Francesca Zanderigo

A new method for assessing PET-MRI coregistration

Positron emission tomography (PET) images are acquired for many purposes, from diagnostic assessment to aiding in the development of novel therapies. Whatever the intended use, it is often necessary to distinguish between different anatomical regions within these images. Because of this, magnetic resonance images (MRIs) are generally acquired to provide an anatomical reference. This reference will only be accurate if the PET image is properly coregistered to the MRI; yet currently, a method to evaluate PET-MRI coregistration accuracy does not exist. This problem is compounded by the fact that two visually indistinguishable coregistration results can produce estimates of ligand binding that vary significantly. Therefore, the focus of this work was to develop a method that can evaluate coregistration performance based on measured ligand binding within certain regions of the coregistered PET image. The evaluation method is based on the premise that a more accurate coregistration will result in higher ligand binding in certain anatomical regions defined by the MRI. This fully automated method was able to assess coregistration results within the variance of an expert manual rater and shows promise as a possible coregistration cost function.

Pattern Recognition Of Magnetic Resonance Imaging-Based Gray Matter Volume Measurements Classifies Bipolar Disorder And Major Depressive Disorder

BACKGROUND Bipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD. METHODS This study extends our previous work by performing individual level classification of BD or MDD in an expanded, currently unmedicated, cohort using gray matter volume (GMV) based on Magnetic Resonance Imaging and a Support Vector Machine. All patients were in a Major Depressive Episode and a leave-two-out analysis was performed. RESULTS Nineteen out of 26 BD subjects and 20 out of 26 MDD subjects were correctly identified, for a combined accuracy of 75%. The three brain regions contributing to the classification were higher GMV in bilateral supramarginal gyrus and occipital cortex indicating MDD, and higher GMV in right dorsolateral prefrontal cortex indicating BD. LIMITATIONS This analysis included scans performed with two different headcoils and scan sequences, which limited the interpretability of results in an independent cohort analysis. CONCLUSIONS Our results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.

Noninvasive Blood-Free Full Quantification of Positron Emission Tomography Radioligand Binding

Full quantification of a positron emission tomography (PET) radioligand binding to its target is preferred because it requires the fewest assumptions, but generally involves measuring the concentration of free radioligand in the arterial plasma by collecting blood samples from the subjects radial artery during the scan, and performing metabolite analysis. This invasive, costly procedure deters subjects’ participation, and requires specialized staff and equipment. Simultaneous estimation (SIME) can fully quantify binding using only PET data from multiple brain regions and one individual anchor value, which is based on a single arterial blood sample. Drawing this sample can still be challenging in clinical settings, particularly when using simultaneous PET/magnetic resonance scanners. Here we propose a methodology for full quantification of binding that does not require any blood samples. The methodology substitutes the SIME blood-based anchor with a value predicted using multiple linear regression of noninvasive, easy-to-collect variables related to the radioligand blood concentration, and individual metabolism, such as injected dose, body mass index, or body surface area. As a study case, we show here the methodology in comparison to analysis with full arterial-line blood sampling in a cohort of 23 available scans with [11C]CUMI-101, a partial agonist of the serotonin 5-HT1A receptors.

Longitudinal effects of cognitive behavioral therapy for depression on the neural correlates of emotion regulation

Cognitive behavioral therapy (CBT) is effective for a substantial minority of patients suffering from major depressive disorder (MDD), but its mechanism of action at the neural level is not known. As core techniques of CBT seek to enhance emotion regulation, we scanned 31 MDD participants prior to 14 sessions of CBT using functional magnetic resonance imaging (fMRI) and a task in which participants engaged in a voluntary emotion regulation strategy while recalling negative autobiographical memories. Eighteen healthy controls were also scanned. Twenty-three MDD participants completed post-treatment fMRI scanning, and 12 healthy volunteers completed repeat scanning without intervention. Better treatment outcome was associated with longitudinal enhancement of the emotion regulation-dependent BOLD contrast within subgenual anterior cingulate, medial prefrontal cortex, and lingual gyrus. Baseline emotion regulation-dependent BOLD contrast did not predict treatment outcome or differ between MDD and control groups. CBT response may be mediated by enhanced downregulation of neural activity during emotion regulation; brain regions identified overlap with those found using a similar task in a normative sample, and include regions related to self-referential and emotion processing. Future studies should seek to determine specificity of this downregulation to CBT, and evaluate it as a treatment target in MDD.

Lack of association between the serotonin transporter and serotonin 1A receptor: an in vivo PET imaging study in healthy adults.

The serotonin neurotransmitter system is modulated in part by the uptake of synaptically released serotonin (5-HT) by the serotonin transporter (5-HTT), and by specific serotonin autoreceptors such as the somatodendritic 5-HT1A receptor, which can limit serotonin neuron depolarization. However, little is known about how 5-HTT and 5-HT1A are related in vivo. To study this question, we reanalyzed positron emission tomography (PET) data obtained earlier in 40 healthy participants (21 females) using [(11)C]WAY-100635 for quantification of 5-HT1A binding and [(11)C](+)-McN-5652 for quantification of 5-HTT binding. We hypothesized negative correlations between 5-HT1A binding in the raphe nuclei (RN) and 5-HTT binding in RN terminal field regions. Controlling for sex, no significant correlations were found (all p>0.05). Similarly, an exploratory analysis correlating whole-brain voxel-wise 5-HTT binding with 5-HT1A binding in RN identified no significant clusters meeting our a priori statistical threshold. The lack of correlation between 5-HT1A and 5-HTT binding observed in the current study may be due to the different temporal responsiveness of regulatory processes controlling the somatodendritic 5-HT1A receptor and 5-HTT in response to changing availability of intrasynaptic serotonin.

Model-Free Quantification of Dynamic PET Data Using Nonparametric Deconvolution

Dynamic positron emission tomography (PET) data are usually quantified using compartment models (CMs) or derived graphical approaches. Often, however, CMs either do not properly describe the tracer kinetics, or are not identifiable, leading to nonphysiologic estimates of the tracer binding. The PET data are modeled as the convolution of the metabolite-corrected input function and the tracer impulse response function (IRF) in the tissue. Using nonparametric deconvolution methods, it is possible to obtain model-free estimates of the IRF, from which functionals related to tracer volume of distribution and binding may be computed, but this approach has rarely been applied in PET. Here, we apply nonparametric deconvolution using singular value decomposition to simulated and test–retest clinical PET data with four reversible tracers well characterized by CMs ([11C]CUMI-101, [11C]DASB, [11C]PE2I, and [11C]WAY-100635), and systematically compare reproducibility, reliability, and identifiability of various IRF-derived functionals with that of traditional CMs outcomes. Results show that nonparametric deconvolution, completely free of any model assumptions, allows for estimates of tracer volume of distribution and binding that are very close to the estimates obtained with CMs and, in some cases, show better test–retest performance than CMs outcomes.

Toward Noninvasive Quantification of Brain Radioligand Binding by Combining Electronic Health Records and Dynamic PET Imaging Data

Quantitative analysis of positron emission tomography (PET) brain imaging data requires a metabolite-corrected arterial input function (AIF) for estimation of distribution volume and related outcome measures. Collecting arterial blood samples adds risk, cost, measurement error, and patient discomfort to PET studies. Minimally invasive AIF estimation is possible with simultaneous estimation (SIME), but at least one arterial blood sample is necessary. In this study, we describe a noninvasive SIME (nSIME) approach that utilizes a pharmacokinetic input function model and constraints derived from machine learning applied to an electronic health record database consisting of “long tail” data (digital records, paper charts, and handwritten notes) that were collected ancillary to the PET studies. We evaluated the performance of nSIME on 95 [11C]DASB PET scans that had measured AIFs. The results indicate that nSIME is a promising alternative to invasive AIF measurement. The general framework presented here may be expanded to other metabolized radioligands, potentially enabling quantitative analysis of PET studies without blood sampling. A glossary of technical abbreviations is provided at the end of this paper.

Non-invasive estimation of [ 11 C]PBR28 binding potential

ABSTRACT [11C]PBR28 is a PET radioligand used to estimate densities of the 18 kDa translocator protein (TSPO) in vivo. Since there is no suitable reference region, arterial blood samples are required for full quantification. Here, we evaluate a methodology for full quantification of [11C]PBR28 PET data that does not require either a reference region or blood samples. Simultaneous estimation (SIME) uses time‐activity curves from several brain regions to estimate binding potential (BPND), a theoretically more sensitive outcome measure than total distribution volume. SIME can be employed with either a measured arterial input function (AIF) or a template input function (tIF) that has similar shape as the AIF, but with arbitrary amplitude. We evaluated the ability of SIME to detect group differences in TSPO densities using PET and arterial plasma data from 21 Alzheimers disease (AD) patients and 15 controls that underwent [11C]PBR28 imaging. Regional BPND obtained with tIFs were compared to those obtained using measured AIFs. Standard kinetic modeling was also employed for comparison. The sensitivity of each method to detect group differences in TSPO densities were assessed by comparing estimated effect sizes between AD patients and controls. For this purpose, BPND estimated for one region with high pathological burden (inferior temporal cortex), and for one region with low pathological burden (cerebellum) was used. BPND estimates obtained with SIME and tIFs were close to identical to those obtained with AIF (3.0 ± 21% difference, r2 = 0.78). In this dataset, the effect sizes between AD patients and controls for both SIME with AIF and SIME with tIF were similar (30.3%, p = 0.001 and 31.0%, p = 0.004, respectively) and were each greater than the effect size observed using the two‐tissue compartment model (16.1%, p = 0.12). None of the tested methods showed difference in TSPO binding in cerebellum. These results demonstrate that BPND can be estimated for [11C]PBR28 using SIME, and may be useful in clinical studies. In addition, arterial sampling may not be necessary if tIFs can be reliably estimated.

Estimation of the binding potential BPND without a reference region or blood samples for brain PET studies

Abstract Binding potential (BPND) is a commonly used PET outcome measure because it can be estimated without blood sampling if a brain reference region (RR) devoid of the target of interest exists. For many radioligands, however, no RR exists, and the total distribution volume (VT), whose estimation requires arterial blood sampling, is normally considered as the outcome measure. Here, we present a method that allows calculation of BPND without requiring either blood samples or a RR. The method extends our previous algorithm for estimating non‐displaceable distribution volumes (VND) without using a RR. Here we show that if a template input function, with arbitrary amplitude but a shape similar to the actual arterial input function, is used in the algorithm, estimation of VT and VND are both proportionally biased, and thus this bias cancels out in the estimation of BPND. The method is evaluated using simulated data, human data acquired with the serotonin 1A receptor radioligand [11C]WAY‐100635, and blocking data acquired in baboons using the serotonin 1A receptor radioligand [11C]CUMI‐101. We evaluated two versions of template input functions: an arbitrarily downscaled version of the actual arterial input function, and an unscaled population‐based input function. In addition, we evaluated how shape modifications of the template input function impact the estimates of BPND. With the downscaled input function, BPND values close to the gold standard were obtained. When the unscaled population‐based based input function was used, greater variability was observed but no discernable bias was introduced. When the input function shape was modified, a systematic but small bias in BPND was introduced. We conclude that, provided the shape of the arterial input function is adequately described, determination of its amplitude is not necessary for estimation of BPND.

Brain tissue selection procedures for image derived input functions derived using independent components analysis

Absolute quantification of positron emission tomography (PET) data requires invasive blood sampling in order to obtain the arterial input function (AIF). This procedure involves considerable costs and risks. A less invasive approach is to estimate the AIF directly from images, known as an image derived input function (IDIF). One promising method, EPICA, extracts IDIF by applying independent components analysis (ICA) on dynamic PET data from the entire brain. EPICA requires exclusion of non-brain voxels from the PET images, which is achieved by using a brain mask prior to ICA. Including the entire brain in the mask may degrade the performance of ICA due to noise, artifacts and confounding information. We applied EPICA to 3 [18F]FDG and 3 [11C]WAY data sets and investigated if altering the brain mask by including or excluding tissue structures improves EPICA performance. EPICA applied to whole brain data yields poor performance but with the appropriate brain mask IDIF curves approximate the AIF well. Different tissue structures are important for different radiotracers suggesting that the kinetics of the radiotracer and its diffusion characteristics in the brain influence IDIF estimation with ICA.