Procedures for Evaluation of Slice Thickness in Medical Imaging Systems

Abstract

The main goal of a medical imaging system is to produce images to provide more accurate and timely diagnoses (Torfeh et al., 2007). In particular, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US) are resourceful tools in medical practice, and in many cases a life saving resource when rapid decisions are needed in the emergency room (Rehani et al., 2000). The above diagnostic techniques are based on the evaluation of high resolution images from technologically sophisticated equipment. Individual images are obtained by using several different electronic components and considerable amounts of data processing, which affect the quality of images produced and, consequently, make the diagnostic process more complicated (Torfeh et al., 2007). In this context, to guarantee a consistent image quality over the lifetime of the diagnostic radiology equipment and to ensure safe and accurate operation of the process as a whole, it is necessary to establish and actively maintain regular and adequate Quality Assurance (QA) procedures. This is significant for computer-aided imaging systems, such as CT, MRI and US. The QA procedure should include periodic tests to ensure accurate target and critical structure localization (Mutic et al., 2003). Such tests are referred to as Quality Controls (QCs). They hold a key role within the QA procedure because they enable complete evaluation of system status and image quality (Chen et al., 2004; Vermiglio et al., 2006). Importantly, QCs permit the identification of image quality degradation before it affects patient scans, and of the source of possible equipment malfunction, pointing to preventive or immediate maintenance requirements. Thus, image QC is crucial to ensure a safe and efficient diagnosis and treatment of diseases (Rampado et al., 2006; Torfeh et al., 2007). For this reason, periodic QCs have been recommended by manufacturers and medical physicists’ organizations to test the performance of medical imaging systems. Protocols for QCs and QA in medical imaging systems have been produced by several professional groups (AAPM – NEMA) (Goodsitt et al., 1998). This highlights the extensive role of QA programs, including QC testing, preventive maintenance, etc. (Rampado et al., 2006). In any clinical imaging study, it is important to have accurate confirmation of several physical characteristics of the medical imaging device. In particular, the slice thickness