Towards quantitative [18F]FDG-PET/MRI of the brain: Automated MR-driven calculation of an image-derived input function for the non-invasive determination of cerebral glucose metabolic rates

• Published in: Journal of cerebral blood flow and metabolism Vol. 39; no. 8; pp. 1516 - 1530
• Main Authors: Sundar, Lalith KS, Muzik, Otto, Rischka, Lucas, Hahn, Andreas, Rausch, Ivo, Lanzenberger, Rupert, Hienert, Marius, Klebermass, Eva-Maria, Füchsel, Frank-Günther, Hacker, Marcus, Pilz, Magdalena, Pataraia, Ekaterina, Traub-Weidinger, Tatjana, Beyer, Thomas
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.08.2019
• Subjects: Adult; Algorithms; Brain - metabolism; Female; Fluorodeoxyglucose F18; Glucose - metabolism; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Angiography - methods; Male; Multimodal Imaging - methods; Neuroimaging - methods; Original; Positron-Emission Tomography - methods; [18F]FDG; Brain imaging; integrated PET/MRI; image-derived input function; cerebral metabolic rates
• ISSN: 0271-678X; 1559-7016
• DOI: 10.1177/0271678X18776820

Absolute quantification of PET brain imaging requires the measurement of an arterial input function (AIF), typically obtained invasively via an arterial cannulation. We present an approach to automatically calculate an image-derived input function (IDIF) and cerebral metabolic rates of glucose (CMRGlc) from the [18F]FDG PET data using an integrated PET/MRI system. Ten healthy controls underwent test–retest dynamic [18F]FDG-PET/MRI examinations. The imaging protocol consisted of a 60-min PET list-mode acquisition together with a time-of-flight MR angiography scan for segmenting the carotid arteries and intermittent MR navigators to monitor subject movement. AIFs were collected as the reference standard. Attenuation correction was performed using a separate low-dose CT scan. Assessment of the percentage difference between area-under-the-curve of IDIF and AIF yielded values within ±5%. Similar test–retest variability was seen between AIFs (9 ± 8) % and the IDIFs (9 ± 7) %. Absolute percentage difference between CMRGlc values obtained from AIF and IDIF across all examinations and selected brain regions was 3.2% (interquartile range: (2.4–4.3) %, maximum < 10%). High test–retest intravariability was observed between CMRGlc values obtained from AIF (14%) and IDIF (17%). The proposed approach provides an IDIF, which can be effectively used in lieu of AIF.