Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.

Real-time magnetic resonance imaging (rtMRI) is a valuable emerging tool for studying the dynamics of vocal production. Conventional 2D rtMRI typically images the midsagittal plane of the vocal tract, acquiring data from all the important articulators. Dynamic 3D MRI would be a major advance, as it would provide 3D visualization of the vocal tract shaping dynamics, especially for the modeling of complex vocal tract geometries, such as liquid and fricative consonants, which is not easily available from 2D rtMRI. In this paper, we present an approach to directly acquire data in 3D using a highly temporally-undersampled stack-of-spirals imaging sequence, and perform reconstruction using a partially separable model. We demonstrate visualization of vocal tract dynamics from midsagittal and coronal scan planes in the articulation of fricative consonants. Index Terms: speech production, vocal tract shaping, dynamic 3D MRI, spiral imaging, English fricative

Noninvasive imaging is widely used in speech research as a means to investigate the shaping and dynamics of the vocal tract during speech production. 3-D dynamic MRI would be a major advance, as it would provide 3-D dynamic visualization of the entire vocal tract. We present a novel method for the creation of 3-D dynamic movies of vocal tract shaping based on the acquisition of 2-D dynamic data from parallel slices and temporal alignment of the image sequences using audio information. Multiple sagittal 2-D real-time movies with synchronized audio recordings are acquired for English vowel-consonant-vowel stimuli /ala/, /a.ιa/, /asa/, and /a∫a/. Audio data are aligned using mel-frequency cepstral coefficients (MFCC) extracted from windowed intervals of the speech signal. Sagittal image sequences acquired from all slices are then aligned using dynamic time warping (DTW). The aligned image sequences enable dynamic 3-D visualization by creating synthesized movies of the moving airway in the coronal planes, visualizing desired tissue surfaces and tube-shaped vocal tract airway after manual segmentation of targeted articulators and smoothing. The resulting volumes allow for dynamic 3-D visualization of salient aspects of lingual articulation, including the formation of tongue grooves and sublingual cavities, with a temporal resolution of 78 ms.

Conventional real-time magnetic resonance imaging (RT-MRI) of the upper airway typically acquires information about the vocal tract from a single midsagittal scan plane. This provides insights into the dynamics of all articulators, but does not allow for visualization of several important features in vocal tract shaping, such as grooving/doming of the tongue, asymmetries in tongue shape, and lateral shaping of the pharyngeal airway. In this paper, we present an approach to RT-MRI of multiple scan planes of interest using time-interleaved acquisition, in which temporal resolution is compromised for greater spatial coverage. We demonstrate simultaneous visualization of vocal tract dynamics from midsagittal, coronal, and axial scan planes in the articulation of English fricatives.

This paper describes a complete system to create anatomically accurate example-based volume deformation and animation of articulated body regions, starting from multiple in vivo volume scans of a specific individual. In order to solve the correspondence problem across volume scans, a template volume is registered to each sample. The wide range of pose variations is first approximated by volume blend deformation (VBD), providing proper initialization of the articulated subject in different poses. A novel registration method is presented to efficiently reduce the computation cost while avoiding strong local minima inherent in complex articulated body volume registration. The algorithm highly constrains the degrees of freedom and search space involved in the nonlinear optimization, using hierarchical volume structures and locally constrained deformation based on the biharmonic clamped spline. Our registration step establishes a correspondence across scans, allowing a data-driven deformation approach in the volume domain. The results provide an occlusion-free person-specific 3D human body model, asymptotically accurate inner tissue deformations, and realistic volume animation of articulated movements driven by standard joint control estimated from the actual skeleton. Our approach also addresses the practical issues arising in using scans from living subjects. The robustness of our algorithms is tested by their applications on the hand, probably the most complex articulated region in the body, and the knee, a frequent subject area for medical imaging due to injuries.

In speech research using real-time magnetic resonance imaging (RT-MRI), frame reconstruction is typically performed with a constant temporal resolution. However, a flexible selection of temporal resolution is desirable because of natural variations in speaking rate and variations in the speed of different articulators. In this work, a novel spiral golden-ratio temporal view order was applied to nasal RT-MRI studies in imaging a mid-sagittal slice of the upper airway. Compared to conventional spiral bit-reversed temporal view order scheme, the proposed golden-ratio scheme provides less temporal blurring in the depiction of rapid tongue tip motion with a selection of high temporal resolution. It also provides higher signal-to-noise ratio (SNR) in the depiction of relatively slow velar motion with a selection of low temporal resolution. Index Terms: speech production, vocal tract shaping, real-time MRI, golden ratio, spiral trajectory

3D MRI of the upper airway has provided valuable insights into vocal tract shaping and data for the modeling of speech production. Small movements of articulators can lead to large changes in the produced sound, therefore improving the resolution of these datasets, within the constraints of a sustained sound (6-12 seconds), is an important area for investigation. This paper provides the first application of compressed sensing (CS) with parallel imaging to high-resolution 3D upper airway MRI. We use spatial finite difference as the sparsifying transform, and investigate the use of high-resolution phase information as a constraint during CS reconstruction. In a retrospective subsampling experiment with no sound production, 5x undersampling produced acceptable image quality when using phase-constrained CS reconstruction. The prospective use of this accelerated acquisition enabled 3D vocal-tract MRI during sustained production of English /s/,/int/,/i/,/r/ with 1.33times1.33times1.33-mm3spatial resolution and 10-seconds of scan time.

Articulated body animation with smooth skin deformation is an important topic in computer graphics. This paper presents a pipeline that extends articulated body deformation to the volume graphics domain. The pipeline consists of in-vivo volume scans, kinematic joint estimation, volumetric joint weight computation, soft-tissue volume deformation, and direct volume rendering. The result is a fully articulated body volume driven by intuitive joint control that respects rigid deformation of the bone structures and produces smooth deformations of both the skin surface and the interior soft tissue regions.

The temporal resolution of dynamic magnetic resonance imaging (MRI) can be increased by sampling a fraction of k-space in an interleaved fashion, which introduces spatial and temporal aliasing. We describe algebraically and graphically the aliasing process caused by dynamic undersampled spiral imaging within 3-D xyf space (the Fourier transform of k(x)k(y)t space) and formulate the unaliasing problem as a set of independent linear inversions. Since each linear system is numerically underdetermined, the use of prior knowledge in the form of bounded support regions is proposed. To overcome the excessive memory requirements for handling large matrices, a fast implementation of the conjugate gradient (CG) method is used. Numerical simulation and in vivo experiments using spiral twofold undersampling demonstrate reduced motion artifacts and the improved depiction of fine cardiac structures. The achieved reduction of motion artifacts and motion blur is comparable to simple filtering, which is computationally more efficient, while the proposed algebraic framework offers greater flexibility to incorporate additional algebraic acceleration techniques and to handle arbitrary sampling schemes.