Towards a barrier-free anthropomorphic brain phantom for quantitative magnetic resonance imaging: Design, first construction attempt, and challenges.

Existing magnetic resonance imaging (MRI) reference objects, or phantoms, are typically constructed from simple liquid or gel solutions in containers with specific geometric configurations to enable multi-year stability. However, there is a need for phantoms that better mimic the human anatomy without barriers between the tissues. Barriers result in regions without MRI signal between the different tissue mimics, which is an artificial image artifact.

MR phase imaging with bipolar acquisition.

We have previously proposed a novel magnetic resonance (MR) phase imaging framework (MAGPI) based on a three-echo sequence that demonstrated substantial gains in phase signal-to-noise ratio (SNR). We improve upon the performance of MAGPI by extending the formulation to handle (i) an alternating gradient polarity (bipolar) readout scheme and (ii) an arbitrary number of echoes. We formulate the phase-imaging problem using maximum-likelihood (ML) estimation.

A Joint Acquisition-Estimation Framework for MR Phase Imaging.

Measuring the phase of the MR signal is faced with fundamental challenges such as phase aliasing, noise and unknown offsets of the coil array. There is a paucity of acquisition, reconstruction and estimation methods that rigorously address these challenges. This reduces the reliability of information processing in phase domain.

MAGPI: A framework for maximum likelihood MR phase imaging using multiple receive coils.

Purpose: Combining MR phase images from multiple receive coils is a challenging problem, complicated by ambiguities introduced by phase wrapping, noise, and the unknown phase-offset between the coils. Various techniques have been proposed to mitigate the effect of these ambiguities but most of the existing methods require additional reference scans and/or use ad hoc post-processing techniques that do not guarantee any optimality.

Theory And Methods: Here, the phase estimation problem is formulated rigorously using a maximum-likelihood (ML) approach.

High-resolution, large dynamic range field map estimation.

Purpose: We present a theory and a corresponding method to compute high-resolution field maps over a large dynamic range.

Theory And Methods: We derive a closed-form expression for the error in the field map value when computed from two echoes. We formulate an optimization problem to choose three echo times which result in a pair of maximally distinct error distributions.

Efficient and robust estimation of blood oxygenation levels in single cerebral veins.

Blood oxygenation level is an important measure that can be used alongside functional magnetic resonance imaging data in order to obtain closer correlates of neuronal activation. A robust estimate of this measure has thus far not been demonstrated. This is mainly due to the lack of knowledge of the underlying parameters which influence the numerical estimates of blood oxygenation.

A joint acquisition-reconstruction paradigm for correcting inhomogeneity artifacts in MR echo planar imaging.

One of the main sources of signal degradation in rapid MR acquisitions, such as Echo Planar Imaging (EPI), is magnetic field variations caused by field inhomogeneities and susceptibility gradients. If unaccounted for during the reconstruction process, this spatially-varying field can cause severe image artifacts. In this paper, we show that correcting for the resulting degradations can be formulated as a blind image deconvolution problem.

Resource-constrained rate control for Motion JPEG2000.

With the increasing importance of heterogeneous networks and time-varying communication channels, fine scalability has become a highly desirable feature in both image and video coders. A single highly scalable bitstream can provide precise rate control for constant bitrate (CBR) traffic and accurate quality control for variable bitrate (VBR) traffic. We first propose two leaky-bucket rate allocation methods that provide constant quality video under buffer constraints.

Collaborative multihop transmission of distributed sensor imagery.

We consider a network of imaging sensors. We address the problem of energy-efficient communication of the measurements of the sensors. A novel algorithm is presented for the purpose of exploiting intersensor and intrasensor correlation, which is inherent in a network of imaging sensors.

A method for coordinating the distributed transmission of imagery.

Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the sensor network lifetime.

Efficient storage and transmission of ladar imagery.

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%.