Highly accelerated dynamic acquisition of 3D grid-tagged hyperpolarized-gas lung images using compressed sensing.

PURPOSE: To develop and test compressed sensing-based multiframe 3D MRI of grid-tagged hyperpolarized gas in the lung. THEORY AND METHODS: Applying grid-tagging RF pulses to inhaled hyperpolarized gas results in images in which signal intensity is predictably and sparsely distributed. In the present work, this phenomenon was used to produce a sampling pattern in which k-space is undersampled by a factor of approximately seven, yet regions of high k-space energy remain densely sampled. Three healthy subjects received multiframe 3D 3 He tagging MRI using this undersampling method. Images were collected during a single exhalation at eight timepoints spanning the breathing cycle from end-of-inhalation to end-of-exhalation. Grid-tagged images were used to generate 3D displacement maps of the lung during exhalation, and time-resolved maps of principal strains and fractional volume change were generated from these displacement maps using finite-element analysis. RESULTS: Tags remained clearly resolvable for 4-6 timepoints (5-8 s) in each subject. Displacement maps revealed noteworthy temporal and spatial nonlinearities in lung motion during exhalation. Compressive normal strains occurred along all three principal directions but were primarily oriented in the head-foot direction. Fractional volume changes displayed clear bilateral symmetry, but with the lower lobes displaying slightly higher change than the upper lobes in 2 of the 3 subjects. CONCLUSION: We developed a compressed sensing-based method for multiframe 3D MRI of grid-tagged hyperpolarized gas in the lung during exhalation. This method successfully overcomes previous challenges for 3D dynamic grid-tagging, allowing time-resolved biomechanical readouts of lung function to be generated.

Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging.

BACKGROUND: Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps εp1 and εp2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain). METHODS: For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms. RESULTS: CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with εp1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and εp2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for εp1 and εp2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively. CONCLUSION: CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.

A comprehensive comparison between shortest-path HARP refinement, SinMod, and DENSEanalysis processing tools applied to CSPAMM and DENSE images.

We addressed comprehensively the performance of Shortest-Path HARP Refinement (SP-HR), SinMod, and DENSEanalysis using 2D slices of synthetic CSPAMM and DENSE images with realistic contrasts obtained from 3D phantoms. The three motion estimation techniques were interrogated under ideal and no-ideal conditions (with MR induced artifacts, noise, and through-plane motion), considering several resolutions and noise levels. Under noisy conditions, and for isotropic pixel sizes of 1.5 mm and 3.0 mm in CSPAMM and DENSE images respectively, the nRMSE obtained for the circumferential and radial strain components were 10.7 ±â€¯10.8% and 25.5 ±â€¯14.8% using SP-HR, 11.9 ±â€¯2.5% and 29.3 ±â€¯6.5% using SinMod, and 6.4 ±â€¯2.0% and 18.2 ±â€¯4.6% using DENSEanalysis. Overall, the results showed that SP-HR tends to fail for large tissue motions, whereas SinMod and DENSEanalysis gave accurate displacement and strain field estimations, being the last which performed the best.

Acute effect of glucose on cerebral blood flow, blood oxygenation, and oxidative metabolism.

While it is known that specific nuclei of the brain, for example hypothalamus, contain glucose-sensing neurons thus their activity is affected by blood glucose level, the effect of glucose modulation on whole-brain metabolism is not completely understood. Several recent reports have elucidated the long-term impact of caloric restriction on the brain, showing that animals under caloric restriction had enhanced rate of tricarboxylic acid cycle (TCA) cycle flux accompanied by extended life span. However, acute effect of postprandial blood glucose increase has not been addressed in detail, partly due to a scarcity and complexity of measurement techniques. In this study, using a recently developed noninvasive MR technique, we measured dynamic changes in global cerebral metabolic rate of O2 (CMRO2 ) following a 50 g glucose ingestion (N = 10). A time dependent decrease in CMRO2 was observed, which was accompanied by a reduction in oxygen extraction fraction (OEF) with unaltered cerebral blood flow (CBF). At 40 min post-ingestion, the amount of CMRO2 reduction was 7.8 ± 1.6%. A control study without glucose ingestion was performed (N = 10), which revealed no changes in CMRO2 , CBF, or OEF, suggesting that the observations in the glucose study was not due to subject drowsiness or fatigue after staying inside the scanner. These findings suggest that ingestion of glucose may alter the rate of cerebral metabolism of oxygen in an acute setting.