Retrieval, visualization, and mining of large radiation dosage data

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Web End = Inf Retrieval J (2016) 19:3858 DOI 10.1007/s10791-015-9265-z

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Web End = MEDICAL INFORMATION RETRIEVAL

Retrieval, visualization, and mining of large radiation dosage data

William Kovacs1 Samuel Weisenthal1 Les Folio1

Qiaoyi Li1 Ronald M. Summers1 Jianhua Yao1

Received: 15 December 2014 / Accepted: 14 August 2015 / Published online: 15 September 2015 Springer Science+Business Media New York (outside the USA) 2015

Abstract Radiation dose monitoring has become an essential service that hospitals must perform. Depending on the system in place, this can result in the collection of large quantities of data, ripe for analysis. These data should include a wide variety of variables for each study because assessment of the propriety of the patients dose is dependent on many factors, including patient age and size, as well as the body section that is being scanned. Moreover, the scanners themselves have many properties that affect patient dose, such as model, pitch and kVp. In this paper, we propose an engine that seamlessly integrated with a clinical PACS to retrieve radiation dosage data. We devised several schemes to analyze these data through visualization and mining techniques that examine it at different scopes. We demonstrate the utility of such visual methods at examining large, noisy, and multi-dimensional data, which is embodied in the collected radiation data.

Keywords Radiation dose Clinical PACS Data retrieval Data visualization

1 Introduction

Medical imaging has drastically improved the clinicians ability to diagnose a disease by providing an easy method to examine a patients body. While there are many different techniques to obtain these images, one of the most effective, computed tomography (CT) scanning, has seen a rapid increase in usage over the past couple of decades (Brenner and Hall 2007). However, due to its use of ionizing radiation, the patient is at a higher risk of encountering harmful side effects, especially induced cancer risks (Brenner et al. 2003; Huang et al. 2014; Pearce et al. 2012). Due to its increased usage, CT imaging was the

& William Kovacs [email protected]

1 Clinical Image Processing Services, Radiology and Imaging Sciences, Clinical Center, NationalInstitutes of Health, Bethesda, MD, USA

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largest source of medical radiation exposure in 2009, nearly half, and actually constituted 24 % of the...