iterative reconstruction CT; Abdominal CT; image quality; patient exposure; Mathematical model observer
Viry Anais, Aberle Christoph, Racine Damien, Knebel Jean-François, Schindera Sebastian T., Schmidt Sabine, Becce Fabio, Verdun Francis R. (2018), Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: A quantitative task-based phantom study, in Physica Medica
, 48, 111-118.
Combes Christele, Viry Anais, Verdun Francis R., Becce Fabio, Anderson Nigel G., Raja Aamir Y., Kirkbride Tracy E., Choi Chloe, Stamp Lisa K., Dalbeth Nicola (2018), Multi-energy spectral photon-counting CT in crystal-related arthropathies: initial experience and diagnostic performance in vitro, in Physics of Medical Imaging
, Houston, United StatesSPIE, USA.
Viry Anais, Racine Damien, Ba Alexandre, Becce Fabio, Bochud François O., Verdun Francis R. (2017), Characterization of a CT unit for the detection of low contrast structures, in SPIE Medical Imaging
, Orlando, Florida, United StatesSPIE, USA.
In Switzerland, as in most Western countries, the radiation exposure of the population is steadily increasing due to an increasing utilization of computed tomography (CT). As an example, from 2008 to 2013 the average dose per Swiss inhabitant increased from 0.8 to 1.0 mSv. Abdominal CT scans are responsible for almost a third of that exposure, with a wide variation in doses for the same clinical question. There are two main explanations for the wide dose variability for abdominal CT in the clinical practice. Firstly, large differences in regards to the technical equipment for dose reduction exists in Switzerland, particularly on the background that very effective techniques were introduced recently by the CT manufacturers for dose reduction (e.g. iterative reconstruction (IR) techniques). Secondly, it has to be assumed that numerous CT scanners are not applied in a dose-efficient approach. In other words, the protocols are insufficiently optimized. The optimization process should begin by making sure that a CT unit is as efficient as possible to convert the radiation received by the detectors into useful image information. Then, one should ensure that the level of image quality produced allows the clinical question to be answered (i.e. diagnostic accuracy) avoiding unnecessary patient exposure. In the past, standard image quality assessment used metrics based on information theory that requires linear data processes. The introduction of IR, which includes highly non-linear processes, introduces a new challenge in the field of image quality assessment. As a matter of fact, although highly noise-suppressed CT images obtained with IR may exhibit a higher contrast-to-noise ratio than conventional filtered-back projection (FBP) images, the detection of low-contrast structures/lesions might still not be possible, thereby reducing the diagnostic value of the examination. Thus, the use of new clinically-relevant metrics controlling low-contrast detectability is required.At the moment, the use of mathematical model observers (MO) to predict low-contrast detectability of simple structures has been proposed by several groups worldwide. This approach is quite enticing since it is a task-based method that satisfies the pre-requisite of an actual optimization procedure of clinical protocols. Manufacturers have started to use these methods to benchmark their CT units, although many questions about the correct use of a CT unit in the clinical world remain unanswered. The choice of the most adequate model observer, phantom geometry, and low-contrast structures to be used in relation to specific clinical CT protocols has to be made. In summary, one needs to define a sound methodology that can be trusted by radiologists to ensure a safe process of patient dose reduction.