Session Item

Quantitative imaging biomarkers (QIBs) derived from MRI techniques, like diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE-) MRI, have the potential to personalize radiotherapy treatment. QIBs can be utilized in various ways, including the prediction of outcome to different radiation dose based on pretreatment images, adaptation of treatment plan during treatment, and response assessment after completion of radiotherapy. With the recent introduction of integrated MRI and radiotherapy treatment systems, the interest in QIBs for radiotherapy is growing.

Movement during the acquisition affects the quantification of the quantitative MRI parameters. To obtain quantitative parameters typically multiple images are acquired through which a model is fitted. Movement between these images could affect the accuracy and repeatability of the quantitative MRI parameters. For example, breathing motion causes blurring in the b-value images of DWI when multiple individual b-value images are averaged. Cardiac motion causes signal drop-out mostly seen in the upper left lobe of the liver. In DCE-MRI motion results in more noise in or even corruption of the signal intensity time curves, which makes quantification with tracer kinetic modelling more difficult. Therefore, quantitative MRI in moving targets, such as liver and lung, is even more challenging than in the pelvic region. Here we focus on the effect of breathing motion on the quantification of DWI.

There are two main strategies to deal with breathing motion: during acquisition or afterwards with offline analysis strategies. In the acquisition of DWI data breath-hold and respiratory-triggered approaches are typically used to deal with the breathing motion. Offline motion correction has been used for DWI in the liver, where individual b-value images were registered before averaging and calculating the ADC. The advantage of this approach is that the data can be acquired in free breathing, which saves time and is easier for the patient and technicians compared to breath-hold or respiratory-triggered approaches.

Motion compensation results in an improvement in image quality and signal-to-noise of the DWI images. Test-retest measurements are used to investigate the effect of motion compensation strategies on the repeatability of the ADC, which is important to distinguish treatment-related changes for response assessment.