Low‐dose T1W DCE‐MRI for early time points perfusion measurement in patients with intracranial tumors: A pilot study applying the microsphere model to measure absolute cerebral blood flow

• Published in: Journal of magnetic resonance imaging Vol. 48; no. 2; pp. 543 - 557
• Main Authors: Li, Ka‐Loh, Lewis, Daniel, Jackson, Alan, Zhao, Sha, Zhu, Xiaoping
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.08.2018
• Subjects: Aged; Bevacizumab; Bevacizumab - therapeutic use; Blood flow; Brain Neoplasms - diagnostic imaging; Cerebral blood flow; Cerebrovascular Circulation; Coefficient of variation; Computer simulation; Contrast agents; Data processing; dynamic contrast enhanced MRI; dynamic susceptibility contrast‐enhanced MRI; early time points; Female; Field strength; Genetic disorders; Glioblastoma; Glioblastoma - diagnostic imaging; Humans; Imaging, Three-Dimensional; intracranial tumor; low‐dose gadolinium‐based contrast agent; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Microspheres; Middle Aged; Models, Statistical; Monoclonal antibodies; Monte Carlo Method; Monte Carlo simulation; Neurilemmoma - diagnostic imaging; Neurofibromatosis; Neurofibromatosis 2; Neurofibromatosis 2 - diagnostic imaging; Neurofibromin 2; Neurological disorders; Noise levels; Patients; Perfusion; Pilot Projects; Reproducibility; Reproducibility of Results; Retrospective Studies; Schwann cells; Statistical analysis; Statistical tests; Substantia alba; Substantia grisea; Time Factors; Tumors; Vestibular system; low-dose gadolinium-based contrast agent; cerebral blood flow; early time points; intracranial tumor; dynamic susceptibility contrast-enhanced MRI; dynamic contrast enhanced MRI
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.25979

BACKGROUND Previous studies have measured cerebral blood flow (CBF) with DSC‐MRI using an “early time points” (ET) method based on microsphere theory. PURPOSE To develop and assess a new ET method for absolute CBF estimation using low‐dose high‐temporal (LDHT) T1W‐DCE‐MRI. STUDY TYPE Retrospective cohort study. SUBJECTS Seven patients with sporadic vestibular schwannoma (VS) who underwent test–retest imaging; one patient with glioblastoma multiforme (GBM) imaged pretreatment; and 12 neurofibromatosis type 2 (NF2) patients undergoing bevacizumab treatment, imaged pre‐ and 90 days posttreatment. FIELD STRENGTH/SEQUENCE LDHT‐DCE‐MRI was performed at 1.5 and 3.0T, using 3D spoiled gradient echo with phase cycling. DSC‐MRI performed in one patient, using 3D echo‐shifted multi‐shot echo‐planar imaging (PRESTO) at 3T. ASSESSMENT Through Monte Carlo simulations, CBF estimation using three newly developed average contrast agent concentration (AC) ‐based methods (ACrPK, ACrMG, ACcomb), was compared against conventional maximum gradient (MG) approaches, at varying Rician noise levels. Reproducibility and applicability of the ACcomb method was assessed in our sporadic‐VS/GBM/NF2 patient cohort, respectively. STATISTICAL TESTS Reproducibility was measured using test–retest coefficient of variation (CoV). Pre‐ and posttreatment CBF values were compared using paired t‐test with Bonferroni correction. RESULTS Monte Carlo stimulations demonstrated that AC‐based methods, particularly ACcomb, offered superior accuracy to conventional MG approaches. Overall test–retest CoV using the ACcomb method was 5.76 in normal‐appearing white matter (NAWM). The new ACcomb method produced gray matter/white matter CBF estimates in the NF2 patient cohort of 55.9 ± 13.9/25.8 ± 3.5 on day 0; compared with 155.6 ± 17.2/128.4 ± 29.1 for the classical MG method. There was a moderate (10% using ACcomb and ACrPK) increase in CBF of NAWM 90 days post therapy (P = 0.03 and 0.005). DATA CONCLUSION Our new AC‐based method of CBF estimation offers excellent reproducibility, and displays more accuracy in both Monte Carlo analysis and clinical data application, than conventional MG‐based approaches. Level of Evidence: 1 Technical Efficacy: Stage 4 J. MAGN. RESON. IMAGING 2018;48:543–557.