Eliot L. Siegel

Experience and Design Recommendations for Picture Archiving and Communication Systems in the Surgical Setting

An analysis of the efficacy of a picture archiving and communication system (PACS) in the surgical domain was undertaken at the Baltimore Veterans Affairs Medical Center. Interviews with surgeons and staff were conducted and supplemented by direct radiologist observation in the operating room (OR) and surgical outpatient clinic to determine patterns of routine clinical PACS use, levels of satisfaction both within and outside of the OR, and perceptions of the relative efficacy of the system in comparison to film. These data as well as suggestions from the surgical staff members were used to make recommendations for specific modifications in PACS design and operation to improve the current system and to help prescribe design improvements for future PAC systems. A high level of satisfaction with the system was found and the use of PACS was favored over film by a majority of surgeons and their staff. Findings of this study suggest that the design of a hospital-wide PAC system must have the flexibility to accommodate the specific requirements of a wide variety of end-users in their unique hospital environments.

Transforming the radiological interpretation process: the SCAR TRIP initiative

The Society for Computer Applications in Radiology (SCAR) Transforming the Radiological Interpretation Process (TRIP) Initiative aims to spearhead research, education, and discovery of innovative solutions to address the problem of information and image data overload. The initiative will foster inter-disciplinary research on technological, environmental and human factors to better manage and exploit the massive amounts of data. TRIP will focus on the following basic objectives: improving the efficiency of interpretation of large data sets, improving the timeliness and effectiveness of communication, and decreasing medical errors. The ultimate goal of the initiative is to improve the quality and safety of patient care. Interdisciplinary research into several broad areas will be necessary to make progress in managing the ever-increasing volume of data. The six concepts involved include: human perception, image processing and computer-aided detection (CAD), visualization, navigation and usability, databases and integration, and evaluation and validation of methods and performance. The result of this transformation will affect several key processes in radiology, including image interpretation; communication of imaging results; workflow and efficiency within the health care enterprise; diagnostic accuracy and a reduction in medical errors; and, ultimately, the overall quality of care.

Sinogram restoration for ultra-low-dose x-ray multi-slice helical CT by nonparametric regression

During the last decade, x-ray computed tomography (CT) has been applied to screen large asymptomatic smoking and nonsmoking populations for early lung cancer detection. Because a larger population will be involved in such screening exams, more and more attention has been paid to studying low-dose, even ultra-low-dose x-ray CT. However, reducing CT radiation exposure will increase noise level in the sinogram, thereby degrading the quality of reconstructed CT images as well as causing more streak artifacts near the apices of the lung. Thus, how to reduce the noise levels and streak artifacts in the low-dose CT images is becoming a meaningful topic. Since multi-slice helical CT has replaced conventional stop-and-shoot CT in many clinical applications, this research mainly focused on the noise reduction issue in multi-slice helical CT. The experiment data were provided by Siemens SOMATOM Sensation 16-Slice helical CT. It included both conventional CT data acquired under 120 kvp voltage and 119 mA current and ultra-low-dose CT data acquired under 120 kvp and 10 mA protocols. All other settings are the same as that of conventional CT. In this paper, a nonparametric smoothing method with thin plate smoothing splines and the roughness penalty was proposed to restore the ultra-low-dose CT raw data. Each projection frame was firstly divided into blocks, and then the 2D data in each block was fitted to a thin-plate smoothing splines surface via minimizing a roughness-penalized least squares objective function. By doing so, the noise in each ultra-low-dose CT projection was reduced by leveraging the information contained not only within each individual projection profile, but also among nearby profiles. Finally the restored ultra-low-dose projection data were fed into standard filtered back projection (FBP) algorithm to reconstruct CT images. The rebuilt results as well as the comparison between proposed approach and traditional method were given in the results and discussions section, and showed effectiveness of proposed thin-plate based nonparametric regression method.

Adoption of PACS by the Department of Veterans Affairs: the past, the present, and future plans

The diffusion of PACS technology within the Department of Veterans Affairs has followed the S curve transition originally described by Ryan and Gross in 1943. They described a paradigm that describes the diffusion of a new technology into the community. However the rate of adoption of filmless radiology by the VA has been much higher than that of the general healthcare system. This is likely due to the fact that the VA and Department of Defense medical systems are somewhat isolated and independent from other health care systems and are subject to a different rate of diffusion of technology. The early introduction and success of PACS in the VA undoubtedly accelerated its acceptance throughout the system. An additional impetus to the growth of PACS in the VA has been the development of an image management system that has been incorporated into the electronic medical record. The universal use of the VISTA HIS and RIS system throughout the VA and the fact that it was developed in-house as well as its extensive support for DICOM functionality have also played a major role in facilitating the acceptance of Picture Archival and Communication Systems throughout the VA.

Efficient and effective nearest neighbor search in a medical image database of tumor shapes

We examine the problem of finding similar tumor shapes. The main contribution of this work is the proposal of a natural similarity function for shape matching called the ‘morphological distance ’. This function has two desirable properties: (a) it matches human perception of similarity, as we illustrate with precision/recall experiments; (b) it can be lower-bounded by a set of features, leading to fast indexing for range queries and nearest neighbor queries.

Automatic classification of computed tomography slices into anatomic regions

We propose a computationally efficient and effective analysis technique to classify X-Ray Computed Tomography (CT) images into four anatomic regions: neck, chest, abdomen, and pelvis. The proposed technique divides a single scan (performed with a single bolus of contrast) into multiple anatomic regions that can be stored in separate electronic folders for each region. Our CT analysis technique extracts relevant features from the image slices and classifies the images into the four anatomic regions using a multilayer perceptron network. The technique is tested on a number of CT images and shown to result in an acceptable level of classification performance.

Indexing Large Collections of Tumor-Like Shapes

We investigated the problem of retrieving similar shapes from a large medical database of tumor shapes (‘find tumors that are similar to a given pattern’). We used a natural similarity function for shape matching based on state-of-the-art concepts from Mathematical Morphology, and showed how the function can be lower bounded by a set of features extracted from the shapes, thus leading to “correct” output (i.e., no false dismissals), a key requirement for medical applications. These features can be organized in a spatial access method, leading to fast indexing for range queries (‘Find objects within distance e of the given object.’) and nearest neighbor queries (‘Find the first k closest objects to the query object.’). In addition to the lower-bounding, our second contribution is the design of a fast algorithm for nearest neighbor search, which achieves significant speedup while provably guaranteeing correctness. Our experiments demonstrate up to 27 times better performance for the proposed method compared to sequential scanning. We also verified that the similarity function matches human perception of shape similarity, with experiments on human subjects obtaining 80% precision for up to 100% recall.