DeepHarmony: A deep learning approach to contrast harmonization across scanner changes

Artificial Intelligence and Texture Analysis in Cardiac Imaging

PURPOSE OF REVIEW

The aim of this structured review is to summarize the current research applications and opportunities arising from artificial intelligence (AI) and texture analysis with regard to cardiac imaging.

RECENT FINDINGS

Current research findings suggest tremendous potential for AI in cardiac imaging, especially with regard to objective image analyses, overcoming the limitations of an observer-dependent subjective image interpretation. Researchers have used this technique across multiple imaging modalities, for instance to detect myocardial scars in cardiac MR imaging, to predict contrast enhancement in non-contrast studies, and to improve image acquisition and reconstruction. AI in medical imaging has the potential to provide novel, much-needed applications for improving patient care pertaining to the cardiovascular system. While several shortcomings are still present in the current methodology, AI may serve as a resourceful assistant to radiologists and clinicians alike.

Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

In vivo magnetic resonance imaging and spectroscopy. Technological advances and opportunities for applications continue to abound

Over the past decades, the field of in vivo magnetic resonance (MR) has built up an impressive repertoire of data acquisition and analysis technologies for anatomical, functional, physiological, and molecular imaging, the description of which requires many book volumes. As such it is impossible for a few authors to have an authoritative overview of the field and for a brief article to be inclusive. We will therefore focus mainly on data acquisition and attempt to give some insight into the principles underlying current advanced methods in the field and the potential for further innovation. In our view, the foreseeable future is expected to show continued rapid progress, for instance in imaging of microscopic tissue properties in vivo, assessment of functional and anatomical connectivity, higher resolution physiologic and metabolic imaging, and even imaging of receptor binding. In addition, acquisition speed and information content will continue to increase due to the continuous development of approaches for parallel imaging (including simultaneous multi-slice imaging), compressed sensing, and MRI fingerprinting. Finally, artificial intelligence approaches are becoming more realistic and will have a tremendous effect on both acquisition and analysis strategies. Together, these developments will continue to provide opportunity for scientific discovery and, in combination with large data sets from other fields such as genomics, allow the ultimate realization of precision medicine in the clinic.