Visual interpretation with three-dimensional annotations (VITA): Three-dimensional image interpretation tool for radiological reporting

10.1007/s10278-013-9624-5Journal of Digital Imaging27149-57JDIM

Imaging Informatics: Challenges in Multi-site Imaging Trials

Multi-site imaging research has several specialized needs that are substantially different from what is commonly available in clinical imaging systems. An attempt to address these concerns is being led by several institutes including the National Institutes of Health and the National Cancer Institute. With the exception of results reporting (which has an infrastructure for standard reports, albeit with several competing lexicons), medical imaging has been largely standardized by the efforts of DICOM, HL7, and IHE. What are not well developed in this area are the tools required for multi-site imaging collaboration and data mining. The goal of this paper is to identify existing clinical interoperability methods that can be used to harmonize the research and clinical worlds, and identify gaps where they exist. To do so, we will detail the approaches of a specific multi-site trial, point out the current deficiencies and workarounds developed in that trial, and finally point to work that seeks to address multi-site imaging challenges

Mapping LIDC, RadLex™, and Lung Nodule Image Features

Ideally, an image should be reported and interpreted in the same way (e.g., the same perceived likelihood of malignancy) or similarly by any two radiologists; however, as much research has demonstrated, this is not often the case. Various efforts have made an attempt at tackling the problem of reducing the variability in radiologists’ interpretations of images. The Lung Image Database Consortium (LIDC) has provided a database of lung nodule images and associated radiologist ratings in an effort to provide images to aid in the analysis of computer-aided tools. Likewise, the Radiological Society of North America has developed a radiological lexicon called RadLex. As such, the goal of this paper is to investigate the feasibility of associating LIDC characteristics and terminology with RadLex terminology. If matches between LIDC characteristics and RadLex terms are found, probabilistic models based on image features may be used as decision-based rules to predict if an image or lung nodule could be characterized or classified as an associated RadLex term. The results of this study were matches for 25 (74%) out of 34 LIDC terms in RadLex. This suggests that LIDC characteristics and associated rating terminology may be better conceptualized or reduced to produce even more matches with RadLex. Ultimately, the goal is to identify and establish a more standardized rating system and terminology to reduce the subjective variability between radiologist annotations. A standardized rating system can then be utilized by future researchers to develop automatic annotation models and tools for computer-aided decision systems

Toward a User-Driven Approach to Radiology Software Solutions: Putting the Wag Back in the Dog

The relationship between healthcare providers and the software industry is evolving. In many cases, industry's traditional, market-driven model is failing to meet the increasingly sophisticated and appropriately individualized needs of providers. Advances in both technology infrastructure and development methodologies have set the stage for the transition from a vendor-driven to a more user-driven process of solution engineering. To make this transition, providers must take an active role in the development process and vendors must provide flexible frameworks on which to build. Only then can the provider/vendor relationship mature from a purchaser/supplier to a codesigner/partner model, where true insight and innovation can occur