Dynamic susceptibility contrast (DSC) MRI is clinically used to measure brain perfusion by monitoring the dynamic passage of a bolus of contrast agent through the brain. For quantitative analysis of the DSC images, the arterial input function is required. It is known that the original assumption of a linear relation between the R2(*) relaxation and the arterial contrast agent concentration is invalid, although the exact relation is as of yet unknown. Studying this relation in vitro is time‐consuming, because of the widespread variations in field strengths, MRI sequences, contrast agents, and physiological conditions. This study aims to simulate the R2(*) versus contrast concentration relation under varying physiological and technical conditions using an adapted version of an open‐source simulation tool. The approach was validated with previously acquired data in human whole blood at 1.5 T by means of a gradient‐echo sequence (proof‐of‐concept). Subsequently, the impact of hematocrit, field strength, and oxygen saturation on this relation was studied for both gradient‐echo and spin‐echo sequences. The results show that for both gradient‐echo and spin‐echo sequences, the relaxivity increases with hematocrit and field strength, while the hematocrit dependency was nonlinear for both types of MRI sequences. By contrast, oxygen saturation has only a minor effect. In conclusion, the simulation setup has proven to be an efficient method to rapidly calibrate and estimate the relation between R2(*) and gadolinium concentration in whole blood. This knowledge will be useful in future clinical work to more accurately retrieve quantitative information on brain perfusion.

Characterization of tumor microvasculature is important in tumor assessment and studying treatment response. This is possible by acquiring vascular biomarkers with magnetic resonance imaging (MRI) based on dynamic susceptibility contrast (DSC). We propose magnetic resonance vascular fingerprinting (MRVF) for hybrid echo planar imaging (HEPI) acquired during the first passage of the contrast agent (CA). The proposed approach was evaluated in patients with gliomas, and we simultaneously estimated vessel radius and relative cerebral blood volume. These parameters were also compared to the respective values estimated using the previously introduced vessel size imaging (VSI) technique. The results of both methods were found to be consistent. MRVF was also found to be robust to noise in the estimation of the parameters. DSC-HEPI-based MRVF provides characterization of microvasculature in gliomas with a short acquisition time and can be further improved in several ways to increase our understanding of tumor physiology.

In the clinical follow-up of glioblastoma patients, presence of delayed arterial transit times (ATT) could affect the evaluation of ASL perfusion data. In this retrospective study the influence of the presence and severity of ATT-artifacts on perfusion assessment and differentiation between tumor progression and pseudo-progression were studied. The results show that the presence of ATT-artifacts lowers the agreement between radiological evaluation of DSC-MRI and ASL, although the severity of ATT-artifacts did not have significant influence. In conclusion, detection of ATT-artifacts is important as it could affect radiological evaluation of ASL-data. Future work aims to include additional quantitative perfusion measures.

This study uses an MRVF approach to analyze the time evolution of a DSC hybrid -EPI (HEPI) sequence that simultaneously acquires gradient and spin echo images. HEPI properties are incorporated from the scanner into simulations including contrast agent extravasation, diffusion, and MR signal evolution and for varying the outcome parameters: CBV, mean vessel-size, and leakage. In vivo data of six glioma patients are used to compare MRVF output maps to those obtained from conventional VSI modelling. The results show reasonable agreement. Also, the noise sensitivity of both techniques was investigated.

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