The primary result of this study is that MR fusion increases the AUC and the predictive accuracy for the SOR and SCR of FDOPA PET. The comparisons between fused and NF images for SOR were statistically significant. For the SCR the all NF group was statistically significant and the NF matched showed a trend toward statistical significance. Comparison between the SOR and the SCR did not show any significant differences. The SCR did have uniformly higher AUCs than the SOR for the fused and NF groups. The cerebellum may have performed as a more stable reference standard because the ROI used for the cerebellum was much larger (both hemispheres and vermis) than the occipital reference standard (100 mm2 at the level of the basal ganglia).
It follows that MRI fusion may better predict pathology by improving the accuracy of ROI placement, and thus of ratios derived from these ROIs. The ROIs on the FDOPA PET (the non-fused images) used a set level above background cortex (10%), while the MR uses gray/white matter differentiation of the basal ganglia as ROI boundaries. The pathologic process of PD decreases this striatal FDOPA uptake in a posterior to anterior manner. [22
] Thus, ROIs drawn directly on PET over-estimate residual activity (SUVmean) in the posterior putamen. The MRI has the advantage of allowing ROIs to be defined structurally rather than functionally.
Pan et al
. recognized that posterior putamen signal loss was a potential confounder with FDOPA PET images and proposed that a fixed size of ROI should be maintained for the striatum. This method is superior to ROI defined by a percentage above background or within a maximum, but it fails to account for individual variations in brain structure. Even small amounts of cortex and white matter in an ROI intended to be part of the putamen could depress the SUVmean resulting in increased false positives, making the test less accurate for patients with cerebral volume loss. A study with similar methods (using MRI for ROI generation), but aimed at comparing the graphical and ratio methods, found AUCs similar to this study. With 89 subjects, AUCs of 0.99, 0.99, and 0.79 were found for the caudate, anterior putamen, and posterior putamen contralateral to predominant symptoms. [12
] It is clear that FDOPA PET interpretation can be aided with simple adjunct quantitative measures such as mean SUV ratios from ROIs drawn with MR fusion. In this study the entire striatum was used for a region of interest, but dividing the striatum into several parts (as in the previously cited study) or even voxel-based analysis can allow for improved discriminatory power for disease states with more subtle pathophysiologic differences like multiple systems atrophy and PD, but proper function-anatomic correlation with techniques like MR/PET fusion is necessary for these techniques to reach their full potential. Long and complex post-image processing is not necessary for highly reliable and accurate results, but one has to be cautious in defining ROIs strictly based on percentages above background or on the voxel with the highest uptake.
This study only had 27 subjects, and of these only 17 had an MR that was fusible, but the study size was adequate to detect a difference between several measures, however this study has low power due to low sample size, and that the significant findings could be due to multiple testing, and are subject to further verification. More complex means of ROI creation including deformation into Montreal Neurologic Institute space with preformed ROIs might offer even greater inter-reader reliability and discriminatory power, but would retain problems with imperfect co-registration of subcortical structures between subjects. A study comparing these methods to manual per subject ROI drawing is yet to be completed, but these more advanced techniques do not seem necessary for accurate reliable clinical interpretation of FDOPA PET. The reference standard used for diagnosis in this study is the clinical follow-up by a movement disorder specialist. This is the current gold standard for diagnosis of premorbid Parkinson’s disease. However, the specialist was not blinded to the results of the FDOPA PET and this is a potential area of bias. The patient’s long follow-up (median 4 years) probably lessened this bias, as the influence of decreases over time and as the disease progresses, diagnosis become more evident.
A potential drawback of this technique is that not all patients have an MR available for fusion. Ten of the 27 patients in this study did not have a fusible MR. The main reasons for not having a fusible MR were that an MR was not available in PACS or that is was a scanned image of film MR. All of these patients had a head MR at some point in their clinical work-up, but generally this was done prior to referral to a specialist in movement disorders. These images were often sent with the patient in a film format or scanned film format, and not as a DICOM file. Within the last several years PACS systems have become a standard at even small healthcare facilities. This should allow for greater accessibility and ease in fusing FDOPA PET and MR for referral centers, mitigating this problem in the future. It should be noted that for PET studies in general that do not have appropriate anatomic scan for fusion, SUVmax remains a valid measure that is less likely to be influenced by ROI placement.
This study demonstrates that MRI fusion with FDOPA PET via readily available commercial software improves at least some of the quantitative measures used to clinically interpret FDOPA PET. Nuclear medicine physicians and radiologists may want to explore MR fusion for FDOPA PET interpretation within their own software/hardware paradigm. FDOPA PET is another clinical scenario where PET/MRI may show an advantage in the future. Potentially MR fusion will also find a role in the recently FDA approved dopamine transport single photon emission tomography (SPECT).