Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter K(trans) that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.

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Background: Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce the risk of heart failure hospitalization and cardiovascular death in patients with heart failure and reduced ejection fraction (HFrEF). However, their effects on cardiac structure and function in HFrEF are uncertain. Methods: We designed a multicenter randomized, double-blind, placebo-controlled trial to investigate the cardiac effects of empagliflozin in patients in NYHA functional class II to IV with a left ventricular (LV) ejection fraction ≤40% and type 2 diabetes or prediabetes. Patients were randomized 1:1 to empagliflozin 10 milligrams once daily or placebo, stratified by age (<65 and ≥65 years) and glycemic status (diabetes or prediabetes). The co-primary outcomes were change from baseline to 36 weeks in LV end-systolic volume indexed to body surface area (LVESVi) and LV global longitudinal strain (LV GLS) measured using cardiovascular magnetic resonance (CMR). Secondary efficacy outcomes included other CMR measures (LVEDVi, LVEF), diuretic intensification, symptoms (Kansas City Cardiomyopathy Questionnaire Total Symptom Score (KCCQ-TSS)), 6-minute walk distance (6MWD), B-lines on lung ultrasound and biomarkers (including NT-proBNP). Results: From April 2018 to August 2019, 105 patients were randomized: 77 (73.3%) male, mean age 68.7 [SD 11.1] years, 82 (78.1%) diabetes and 23 (21.9%) prediabetes, mean LVEF 32.5% [9.8%], and 81 (77.1%) NYHA II and 24 (22.9%) NYHA III. Patients received standard treatment for HFrEF. Compared with placebo, empagliflozin reduced LVESVi by 6.0 (-10.8 to -1.2) ml/m 2 (p=0.015). There was no difference in LV GLS. Empagliflozin reduced LVEDVi by 8.2 (-13.7 to -2.6) ml/m 2 (p=0.0042) and reduced NT-proBNP by 28 (2 to 47) %, p=0.038. There were no between-group differences in other CMR measures, KCCQ-TSS, 6MWD or B-lines. Conclusions: The SGLT2 inhibitor empagliflozin reduced LV volumes in patients with HFrEF and type 2 diabetes or prediabetes. Favorable reverse LV remodeling may be a mechanism by which SGLT2 inhibitors reduce HF hospitalization and mortality in HFrEF. Clinical Trial Registration: URL: https://www.clinicaltrials.gov Unique Identifier: NCT03485092.

Dynamic susceptibility contrast MRI (DSC-MRI) is the current standard for the measurement of Cerebral Blood Flow (CBF) and Cerebral Blood Volume (CBV), but it is not suitable for the measurement of Extraction Flow (EF) and may not allow for absolute quantification. The objective of this study was to develop and evaluate a methodology to measure CBF, CBV, and EF from T1-weighted dynamic contrast-enhanced MRI (DCE-MRI). A twocompartment modeling approach was developed, which applies both to tissues with an intact and with a broken Blood-BrainBarrier (BBB). The approach was evaluated using measurements in normal grey matter (GM) and white matter (WM) and in tumors of 15 patients. Accuracy and precision were estimated with simulations of normal brain tissue. All tumor and normal tissue curves were accurately fitted by the model. CBF (mL/100 mL/min) was 82 ± 21 in GM and 23 ± 14 in WM, CBV (mL/100 mL) was 2.6 ± 0.8 in GM and 1.3 ± 0.4 in WM. EF (mL/100 mL/min) was close to zero in GM (−0.009 ± 0.05) and WM (−0.03 ± 0.08). Simulations show an overlap between CBF values of WM and GM, which is eliminated when Contrast-to-Noise (CNR) is improved. The model provides a consistent description of tracer kinetics in all brain tissues, and an accurate assessment of perfusion and permeability in reference tissues. The measurement sequence requires optimization to improve CNR and the precision in the perfusion parameters. With this approach, DCE-MRI presents a promising alternative to DSC-MRI for quantitative bolustracking in the brain.