Pancreas deformation in the presence of tumors using feature tracking from free‐breathing XD‐GRASP MRI

Abstract

Background: Quantifying the biomechanical properties of pancreatic tumors could potentially help with assessment of tumor aggressiveness, prognosis, and prediction of therapy response. Purpose: To quantify respiratory-induced deformation in the pancreas and pancreatic lesions using XD-GRASP (eXtra-Dimensional Golden-angle RAdial Sparse Parallel), MRI. Study Type: Retrospective study where patients undergoing clinically indicated abdominal MRI which included free-breathing radial T1-weighted (T1W) imaging were studied. Subjects: Thirty-two patients (12 male and 20 female) including nine with pancreatic lesions constituted our study cohort. Field Strength/Sequence: 3.0 T with T1WI contrast-enhanced gradient echo radial free-breathing acquisition. Assessment: Using the XD-GRASP imaging technique, the acquired free-breathing radial data were sorted and binned into 10 consecutive respiratory motion states that were jointly reconstructed. 3D deformation fields along the respiratory dimension were computed using an optical flow method and were analyzed in the pancreas. Statistical Tests: The Wilcoxon signed-rank test was used to assess the difference in average displacement across pancreatic regions, while the Wilcoxon rank-sum test was used for displacement differences between patients with and without tumors. The interclass correlation coefficient (ICC) was computed to assess consistency between observers for each image quality measure. Results: There was a significantly larger displacement in the pancreatic tail compared with the head (8.2 ± 3.7 mm > 5.8 ± 2.4 mm; P < 0.001) and body regions (8.2 ± 3.7 mm > 6.6 ± 2.9 mm; P < 0.001). Furthermore, there was reduced normalized average displacement in patients with pancreatic lesions compared with subjects without lesions (0.33 ± 0.1 < 0.69 ± 0.26, P < 0.001 for the head; 0.30 ± 0.1 < 0.84 ± 0.31, P < 0.001 for the body; and 0.44 ± 0.31 < 1.08 ± 0.53, P < 0.001 for the tail, respectively). Data Conclusion: Free-breathing respiratory motion-sorted XD-GRASP MRI has the potential to noninvasively characterize the biomechanical properties of the pancreas by quantifying breathing-induced mechanical displacement.

GRASPNET: Fast spatiotemporal deep learning reconstruction of golden‐angle radial data for free‐breathing dynamic contrast‐enhanced magnetic resonance imaging

“…The combination of golden‐angle radial k‐space acquisition and temporal compressed sensing in the Golden angle RAdial Sparse Parallel (GRASP) technique 1 enabled performing dynamic contrast‐enhanced (DCE) MRI during free‐breathing with high spatial and temporal resolution and large volumetric coverage, by exploiting motion tolerance from radial MRI and k‐space undersampling from compressed sensing. Significant clinical applications include organs affected by respiratory motion, such as the liver, 2,3 kidneys, 4 and pancreas 5,6 . For example, GRASP can be employed to perform multiphase liver examinations with temporal resolutions of 15 s 2 or liver perfusion with higher temporal resolutions of up to 1.7 s 1 .…”

“…Significant clinical applications include organs affected by respiratory motion, such as the liver, 2,3 kidneys, 4 and pancreas. 5,6 For example, GRASP can be employed to perform multiphase liver examinations with temporal resolutions of 15 s 2 or liver perfusion with higher temporal resolutions of up to 1.7 s. 1 However, GRASP reconstruction is iterative, because it is based on compressed sensing principles, and thus it can result in long reconstruction times according to the dimensionality of the data (number of spatial and temporal points and number of coils). Even with the use of parallel computing based on graphical processing units (GPUs), the reconstruction time for GRASP is still in the range of several minutes, 7 which is suboptimal for clinical implementation.…”

Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence

“…It is based on reconstructing the extra respiratory motion dimension from the k-space data, by ordering each contrast-enhanced phase from end-inspiration to end-expiration into multiple respiratory motion states, where the grouped number of spokes in each motion state remains the same (Grimm 2015). However, to our knowledge there is limited previous work on XD-GRASP in abdominal imaging (Feng et al 2016, Chitiboi et al 2019 and no previous work evaluating the impact of XD-GRASP acquisition parameters on image quality and DCE-MRI parameters. The purpose of this study was therefore to evaluate the impact of the number of respiratory motion states using XD-GRASP reconstruction on image quality and quantitative DCE-MRI perfusion parameters.…”

Using Free‐Breathing MRI to Quantify Pancreatic Fat and Investigate Spatial Heterogeneity in Children

“…In children, image quality of FB‐MRI has been shown to be equivalent or superior to image quality of BH‐MRI for assessing liver fat content 15,16 . A different FB‐MRI method (eXtra‐Dimensional Golden‐angle RAdial Sparse Parallel [XD‐GRASP]) has also been shown to produce higher quality T 1 ‐weighted pancreatic images than BH‐MRI in adults, but it was not investigated for multiecho MRI and fat quantification 17 . FB‐MRI has yet to be applied to pancreatic fat quantification in the pediatric population.…”