Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors

Abstract

The PDACS method can potentially improve the real time tracking of moving tumors by significantly increasing MRI's data acquisition speeds. In 3T images, the PDACS method does provide a benefit over the other two methods in terms of both the overall image quality and the ability to accurately and automatically contour the tumor shape. MRI's data acquisition may be accelerated using the simpler viewsharing strategy at the lower, 0.5T magnetic field, as the marginal benefit of the PDACS method may not justify its additional reconstruction times.

Sliding window prior data assisted compressed sensing for MRI tracking of lung tumors

“…In a sense, this original PDACS implementation represents some combination of compressed sensing and the KEYHOLE method. While this approach is simple to implement and improves CS reconstruction for dynamic images, there are large regions of unsampled k‐space in the dynamic images that may become progressively more mismatched from the prior data as the scan series progresses. Slow changes in the MR signal, possibly due to magnetic field instability arising from hardware heating, or shifts in a patient's position during scans could lead to the prior data and the current data to be severely “mismatched” in longer duration scans, leading to image artifacts.…”

“…In the original PDACS paper, it was suggested that fully sampled prior data should be periodically re‐acquired during the dynamic scan for best results. In practice, this will be inconvenient to perform as tumor tracking with prediction algorithm is best performed with images with a consistent frame rate, and hence re‐acquiring slower, fully sampled data may require the “tracking” dynamic images to be periodically stopped, interrupting treatment.…”

Compressed sensing for longitudinal MRI: An adaptive‐weighted approach

“…While these conditions meet in many application of dynamic imaging, such as prior image constrained compressed sensing (PICCS) in CT (Chen et al, 2008;Lauzier et al, 2012) and dynamic MRI (Jung et al, 2009;Lustig et al, 2006;Gamper et al, 2008;Yip et al, 2014), in longitudinal MRI none of these requirements are guaranteed. While there are solutions for miss-registration and variable grey level intensities (see Section 4), the temporal similarity in longitudinal MRI is a-priori unknown.…”

“…Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment (Rees et al, 2009;Young et al, 2011;Weizman et al, 2012;Yip et al, 2014;Weizman et al, 2014). They constitute one of the most efficient tools to track pathology changes and to evaluate treatment efficacy in brain diseases.…”

“…Examples include computed tomography (Chen et al, 2008;Lauzier et al, 2012), spectroscopic imaging (Hu et al, 1988), imaging of contrast agent uptake (Van Vaals et al, 1993), dynamic MRI (Jones et al, 1993;Liang and Lauterbur, 1994;Korosec et al, 1996;Madore et al, 1999;Tsao et al, 2003;Mistretta et al, 2006) and real time tracking of tumors (Yip et al, 2014), locally-focused MRI (Cao et al, 1995) and feature-recognizing MRI (Cao and Levin, 1993).…”