Hypometabolic patterns of focal cortical dysplasia in PET-MRI co-registration imaging: a retrospective evaluation in a series of 83 patients

Objective

To characterize the PET-MRI co-registration of hypometabolic patterns in focal cortical dysplasia (FCD) types I and II and provide some suggestions in presurgical evaluation of epilepsy surgery.

Methods

We retrospectively analyzed PET-MRI co-registration imaging data from a cohort of 83 epilepsy patients with histologically confirmed FCD types I and II. Hypometabolic patterns were classified into 4 types: bottom of sulcus hypometabolism (BOSH), single island of sulcus hypometabolism (SIOS), single gyrus or sulcus hypometabolism (SGOS), and multiple gyri and sulci hypometabolism (MGOS).

Results

Most of cases that were overlooked by conventional MRI and PET evaluation but positive in PET-MRI co-registration were focalized lesions in dorsolateral frontal lobe (9/15) and FCD type IIa was the most prevalent pathological type (11/15). The FCD histological types (p = 0.027) and locations (p < 0.001) were independent predictors of PET-MRI co-registration hypometabolic patterns. Focalized hypometabolic patterns (BOSH, SIOS, SGOS) were primarily observed in the frontal lobe (33/39) and FCD type II (43/62) and extensive pattern (MGOS) in temporal lobe (18/20) and FCD type I (16/21; p < 0.005).

Conclusion

PET-MRI co-registration enhanced the detection of FCD type IIa compared with conventional MRI and PET reading. The hypometabolic patterns of FCD type I and temporal lobe FCD were more extensive than those of FCD type II and frontal lobe FCD, respectively. The predilection of focalized hypometabolic patterns in frontal lobe FCD suggested that subtle lesions should be checked carefully in patients with suspected frontal lobe epilepsy.

A Comparison of Two Statistical Mapping Tools for Automated Brain FDG-PET Analysis in Predicting Conversion to Alzheimer's Disease in Subjects with Mild Cognitive Impairment

OBJECTIVE

Automated voxel-based analysis methods are used to detect cortical hypometabolism typical of Alzheimer's Disease (AD) on FDG-PET brain scans. We compared the accuracy of two clinically validated tools for their ability to identify those MCI subjects progressing to AD at followup, to evaluate the impact of the analysis method on FDG-PET diagnostic performance.

METHODS

SPMGrid and BRASS (Hermes Medical Solutions, Stockholm, Sweden) were tested on 131 MCI and elderly healthy controls from the EADC PET dataset. The concordance between the tools was tested by correlating the quantitative parameters (z- and t-values), calculated by the two software tools, and by measuring the topographical overlap of the abnormal regions (Dice score). Three independent expert readers blindly assigned a diagnosis based on the two map sets. We used conversion to AD dementia as the gold standard.

RESULTS

The t-map and z-map calculated with SPMGrid and BRASS, respectively, showed a good correlation (R > .50) for the majority of individual cases (128/131) and for the majority of selected regions of interest (ROIs) (98/116). The overlap of the hypometabolic patterns from the two tools was, however, poor (Dice score .36). The diagnostic performance was comparable, with BRASS showing significantly higher sensitivity (.82 versus .59) and SPMGrid showing higher specificity (.87 versus .52).

CONCLUSION

Despite similar diagnostic performance in predicting conversion to AD in MCI subjects, the two tools showed significant differences, and the maps provided by the tools showed limited overlap. These results underline the urgency for standardization across FDG-PET analysis methods for their use in clinical practice.