Abstract
We describe a fully automated processing pipeline to support the noninvasive absolute quantification of the cerebral metabolic rate for glucose (CMRGlc) in a clinical setting. This pipeline takes advantage of “anatometabolic” information associated with fully integrated PET/MRI. Methods: Ten healthy volunteers (5 men and /5 women; 27 ± 7 y old; 70 ± 10 kg) underwent a test-retest 18F-FDG PET/MRI examination of the brain. The imaging protocol consisted of a 60-min PET list-mode acquisition with parallel MRI acquisitions, including 3-dimensional time-of-flight MR angiography, MRI navigators, and a T1-weighted MRI scan. State-of-the-art MRI-based attenuation correction was derived from T1-weighted MRI (pseudo-CT [pCT]). For validation purposes, a low-dose CT scan was also performed. Arterial blood samples were collected as the reference standard (arterial input function [AIF]). The developed pipeline allows the derivation of an image-derived input function (IDIF), which is subsequently used to create CMRGlc maps by means of a Patlak analysis. The pipeline also includes motion correction using the MRI navigator sequence as well as a novel partial-volume correction that accounts for background heterogeneity. Finally, CMRGlc maps are used to generate a normative database to facilitate the detection of metabolic abnormalities in future patient scans. To assess the performance of the developed pipeline, IDIFs extracted by both CT-based attenuation correction (CT-IDIF) and MRI-based attenuation correction (pCT-IDIF) were compared with the reference standard (AIF) using the absolute percentage difference between the areas under the curves as well as the absolute percentage difference in regional CMRGlc values. Results: The absolute percentage differences between the areas under the curves for CT-IDIF and pCT-IDIF were determined to be 1.4% ± 1.0% and 3.4% ± 2.6%, respectively. The absolute percentage difference in regional CMRGlc values based on CT-IDIF and pCT-IDIF differed by less than 6% from the reference values obtained from the AIF. Conclusion: By taking advantage of the capabilities of fully integrated PET/MRI, we developed a fully automated computational pipeline that allows the noninvasive determination of regional CMRGlc values in a clinical setting. This methodology might facilitate the proliferation of fully quantitative imaging into the clinical arena and, as a result, might contribute to improved diagnostic efficacy.
Highlights
The proliferation of fully quantitative imaging into the clinical arena and, as a result, might contribute to improved diagnostic efficacy
· 100: to assess differences in regional cerebral metabolic rate for glucose (CMRGlc) values determined on the basis of the image-derived input function (IDIF) against the arterial input function (AIF), the absolute percentage error between CMRGlc values based on the 3 input functions was calculated for 6 brain regions: corpus callosum, brain stem, cerebellum, thalamus, anterior cingulate cortex, and superior frontal cortex
The absolute percentage error of regional CMRGlc values associated with the pCT-IDIF (5.8% 6 3.2%) was higher than that associated with the CT-IDIF (3.5% 6 2.1%)
Summary
Ten healthy volunteers (5 men and 5 women; 27 6 7 y old) were included in this study. An accurate correction for partial-volume distortions that affected the apparent tracer concentration in the Pmask mandated correction for background heterogeneity in both the radial and the circumferential directions (Fig. 3). Sampled MRI navigators interleaved between MR se- tracer concentration gradient was determined toward the brain, whereas quences were used to perform motion correction of PET images [14]. Segmentation of the tracer concentration using Otsu thresholding [23] at each time frame resulted in the following volumes within the background mantel: brain tissue (BSvol); the region located between the BS and the Pmask, representing the MZ (MZvol); and background sections (BGvolj, where j – 1,...M), representing heterogeneous tracer concentrations expressed predominantly in the circumferential direction (Fig. 3). 2 + BGj · RBGjCA: Once an estimate of CA9 has been calculated (CA9n), it can be iteratively improved by recalculating new estimates for BS9n11, BGj n119, and MZ9n11, yielding the updated CA9n11, as follows: BSn911
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