Evaluation of the automatic three-dimensional delineation of caudate and putamen for PET receptor occupancy studies

[11C]SCH23390 binding to the D1-dopamine receptor in the human brain-a comparison of manual and automated methods for image analysis

Background

The D₁-dopamine receptor radioligand [¹¹C]SCH23390 has been frequently used in PET studies. In drug-naïve patients with schizophrenia, the findings have been inconsistent, with decreases, increases, and no change in the frontal cortex D₁-dopamine receptors. While these discrepancies are likely primarily due to a lack of statistical power in these studies, we speculated that an additional explanation may be the differences due to methods of image analysis between studies, affecting reliability as well as bias between groups.

Methods

Fifteen healthy subjects underwent two PET measurements with [¹¹C]SCH23390 on the same day. The binding potential (BP_ND) was compared using a 95% confidence interval following manual and automated delineation of a region of interest (ROI) as well as with and without frame-by-frame realignment.

Results

Automated target region delineation produced lower BP_ND values, while automated delineation of the reference region yielded higher BP_ND values. However, no significant differences were observed for repeatability using automated and manual delineation methods. Frame-by-frame realignment generated higher BP_ND values and improved repeatability.

Conclusions

The results suggest that the choice of ROI delineation method is not an important factor for reliability, whereas the improved results following movement correction confirm its importance in PET image analysis. Realignment is therefore especially important for measurements in patient populations such as schizophrenia or Parkinson’s disease, where motion artifacts may be more prevalent.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0416-2) contains supplementary material, which is available to authorized users.

Comparison of the biological effects of (18)F at different intracellular levels

We herein examined the biological effects of cells treated with (18)F labeled drugs for positron emission tomography (PET). The relationship between the intracellular distribution of (18)F and levels of damaged DNA has yet to be clarified in detail. We used culture cells (Chinese Hamster Ovary cells) treated with two types of (18)F labeled drugs, fluorodeoxyglucose (FDG) and fluorine ion (HF). FDG efficiently accumulated in cells, whereas HF did not. To examine the induction of DNA double strand breaks (DSB), we measured the number of foci for 53BP1 that formed at the site of DNA DSB. The results revealed that although radioactivity levels were the same, the induction of 53BP1 foci was stronger in cells treated with (18)F-FDG than in those treated with (18)F-HF. The clonogenic survival of cells was significantly lower with (18)F-FDG than with (18)F-HF. We concluded that the efficient accumulation of (18)F in cells led to stronger biological effects due to more severe cellular lethality via the induction of DNA DSB.

Quantification of receptor-ligand binding potential in sub-striatal domains using probabilistic and template regions of interest

Sub-striatal regions of interest (ROIs) are widely used in PET studies to investigate the role of dopamine in the modulation of neural networks implicated in emotion, cognition and motor function. One common approach is that of Mawlawi et al. (2001) and Martinez et al. (2003), where each striatum is divided into five sub-regions. This study focuses on the use of two spatial normalization-based alternatives to manual sub-striatal ROI delineation per subject: manual ROI delineation on a template brain and the production of probabilistic ROIs from a set of subject-specific manually delineated ROIs. Two spatial normalization algorithms were compared: SPM5 unified segmentation and ART. The ability of these methods to quantify sub-striatal regional non-displaceable binding potential (BP(ND)) and BP(ND) % change (following methylphenidate) was tested on 32 subjects (16 controls and 16 ADHD patients) scanned with the dopamine D(2)/D(3) ligand [(18)F]fallypride. Probabilistic ROIs produced by ART provided the best results, with similarity index values against subject-specific manual ROIs of 0.75-0.89 (mean 0.84) compared to 0.70-0.85 (mean 0.79) for template ROIs. Correlations (r) for BP(ND) and BP(ND) % change between subject-specific manual ROIs and these probabilistic ROIs of 0.90-0.98 (mean 0.95) and 0.98-1.00 (mean 0.99) respectively were superior overall to those obtained with template ROIs, although only marginally so for BP(ND) % change. The significance of relationships between BP(ND) measures and both behavioural tasks and methylphenidate plasma levels was preserved with ART combined with both probabilistic and template ROIs. SPM5 virtually matched the performance of ART for BP(ND) % change estimation but was inferior for BP(ND) estimation in caudate sub-regions. ART spatial normalization combined with probabilistic ROIs and to a lesser extent template ROIs provides an efficient and accurate alternative to time-consuming manual sub-striatal ROI delineation per subject, especially when the parameter of interest is BP(ND) % change.

Segmentation of striatal brain structures from high resolution PET images

We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric (11)C-raclopride positron emission tomography (PET) brain images. We focus on the images acquired using a novel brain dedicated high-resolution (HRRT) PET scanner. The segmentation method first extracts the striatum using a deformable surface model and then divides the striatum into its substructures based on a graph partitioning algorithm. The weighted kernel k-means algorithm is used to partition the graph describing the voxel affinities within the striatum into the desired number of clusters. The method was experimentally validated with synthetic and real image data. The experiments showed that our method was able to automatically extract caudate, ventral striatum, and putamen from the images. Moreover, the putamen could be subdivided into anterior and posterior parts. An automatic method for the extraction of striatal structures from high-resolution PET images allows for inexpensive and reproducible extraction of the quantitative information from these images necessary in brain research and drug development.