Simultaneous perfusion, diffusion, T2 *, and T1 mapping with MR fingerprinting

Quantitative non-contrast perfusion MRI in the body using arterial spin labeling

Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) method that enables the assessment and the quantification of perfusion without the need for an exogenous contrast agent. ASL was originally developed in the early 1990s to measure cerebral blood flow. The utility of ASL has since then broadened to encompass various organ systems, offering insights into physiological and pathological states. In this review article, we present a synopsis of ASL for quantitative non-contrast perfusion MRI, as a contribution to the special issue titled "Quantitative MRI-how to make it work in the body?" The article begins with an introduction to ASL principles, followed by different labeling strategies, such as pulsed, continuous, pseudo-continuous, and velocity-selective approaches, and their role in perfusion quantification. We proceed to address the technical challenges associated with ASL in the body and outline some of the innovative approaches devised to surmount these issues. Subsequently, we summarize potential clinical applications, challenges, and state-of-the-art ASL methods to quantify perfusion in some of the highly perfused organs in the thorax (lungs), abdomen (kidneys, liver, pancreas), and pelvis (placenta) of the human body. The article concludes by discussing future directions for successful translation of quantitative ASL in body imaging.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.