Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications.

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models.

Parallel MRI Reconstruction Using Broad Learning System.

As an inverse problem, parallel magnetic resonance imaging (pMRI) reconstruction accelerates imaging speed by interpolating missing k-space data from a group of phased-array coils. Deep learning methods have been used for improving pMRI reconstruction quality in recent years. However, deep learning approaches need a large amount of training data that are acquired from different hardware configurations and anatomical areas.

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging.

Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and 'one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework.

DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES.

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered.

Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method.

Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs.

Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery.

This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI). Each temporal-domain MR image is viewed as a point that lies onto or close to a smooth manifold, and landmark points are identified to describe the point cloud concisely. To facilitate computations, a dimensionality reduction module generates low-dimensional/compressed renditions of the landmark points.

MLS: Joint Manifold-Learning and Sparsity-Aware Framework for Highly Accelerated Dynamic Magnetic Resonance Imaging.

Manifold-based models have been recently exploited for accelerating dynamic magnetic resonance imaging (dMRI). While manifold-based models have shown to be more efficient than conventional low-rank approaches, joint low-rank and sparsity-aware modeling still appears to be very efficient due to the inherent sparsity within dMR images. In this paper, we propose a joint manifold-learning and sparsity-aware framework for dMRI.

KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction.

The conventional calibration-based parallel imaging method assumes a linear relationship between the acquired multi-channel k-space data and the unacquired missing data, where the linear coefficients are estimated using some auto-calibration data. In this paper, we first analyze the model errors in the conventional calibration-based methods and demonstrate the nonlinear relationship. Then, a much more general nonlinear framework is proposed for auto-calibrated parallel imaging.

Bi-Linear Modeling of Manifold-Data Geometry for Dynamic-MRI Recovery.

This paper establishes a modeling framework for data located onto or close to (unknown) smooth manifolds, embedded in Euclidean spaces, and considers its application to dynamic magnetic resonance imaging (dMRI). The framework comprises several modules: First, a set of landmark points is identified to describe concisely a data cloud formed by highly under-sampled dMRI data, and second, low-dimensional renditions of the landmark points are computed. Searching for the linear operator that decompresses low-dimensional data to high-dimensional ones, and for those combinations of landmark points which approximate the manifold data by affine patches, leads to a bi-linear model of the dMRI data, cognizant of the intrinsic data geometry.

A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI.

While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models.

M-MRI: A Manifold-based Framework to Highly Accelerated Dynamic Magnetic Resonance Imaging.

High-dimensional signals, including dynamic magnetic resonance (dMR) images, often lie on low dimensional manifold. While many current dynamic magnetic resonance imaging (dMRI) reconstruction methods rely on priors which promote low-rank and sparsity, this paper proposes a novel manifold-based framework, we term M-MRI, for dMRI reconstruction from highly undersampled -space data. Images in dMRI are modeled as points on or close to a smooth manifold, and the underlying manifold geometry is learned through training data, called "navigator" signals.

ACCELERATING DYNAMIC MAGNETIC RESONANCE IMAGING BY NONLINEAR SPARSE CODING.

Although being high-dimensional, dynamic magnetic resonance images usually lie on low-dimensional manifolds. Nonlinear models have been shown to capture well that latent low-dimensional nature of data, and can thus lead to improvements in the quality of constrained recovery algorithms. This paper advocates a novel reconstruction algorithm for dynamic magnetic resonance imaging (dMRI) based on nonlinear dictionary learned from low-spatial but high-temporal resolution images.