Historically, clinical MRI started with main magnetic field strengths in the ∼0.05–0.35T range. In the past 40 years there have been considerable developments in MRI hardware, with one of the primary ones being the trend to higher magnetic fields. While resulting in large improvements in data quality and diagnostic value, such developments have meant that conventional systems at 1.5 and 3T remain relatively expensive pieces of medical imaging equipment, and are out of the financial reach for much of the world. In this review we describe the current state‐of‐the‐art of low‐field systems (defined as 0.25–1T), both with respect to its low cost, low foot‐print, and subject accessibility. Furthermore, we discuss how low field could potentially benefit from many of the developments that have occurred in higher‐field MRI. In the first section, the signal‐to‐noise ratio (SNR) dependence on the static magnetic field and its impact on the achievable contrast, resolution, and acquisition times are discussed from a theoretical perspective. In the second section, developments in hardware (eg, magnet, gradient, and RF coils) used both in experimental low‐field scanners and also those that are currently in the market are reviewed. In the final section the potential roles of new acquisition readouts, motion tracking, and image reconstruction strategies, currently being developed primarily at higher fields, are presented. Level of Evidence : 5 Technical Efficacy Stage : 1 J. Magn. Reson. Imaging 2019.

By using the complex signal, the AIF estimation becomes more accurate and precise. This might enable patient specific AIFs, thereby improving the quantitative values obtained from DCE-MRI. Magn Reson Med 76:1236-1245, 2016. © 2015 Wiley Periodicals, Inc.

T2 prepared imaging provides high quality reproducible T2 maps of healthy and diseased mouse myocardium.

Although thermal measures are directly coupled to tissue damage and therefore suitable for RF safety assessment, insecurities in the applied thermal modeling limit their estimation accuracy. Magn Reson Med 77:1691-1700, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

This study represents a first exploration of measuring thermoregulation, which will become essential when new safety guidelines are based on thermal dose.

ObjectivePelvic organ prolapse (POP) is clinically diagnosed in the supine position, where the effect of gravity is simulated by having the patients put strain on their pelvic floor. The objective of this study was to determine the degree of POP underestimation in the supine position based on magnetic resonance imaging (MRI) findings.MethodsThis prospective study was conducted with symptomatic POP grade ≥ 2 patients. Fifteen female patients were examined with an MRI system that allows supine and upright imaging. The differences between supine and upright in distances of the bladder neck, cervix, and pouch of Douglas from the pubococcygeal line (PCL) were estimated, together with changes in the genital hiatal area. Patients were scanned at rest and during straining. All distances were compared using the Wilcoxon ranking test.ResultsAll mean distances from the PCL increased from the supine–strain to the upright–rest and from the supine–strain to the upright–strain position. These distances were found in the supine and upright positions: the bladder descended 1.3 cm to 1.4 cm, the cervix 1.1 cm to 2.2 cm, and the pouch of Douglas 0.8 cm to 1.5 cm respectively (all p values <0.05). The hiatal area was larger in the upright–strain position (mean 42.0 cm2; SD ±14.8) than during the supine–strain position (mean 33.5 cm2; SD ±14.5), with a p value of 0.02.ConclusionUpright MRI scanning of patients with POP grade ≥ 2 both at rest and during straining shows a significantly larger extent of the prolapse than that observed during supine straining.

No abstract

Background: Respiratory‐induced motion (RIM) causes uncertainties in localizing hepatic lesions, which could lead to inaccurate targeting during interventions. One approach to mitigate the problem is respiratory motion estimation (RME), in which the liver motion is estimated by measuring external signals called surrogates. Methods: A learning‐based approach has been developed and validated to estimate the RIM of hepatic lesions. External markers placed on the human's abdomen were chosen as surrogates. Accordingly, appropriate motion models (multivariate, Ridge and Lasso regression models) were designed to correlate the liver motion with the abdominal motion, and trained to estimate the superior–inferior (SI) motion of the liver. Three subjects volunteered for 6 sessions of such that liver images acquired by magnetic resonance imaging (MRI) were recorded alongside camera‐tracked external markers. Results and conclusions: The proposed machine learning approach was validated in MRI on human subjects and the results show that the approach could estimate the respiratory‐induced SI motion of the liver with a mean absolute error (MAE) accuracy below 2 mm.