Search

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups. [ABSTRACT FROM AUTHOR]

Optical coherence tomography angiography (OCTA) is an imaging modality that can be used for analyzing retinal vasculature. Quantitative assessment of en face OCTA images requires accurate segmentation of the capillaries. Using deep learning approaches for this task faces two major challenges. First, acquiring sufficient manual delineations for training can take hundreds of hours. Second, OCTA images suffer from numerous contrast-related artifacts that are currently inherent to the modality and vary dramatically across scanners. We propose to solve both problems by learning a disentanglement of an anatomy component and a local contrast component from paired OCTA scans. With the contrast removed from the anatomy component, a deep learning model that takes the anatomy component as input can learn to segment vessels with a limited portion of the training images being manually labeled. Our method demonstrates state-of-the-art performance for OCTA vessel segmentation. [ABSTRACT FROM AUTHOR]

Spatial normalization is frequently used to map data to a standard coordinate system by removing intersubject morphological differences, thereby allowing for group analysis to be carried out. The work presented in this paper is motivated by the need for an automated cortical surface normalization technique that will automatically identify homologous cortical landmarks and map them to the same coordinates on a standard manifold. The geometry of a cortical surface is analyzed using two shape measures that distinguish the sulcal and gyral regions in a multiscale framework. A multichannel optical flow warping procedure aligns these shape measures between a reference brain and a subject brain, creating the desired normalization. The partial differential equation that carries out the warping is implemented in a Euclidean framework in order to facilitate a multiresolution strategy, thereby permitting large deformations between the two surfaces. The technique is demonstrated by aligning 33 normal cortical surfaces and showing both improved structural alignment in manually labeled sulci and improved functional alignment in positron emission tomography data mapped to the surfaces. A quantitative comparison between our proposed surface-based spatial normalization method and a leading volumetric spatial normalization method is included to show that the surface-based spatial normalization performs better in matching homologous cortical anatomies. Index Terms--Cerebral cortex, multiresolution, multiscale, optical flow, spatial normalization, surface correspondence, surface warping.

Cardiovascular stress test imaging assists in the diagnosis and monitoring of cardiovascular disease. The procedure can be carried out in a magnetic resonance (MR) scanner using pharmacological agents that mimic the effects of natural exercise. In order to provide real time indication of ischemia, thereby assisting in diagnosis and helping to assure patient safety, it is desirable to have real time monitoring of the myocardial regional function. This paper presents an algorithm for the real time myocardium region-of-interest reconstruction and myocardial strain computation using data acquired from a real time pulse sequence that has been previously reported. The chirp Fourier transform is used for efficient computation, enabling a real-time continuous strain map at a rate of 25 frames/s. Coupled with a real time data path from the scanner to a laptop computer, this algorithm enables real time continuous monitoring of cardiac strain and is targeted for use in the early detection and quantification of ischemia during MR stress tests. Index Terms--Cardiac motion, FastHARP (HARP-MRI), harmonic phase (HARP), magnetic resonance (MR) tagging, myocardial strain, real-time imaging.

A novel approach is described for computing thickness, correspondence and gridding of tissues between two simply connected boundaries. The results have shown that the proposed technique has taken advantage of both the speed of the Eulerian approach and the accuracy of the Lagrangian approach.

Magnetic resonance (MR) tagging is capable of accurate, noninvasive quantification of regional myocardial function. Routine clinical use, however, is hindered by cumbersome and time-consuming postprocessing procedures. We propose a fast, semiautomatic method for tracking three-dimensional (3-D) cardiac motion from a temporal sequence of short- and long-axis tagged MR images. The new method, called 3-D-HARmonic Phase (3D-HARP), extends the HARP approach, previously described for two-dimensional (2-D) tag analysis, to 3-D. A 3-D material mesh model is built to represent a collection of material points inside the left ventricle (LV) wall at a reference time. Harmonic phase, a material property that is time-invariant, is used to track the motion of the mesh through a cardiac cycle. Various motion-related functional properties of the myocardium, such as circumferential strain and left ventricular twist, are computed from the tracked mesh. The correlation analysis of 3D-HARP and FINDTAGS + Tag Strain(E) Analysis (TEA), which are well-established tag analysis techniques, shows that the regression coefficients of circumferential strain ([E.sub.CC]) and twist angle are [r.sup.2] = 0.8605 and [r.sup.2] = 0.8645, respectively. The total time required for tracking 3-D cardiac motion is approximately 10 min in a 9 timeframe tagged MRI dataset and has the potential to be much faster. Index Terms--Cardiac motion tracking, MR tagging, phase time-invariance, strain computation, 3-D-HARmonic Phase (3D-HARP).

Magnetic resonance tagging has proven useful in the visualization and quantification of cardiac motion. Traditionally, tags are designed to have crisp geometric profiles in order to enhance both visualization and detection of tags. Recent image acquisition and analysis methods, however, have been designed to exploit sinusoidal tag profiles. This paper presents a method based on harmonic phase (HARP) concepts to synthesize tag lines that have both crisp profiles and alternative orientations from the original sinusoidal patterns. Results are demonstrated on images acquired with SPAMM, CSPAMM, and fast-HARP pulse sequences. Index Terms--Harmonic phase (HARP), magnetic resonance (MR) tagging, motion tracking, regional function.

We outline an Eulerian framework for computing the thickness of tissues between two simply connected boundaries that does not require landmark points or parameterizations of either boundary. Thickness is defined as the length of correspondence trajectories, which run from one tissue boundary to the other, and which follow a smooth vector field constructed in the region between the boundaries. A pair of partial differential equations (PDEs) that are guided by this vector field are then solved over this region, and the sum of their solutions yields the thickness of the tissue region. Unlike other approaches, this approach does not require explicit construction of any correspondence trajectories. An efficient, stable, and computationally fast solution to these PDEs is found by careful selection of finite differences according to an up-winding condition. The behavior and performance of our method is demonstrated on two simulations and two magnetic resonance imaging data sets in two and three dimensions. These experiments reveal very good performance and show strong potential for application in tissue thickness visualization and quantification. Index Terms--Correspondence trajectory, numerical methods, partial differential equations (PDEs), thickness.

Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models--the ability to automatically handle topology changes--turns out to be a liability in applications where the object to be segmented has a known topology that must be preserved. In this paper, we present a new class of geometric deformable models designed using a novel topology-preserving level set method, which achieves topology preservation by applying the simple point concept from digital topology. These new models maintain the other advantages of standard geometric deformable models including subpixel accuracy and production of nonintersecting curves or surfaces. Moreover, since the topology-preserving constraint is enforced efficiently through local computations, the resulting algorithm incurs only nominal computational overhead over standard geometric deformable models. Several experiments on simulated and real data are provided to demonstrate the performance of this new deformable model algorithm. Index Terms--Geometric deformable model, topology preservation, topological constraint, level set method, digital topology, simple points, active contours.

Abstract Background Previous studies of mechanical strain anomalies in myocardial infarction (MI) have been largely limited to analysis of one-dimensional (1D) and two-dimensional (2D) strain parameters. Advances in cardiovascular magnetic resonance (CMR) methods now permit a complete three-dimensional (3D) interrogation of myocardial regional strain. The aim of this study was to investigate the incremental value of CMR-based 3D strain and to test the hypothesis that 3D strain is superior to 1D or 2D strain analysis in the assessment of viability using a porcine model of infarction. Methods Infarction was induced surgically in 20 farm pigs. Cine, late gadolinium enhancement, and CMR tagging images were acquired at 11 days before (baseline), and 11 days (early) and 1 month (late) after induction of infarct. Harmonic phase analysis was performed to measure circumferential, longitudinal, and radial strains in myocardial segments, which were defined based on the transmurality of delayed enhancement. Univariate, bivariate, and multivariate logistic regression models of strain parameters were created and analyzed to compare the overall diagnostic accuracy of 3D strain analysis with 1D and 2D analyses in identifying the infarct and its adjacent regions from healthy myocardium. Results 3D strain differed significantly in infarct, adjacent, and remote segments (p Conclusions Cumulative 3D strain information accurately identifies infarcts and their neighboring regions from healthy myocardium. The 3D interrogation of myocardial contractility provides incremental diagnostic accuracy in delineating the dysfunctional and nonviable myocardium in comparison with 1D or 2D quantification of strain. The infarct neighboring regions are the major beneficiaries of the 3D assessment of regional strain.

Abstract Background We sought to investigate the relationship between infarct and dyssynchrony post- myocardial infarct (MI), in a porcine model. Mechanical dyssynchrony post-MI is associated with left ventricular (LV) remodeling and increased mortality. Methods Cine, gadolinium-contrast, and tagged cardiovascular magnetic resonance (CMR) were performed pre-MI, 9 ± 2 days (early post-MI), and 33 ± 10 days (late post-MI) post-MI in 6 pigs to characterize cardiac morphology, location and extent of MI, and regional mechanics. LV mechanics were assessed by circumferential strain (eC). Electro-anatomic mapping (EAM) was performed within 24 hrs of CMR and prior to sacrifice. Results Mean infarct size was 21 ± 4% of LV volume with evidence of post-MI remodeling. Global eC significantly decreased post MI (-27 ± 1.6% vs. -18 ± 2.5% (early) and -17 ± 2.7% (late), p < 0.0001) with no significant change in peri-MI and MI segments between early and late time-points. Time to peak strain (TTP) was significantly longer in MI, compared to normal and peri-MI segments, both early (440 ± 40 ms vs. 329 ± 40 ms and 332 ± 36 ms, respectively; p = 0.0002) and late post-MI (442 ± 63 ms vs. 321 ± 40 ms and 355 ± 61 ms, respectively; p = 0.012). The standard deviation of TTP in 16 segments (SD16) significantly increased post-MI: 28 ± 7 ms to 50 ± 10 ms (early, p = 0.012) to 54 ± 19 ms (late, p = 0.004), with no change between early and late post-MI time-points (p = 0.56). TTP was not related to reduction of segmental contractility. EAM revealed late electrical activation and greatly diminished conduction velocity in the infarct (5.7 ± 2.4 cm/s), when compared to peri-infarct (18.7 ± 10.3 cm/s) and remote myocardium (39 ± 20.5 cm/s). Conclusions Mechanical dyssynchrony occurs early after MI and is the result of delayed electrical and mechanical activation in the infarct.

High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the appearance of these images. This paper presents an approach SMORE based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images. This approach is self-supervised, which requires no external training data because the high-resolution and low-resolution data that are present in the image itself are used for training. For 3D MRI, the method consists of only one self-supervised super-resolution (SSR) deep CNN that is trained from the volumetric image data. For 2D MRI, there is a self-supervised anti-aliasing (SAA) deep CNN that precedes the SSR CNN, also trained from the volumetric image data. Both methods were evaluated on a broad collection of MR data, including filtered and downsampled images so that quantitative metrics could be computed and compared, and actual acquired low resolution images for which visual and sharpness measures could be computed and compared. The super-resolution method is shown to be visually and quantitatively superior to previously reported methods. [ABSTRACT FROM AUTHOR]

Background: In people with multiple sclerosis (MS), optic neuritis (ON) results in inner retinal layer thinning, and reduced density of the retinal microvasculature. Objective: To compare inter-eye differences (IEDs) in macular optical coherence tomography (OCT) and OCT angiography (OCTA) measures in MS patients with a history of unilateral ON (MS ON) vs. MS patients with no history of ON (MS non-ON), and to assess how these measures correlate with visual function outcomes after ON. Methods: In this cross-sectional study, people with MS underwent OCT and OCTA. Superficial vascular plexus (SVP) density of each eye was quantified using a deep neural network. IEDs were calculated with respect to the ON eye in MS ON patients, and with respect to the right eye in MS non-ON patients. Statistical analyses used mixed-effect regression models accounting for intra-subject correlations. Results: We included 43 MS ON patients (with 92 discrete OCT/OCTA visits) and 14 MS non-ON patients (with 24 OCT/OCTA visits). Across the cohorts, mean IED in SVP density was −2.69% (SD 3.23) in MS ON patients, as compared to 0.17% (SD 2.39) in MS non-ON patients (p = 0.002). When the MS ON patients were further stratified according to time from ON and compared to MS non-ON patients with multiple cross-sectional analyses, we identified that IED in SVP density was significantly increased in MS ON patients at 1–3 years (p = < 0.001) and >3 years post-ON (p < 0.001), but not at <3 months (p = 0.21) or 3–12 months post-ON (p = 0.07), while IED in ganglion cell + inner plexiform layer (GCIPL) thickness was significantly increased in MS ON patients at all time points post-ON (p ≦ 0.01 for all). IED in SVP density and IED in GCIPL thickness demonstrated significant relationships with IEDs in 100% contrast, 2.5% contrast, and 1.25% contrast letter acuity in MS ON patients (p < 0.001 for all). Conclusions: Our findings suggest that increased IED in SVP density can be detected after ON in MS using OCTA, and detectable changes in SVP density after ON may occur after changes in GCIPL thickness. IED in SVP density and IED in GCIPL thickness correlate well with visual function outcomes in MS ON patients. [ABSTRACT FROM AUTHOR]

Synthesizing a CT image from an available MR image has recently emerged as a key goal in radiotherapy treatment planning for cancer patients. CycleGANs have achieved promising results on unsupervised MR-to-CT image synthesis; however, because they have no direct constraints between input and synthetic images, cycleGANs do not guarantee structural consistency between these two images. This means that anatomical geometry can be shifted in the synthetic CT images, clearly a highly undesirable outcome in the given application. In this paper, we propose a structure-constrained cycleGAN for unsupervised MR-to-CT synthesis by defining an extra structure-consistency loss based on the modality independent neighborhood descriptor. We also utilize a spectral normalization technique to stabilize the training process and a self-attention module to model the long-range spatial dependencies in the synthetic images. Results on unpaired brain and abdomen MR-to-CT image synthesis show that our method produces better synthetic CT images in both accuracy and visual quality as compared to other unsupervised synthesis methods. We also show that an approximate affine pre-registration for unpaired training data can improve synthesis results. [ABSTRACT FROM AUTHOR]