Validation of Parametric Methods for [11C]UCB-J PET Imaging Using Subcortical White Matter as Reference Tissue

PURPOSE

The aim of this study was to evaluate different non-invasive methods for generating (R)-1-((3-([¹¹C]methyl)pyridin-4-yl)methyl)-4-(3,4,5-trifluorophenyl)pyrrolidin-2-one) ([¹¹C]UCB-J) parametric maps using white matter (centrum semi-ovale-SO) as reference tissue.

PROCEDURES

Ten healthy volunteers (8 M/2F; age 27.6 ± 10.0 years) underwent a 90-min dynamic [¹¹C]UCB-J positron emission tomography (PET) scan with full arterial blood sampling and metabolite analysis before and after administration of a novel chemical entity with high affinity for presynaptic synaptic vesicle glycoprotein 2A (SV2A). A simplified reference tissue model (SRTM2), multilinear reference tissue model (MRTM2), and reference Logan graphical analysis (rLGA) were used to generate binding potential maps using SO as reference tissue (BP_SO). Shorter dynamic acquisitions down to 50 min were also considered. In addition, standard uptake value ratios (SUVR) relative to SO were evaluated for three post-injection intervals (SUVR_SO,40-70min, SUVR_SO,50-80min, and SUVR_SO,60-90min respectively). Regional parametric BP_SO + 1 and SUVR_SO were compared with regional distribution volume ratios of a 1-tissue compartment model (1TCM DVR_SO) using Spearman correlation and Bland-Altman analysis.

RESULTS

For all methods, highly significant correlations were found between regional, parametric BP_SO + 1 (r = [0.63;0.96]) or SUVR_SO (r = [0.90;0.91]) estimates and regional 1TCM DVR_SO. For a 90-min dynamic scan, parametric SRTM2 and MRTM2 values presented similar small bias and variability (- 3.0 ± 2.9 % for baseline SRTM2) and outperformed rLGA (- 10.0 ± 5.3 % for baseline rLGA). Reducing the dynamic acquisition to 60 min had limited impact on the bias and variability of parametric SRTM2 BP_SO estimates (- 1.0 ± 9.9 % for baseline SRTM2) while a higher variability (- 1.83 ± 10.8 %) for baseline MRTM2 was observed for shorter acquisition times. Both SUVR_SO,60-90min and SUVR_SO,50-80min showed similar small bias and variability (- 2.8 ± 4.6 % bias for baseline SUVR_SO,60-90min).

CONCLUSION

SRTM2 is the preferred method for a voxelwise analysis of dynamic [¹¹C]UCB-J PET using SO as reference tissue, while reducing the dynamic acquisition to 60 min has limited impact on [¹¹C]UCB-J BP_SO parametric maps. For a static PET protocol, both SUVR_SO,60-90min and SUVR_SO,50-80min images are an excellent proxy for [¹¹C]UCB-J BP_SO parametric maps.

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition

Objectives

To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue.

Materials and methods

Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization.

Results

The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones.

Conclusion

An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.

Electronic supplementary material

The online version of this article (10.1007/s10334-020-00871-3) contains supplementary material, which is available to authorized users.

A new method to quantify tau pathologies with (11)C-PBB3 PET using reference tissue voxels extracted from brain cortical gray matter

Background

Quantitative in vivo imaging of tau pathologies potentially improves diagnostic accuracy of neurodegenerative tauopathies and would facilitate evaluation of disease-modifying drugs targeting tau lesions in these diseases. Tau pathology can be quantified by reference tissue models without arterial blood sampling when reference tissue devoid of target binding sites is available. The cerebellar cortex has been used as a reference region in analyses of tau positron emission tomography (PET) data in Alzheimer’s disease (AD). However, in a significant subset of tauopathies such as progressive supranuclear palsy and corticobasal degeneration, tau accumulation may occur in diverse brain regions including the cerebellar cortex. This hampers selection of a distinctive reference region lacking binding sites for a tau PET ligand. The purpose of this study was to develop a new method to quantify specific binding of a PET radioligand, ¹¹C-PBB3, to tau deposits using reference voxels extracted from cortical gray matter, which have a low likelihood of containing tau accumulations.

Methods

We reanalyzed ¹¹C-PBB3 PET data of seven mild AD patients (ADs) and seven elderly healthy control subjects (HCs) acquired in a previous study. As a standard method, parametric images of binding potential (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {BP}_{\mathsf{ND}}^{\ast } $$\end{document}BPND∗) were initially generated using reference tissue manually defined on the cerebellar cortex. We then constructed a frequency histogram of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {BP}_{\mathsf{ND}}^{\ast } $$\end{document}BPND∗ values in these parametric images and selected cortical gray matter voxels contained in a certain range of the histogram with a low likelihood of having ¹¹C-PBB3 binding sites. Finally, these reference voxels were used for generating new \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {BP}_{\mathsf{ND}}^{\ast } $$\end{document}BPND∗ parametric images.

Results

Reference tissue voxels defined by the histogram analysis spread throughout the cortical gray matter of AD and HC brains. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {BP}_{\mathsf{ND}}^{\ast } $$\end{document}BPND∗ values determined by our new method correlated very well with those estimated by the standard method (r² = 0.94), although the binding estimates by the current method were slightly higher by ~0.14 than those by the standard method.

Conclusions

We developed and validated a new method enabling quantification of tau lesions that can accumulate in the cerebellum and other extensive brain areas. This method may be applicable to all tauopathy subtypes and various tau PET ligands besides ¹¹C-PBB3.

Trial registration

The number is UMIN000009052

Effect of T2* correction on contrast kinetic model analysis using a reference tissue arterial input function at 7 T

OBJECTIVES

We aimed to investigate the effect of T2* correction on estimation of kinetic parameters from T1-weighted dynamic contrast enhanced (DCE) MRI data when a reference-tissue arterial input function (AIF) is used.

MATERIALS AND METHODS

DCE-MRI data were acquired from seven mice with 4T1 mouse mammary tumors using a double gradient echo sequence at 7 T. The AIF was estimated from a region of interest in the muscle. The extended Tofts model was used to estimate pharmacokinetic parameters in the enhancing part of the tumor, with and without T2* correction of the lesion and AIF. The parameters estimated with T2* correction of both the AIF and lesion time-intensity curve were assumed to be the reference standard.

RESULTS

For the whole population, there was significant difference (p < 0.05) in transfer constant (K(trans)) between T2* corrected and not corrected methods, but not in interstitial volume fraction (ve). Individually, no significant differences were found in K(trans) and ve of four and six tumors, respectively, between the T2* corrected and not corrected methods. In contrast, K(trans) was significantly underestimated, if the T2* correction was not used, in other tumors for which the median K(trans) was larger than 0.4 min(-1).

CONCLUSION

T2* effect on tumors with high K(trans) may not be negligible in kinetic model analysis, even if AIF is estimated from reference tissue where the concentration of contrast agent is relatively low.