brain tumor involves examining the current imaging exam and checking for previous imaging exams that may occur pre- or post-treatment. The goal of this research is to develop an effective method to review all of the pertinent patient information from the medical record. We have designed and developed a medical system that incorporates Hospital Information Systems, Radiology Information Systems, and Picture Archiving and Communications Systems information. Our research improves clinical review of patients data by organizing image display, removing unnecessary documents, and mining for key clinical scenarios that are important in the assessment and care of brain-tumor patients.

Integration of HIS/RIS clinical document with PACS image studies for neuroradiology

Poring over the medical record of brain tumor patients for pertinent history can be an overwhelming task for the neuroradiologist. The evaluation of an imaging study in a brain tumor patient involves examining the prior imaging and clinical documents for recent intervention potentially affecting the appearance of the brain and then drawing a conclusion and rendering a report based on the contextual information obtained. In complex cases, the radiologist can spend much of his/her time trying to locate the appropriate documents. The purpose of this research is to develop effective methods to review all of the pertinent information in a patient medical record incorporating HIS (Hospital Information Systems), RIS (Radiology Information Systems) and PACS (Picture Archiving and Communications Systems) information. Our research involves three areas in improving the clinical workflow for neuroradiologists: filtering the document worklist for pertinent clinical data, identification of key clusters of clinical information, and an automatic hanging protocol that displays the MR images for optimal image comparison.