Medical imaging dose optimisation from ground up: expert opinion of an international summit

Abstract

As in any medical intervention, there is either a known or an anticipated benefit to the patient from undergoing a medical imaging procedure. This benefit is generally significant, as demonstrated by the manner in which medical imaging has transformed clinical medicine. At the same time, when it comes to imaging that deploys ionising radiation, there is a potential associated risk from radiation. Radiation risk has been recognised as a key liability in the practice of medical imaging, creating a motivation for radiation dose optimisation. The level of radiation dose and risk in imaging varies but is generally low. Thus, from the epidemiological perspective, this makes the estimation of the precise level of associated risk highly uncertain. However, in spite of the low magnitude and high uncertainty of this risk, its possibility cannot easily be refuted. Therefore, given the moral obligation of healthcare providers, 'first, do no harm,' there is an ethical obligation to mitigate this risk. Precisely how to achieve this goal scientifically and practically within a coherent system has been an open question. To address this need, in 2016, the International Atomic Energy Agency (IAEA) organised a summit to clarify the role of Diagnostic Reference Levels to optimise imaging dose, summarised into an initial report (Järvinen et al 2017 Journal of Medical Imaging 4 031214). Through a consensus building exercise, the summit further concluded that the imaging optimisation goal goes beyond dose alone, and should include image quality as a means to include both the benefit and the safety of the exam. The present, second report details the deliberation of the summit on imaging optimisation.

Validation of algorithmic CT image quality metrics with preferences of radiologists

“…As the algorithmic assessments of several essential image quality metrics of clinical CT images were validated with preferences of radiologists, it will be interesting to investigate the correlation of the algorithmic assessments with clinical task performance across a variety of tasks. Furthermore, the algorithmic measurements of image quality metrics may be combined with dose metrics to support the optimization of dose and image acquisition parameters to minimize clinical risks . They have the potential to greatly facilitate data‐driven monitoring and optimization of CT image acquisition protocols.…”

“…The key information content a medical imaging system presents includes anatomic and physiologic information of a patient. Effective rendering of such information in clinical images is indispensable for assuring quality of clinical decision making and mitigating clinical risks . In particular, Computed tomography (CT) is well known for its capability to produce high quality clinical images of a patient quickly in three dimensions (3D) .…”

The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession

“…The required metrics involve combining several types of data together for more comprehensive analysis, as also described in a recent publication from an international summit [30] describing the optimisation process. Accordingly, the comprehensive optimisation process should include the combined risk factors from clinical aspects and radiation dose.…”

“…Accordingly, the comprehensive optimisation process should include the combined risk factors from clinical aspects and radiation dose. To provide effective and patient specific risk -or benefitassessment, the radiation risk models and clinical image quality definition could be determined further by utilising clinical data channels such as previous radiology findings, lab results, genetic data and other clinical information [30]. DL methods are prerequisites for this kind of data analysis due to inherent non-linearity of the problem and large amount of heterogeneous data which is not equitable by traditional methods [11,27].…”

Optimization of low-dose chest CT protocols for the evaluation of the ground glass nodules using different iterative reconstruction algorithms

“…КТ является наиболее перспективной и актуальной областью лучевой диагностики для обеспечения радиационной безопасности пациентов за счет применения принципов обоснования и оптимизации [1][2][3][4]. Принцип оптимизации заключается в поддержании доз облучения пациентов на таком низком уровне, который возможен при условии получения качественной полноценной диагностической информации с учетом экономических и социальных факторов [5][6][7][8].…”