Comparison of different methods of spatial normalization of FDG-PET brain images in the voxel-wise analysis of MCI patients and controls

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification

INTRODUCTION

Spatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images.

METHODS

We propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR).

RESULTS

In total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05).

CONCLUSION

The automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

Assessment of a one-week ketogenic diet on brain glycolytic metabolism and on the status epilepticus stage of a lithium-pilocarpine rat model

The ketogenic diet (KD) has been shown to be effective in refractory epilepsy after long-term administration. However, its interference with short-term brain metabolism and its involvement in the early process leading to epilepsy remain poorly understood. This study aimed to assess the effect of a short-term ketogenic diet on cerebral glucose metabolic changes, before and after status epilepticus (SE) in rats, by using [18F]-FDG PET. Thirty-nine rats were subjected to a one-week KD (KD-rats, n = 24) or to a standard diet (SD-rats, n = 15) before the induction of a status epilepticus (SE) by lithium-pilocarpine administrations. Brain [18F]-FDG PET scans were performed before and 4 h after this induction. Morphological MRIs were acquired and used to spatially normalize the PET images which were then analyzed voxel-wisely using a statistical parametric-based method. Twenty-six rats were analyzed (KD-rats, n = 15; SD-rats, n = 11). The 7 days of the KD were associated with significant increases in the plasma β-hydroxybutyrate level, but with an unchanged glycemia. The PET images, recorded after the KD and before SE induction, showed an increased metabolism within sites involved in the appetitive behaviors: hypothalamic areas and periaqueductal gray, whereas no area of decreased metabolism was observed. At the 4th hour following the SE induction, large metabolism increases were observed in the KD- and SD-rats in areas known to be involved in the epileptogenesis process late-i.e., the hippocampus, parahippocampic, thalamic and hypothalamic areas, the periaqueductal gray, and the limbic structures (and in the motor cortex for the KD-rats only). However, no statistically significant difference was observed when comparing SD and KD groups at the 4th hour following the SE induction. A one-week ketogenic diet does not prevent the status epilepticus (SE) and associated metabolic brain abnormalities in the lithium-pilocarpine rat model. Further explorations are needed to determine whether a significant prevention could be achieved by more prolonged ketogenic diets and by testing this diet in less severe experimental models, and moreover, to analyze the diet effects on the later and chronic stages leading to epileptogenesis.

Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas

PURPOSE

A unique advantage of the brain positron emission tomography (PET) imaging is the ability to image different biological processes with different radiotracers. However, the diversity of the brain PET image patterns also makes their spatial normalization challenging. Since structural MR images are not always available in the clinical practice, this study proposed a PET-only spatial normalization method based on adaptive probabilistic brain atlas.

METHODS

The proposed method (atlas-based method) consists of two parts, an adaptive probabilistic brain atlas generation algorithm, and a probabilistic framework for registering PET image to the generated atlas. To validate this method, the results of MRI-based method and template-based method (a widely used PET-only method) were treated as the gold standard and control, respectively. A total of 286 brain PET images, including seven radiotracers (FDG, PIB, FBB, AV-45, AV-1451, AV-133, [¹⁸F]altanserin) and four groups of subjects (Alzheimer disease, Parkinson disease, frontotemporal dementia, and healthy control), were spatially normalized using the three methods. The results were then quantitatively compared by using correlation analysis, meta region of interest (meta-ROI) standardized uptake value ratio (SUVR) analysis, and statistical parametric mapping (SPM) analysis.

RESULTS

The Pearson correlation coefficient between the images computed by atlas-based method and the gold standard was 0.908 ± 0.005. The relative error of meta-ROI SUVR computed by atlas-based method was 2.12 ± 0.18%. Compared with template-based method, atlas-based method was also more consistent with the gold standard in SPM analysis.

CONCLUSION

The proposed method provides a unified approach to spatially normalize brain PET images of different radiotracers accurately without MR images. A free MATLAB toolbox for this method has been provided.

mGluR 5 and GABA A receptor‐specific parametric PET atlas construction— PET / MR data processing pipeline, validation, and application

The glutamate and γ‐aminobutyric acid neuroreceptor subtypes mGluR₅ and GABA_A are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non‐displaceable binding potential (BP_ND), and total distribution volume (V_T) based on simultaneously acquired bolus‐infusion positron emission tomography (PET) and MR data. The pipeline was applied to [¹¹C]ABP688‐PET/MR (13 healthy male non‐smokers, 26.6 ± 7.0 years) and [¹¹C]Flumazenil‐PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as V_T and BP_ND atlases of mGluR₅ and GABA_A, were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δ_ABP = 3.0 ± 1.0% and δ_FMZ = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σ_ABP = 4.0%–16.0%, σ_FMZ = 3.9%–9.5%). An evaluation of PET‐to‐PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [¹⁵O]H₂O‐template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions.

Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease

A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of Alzheimer's disease (AD) using brain structural magnetic resonance imaging (MRI) data. However, the data available are limited in the case of this specific disease. Training a DL model with a large number of feature parameters on a small dataset of MRI scans will likely lead to overfitting. Overfitting reduces the generality and efficiency of the model. In this study, we show that a traditional nonlinear transformation from native space to template space, as a preprocessing stage, is effective in reducing overfitting through the reduction of spatial variations in the input data. To evaluate this effectiveness, we compare two different pre-processing approaches for DL-based AD classification task: (1) affine registration and (2) nonlinear diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL). The results show that the accuracy of the nonlinear registration based approach is much higher than the affine registration based approach. Furthermore, from the classification results obtained with noisy images, DARTEL is less susceptible to noise than affine registration. In summary, our experimental results suggest that nonlinear transformation is a preferable preprocessing step for training DL-based AD classification models on limited size datasets.

Amyloid burden quantification depends on PET and MR image processing methodology

Quantification of amyloid load with positron emission tomography can be useful to assess Alzheimer's Disease in-vivo. However, quantification can be affected by the image processing methodology applied. This study's goal was to address how amyloid quantification is influenced by different semi-automatic image processing pipelines. Images were analysed in their Native Space and Standard Space; non-rigid spatial transformation methods based on maximum a posteriori approaches and tissue probability maps (TPM) for regularisation were explored. Furthermore, grey matter tissue segmentations were defined before and after spatial normalisation, and also using a population-based template. Five quantification metrics were analysed: two intensity-based, two volumetric-based, and one multi-parametric feature. Intensity-related metrics were not substantially affected by spatial normalisation and did not significantly depend on the grey matter segmentation method, with an impact similar to that expected from test-retest studies (≤10%). Yet, volumetric and multi-parametric features were sensitive to the image processing methodology, with an overall variability up to 45%. Therefore, the analysis should be carried out in Native Space avoiding non-rigid spatial transformations. For analyses in Standard Space, spatial normalisation regularised by TPM is preferred. Volumetric-based measurements should be done in Native Space, while intensity-based metrics are more robust against differences in image processing pipelines.

Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer's Disease

BACKGROUND

Several automatic tools have been implemented for semi-quantitative assessment of brain [18]F-FDG-PET.

OBJECTIVE

We aimed to head-to-head compare the diagnostic performance among three statistical parametric mapping (SPM)-based approaches, another voxel-based tool (i.e., PALZ), and a volumetric region of interest (VROI-SVM)-based approach, in distinguishing patients with prodromal Alzheimer's disease (pAD) from controls.

METHODS

Sixty-two pAD patients (MMSE score = 27.0±1.6) and one hundred-nine healthy subjects (CTR) (MMSE score = 29.2±1.2) were enrolled in five centers of the European Alzheimer's Disease Consortium. The three SPM-based methods, based on different rationales, included 1) a cluster identified through the correlation analysis between [18]F-FDG-PET and a verbal memory test (VROI-1), 2) a VROI derived from the comparison between pAD and CTR (VROI-2), and 3) visual analysis of individual maps obtained by the comparison between each subject and CTR (SPM-Maps). The VROI-SVM approach was based on 6 VROI plus 6 VROI asymmetry values derived from the pAD versus CTR comparison thanks to support vector machine (SVM).

RESULTS

The areas under the ROC curves between pAD and CTR were 0.84 for VROI-1, 0.83 for VROI-2, 0.79 for SPM maps, 0.87 for PALZ, and 0.95 for VROI-SVM. Pairwise comparisons of Youden index did not show statistically significant differences in diagnostic performance between VROI-1, VROI-2, SPM-Maps, and PALZ score whereas VROI-SVM performed significantly (p < 0.005) better than any of the other methods.

CONCLUSION

The study confirms the good accuracy of [18]F-FDG-PET in discriminating healthy subjects from pAD and highlights that a non-linear, automatic VROI classifier based on SVM performs better than the voxel-based methods.

Customized FreeSurfer-based brain atlas for diffeomorphic anatomical registration through exponentiated lie algebra tool

OBJECTIVE

Digital brain template and atlas designed for a specific group provide advantages for the analysis and interpretation of neuroimaging data, but require a significant workload for development. We developed a simple method to create customized brain atlas for diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) tool using FreeSurfer-generated volume-of-interest (FSVOI) images and validated.

METHODS

¹⁸F-florbetaben positron emission tomography (PET) and magnetic resonance (MR) imaging data were obtained from 248 participants of Alzheimer's disease spectrum (from cognitively normal to Alzheimer's disease dementia). To create a customized atlas, MR images of 84 amyloid-negative controls were first processed with FreeSurfer to obtain individual FSVOI and with DARTEL tool to create DARTEL template. Individual FSVOI images were spatially normalized, and each voxel was then labelled with a VOI label with maximum probability. Using these template and atlas, all images were normalized, and the regional standardized uptake value ratios (SUVR) were measured.

RESULTS

¹⁸F-florbetaben SUVR values measured with customized atlas showed excellent one-to-one correlation with SUVR measured with individual FSVOI in all regions, and thereby showed almost identical between-group comparison results and outperformed the classic methods.

CONCLUSIONS

Customized FreeSurfer-based brain atlas for DARTEL tool is easy to create and useful for the analysis of PET and MR images with high adaptability and reliability for broad research purposes.

Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package

BACKGROUND

Spatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI - including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms - to evaluate their performance in the presence of MS-related pathologies.

METHODS

3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests.

RESULTS

All four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms.

CONCLUSIONS

SPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.

Controls-based denoising, a new approach for medical image analysis, improves prediction of conversion to Alzheimer's disease with FDG-PET

OBJECTIVE

The pattern expression score (PES), i.e., the degree to which a pathology-related pattern is present, is frequently used in FDG-brain-PET analysis and has been shown to be a powerful predictor of conversion to Alzheimer's disease (AD) in mild cognitive impairment (MCI). Since, inevitably, the PES is affected by non-pathological variability, our aim was to improve classification with the simple, yet novel approach to identify patterns of non-pathological variance in a separate control sample using principal component analysis and removing them from patient data (controls-based denoising, CODE) before calculating the PES.

METHODS

Multi-center FDG-PET from 220 MCI patients (64 non-converter, follow-up ≥ 4 years; 156 AD converter, time-to-conversion ≤ 4 years) were obtained from the ADNI database. Patterns of non-pathological variance were determined from 262 healthy controls. An AD pattern was calculated from AD patients and controls. We predicted AD conversion based on PES only and on PES combined with neuropsychological features and ApoE4 genotype. We compared classification performance achieved with and without CODE and with a standard machine learning approach (support vector machine).

RESULTS

Our model predicts that CODE improves the signal-to-noise ratio of AD-PES by a factor of 1.5. PES-based prediction of AD conversion improved from AUC 0.80 to 0.88 (p= 0.001, DeLong's method), sensitivity 69 to 83%, specificity 81% to 88% and Matthews correlation coefficient (MCC) 0.45 to 0.66. Best classification (0.93 AUC) was obtained when combining the denoised PES with clinical features.

CONCLUSIONS

CODE, applied in its basic form, significantly improved prediction of conversion based on PES. The achieved classification performance was higher than with a standard machine learning algorithm, which was trained on patients, explainable by the fact that CODE used additional information (large sample of healthy controls). We conclude that the proposed, novel method is a powerful tool for improving medical image analysis that offers a wide spectrum of biomedical applications, even beyond image analysis.

Targeted Gold Nanoparticle⁻Oligonucleotide Contrast Agents in Combination with a New Local Voxel-Wise MRI Analysis Algorithm for In Vitro Imaging of Triple-Negative Breast Cancer

Gold nanoparticles (GNPs) have tremendous potential as cancer-targeted contrast agents for diagnostic imaging. The ability to modify the particle surface with both disease-targeting molecules (such as the cancer-specific aptamer AS1411) and contrast agents (such as the gadolinium chelate Gd(III)-DO3A-SH) enables tailoring the particles for specific cancer-imaging and diagnosis. While the amount of image contrast generated by nanoparticle contrast agents is often low, it can be augmented with the assistance of computer image analysis algorithms. In this work, the ability of cancer-targeted gold nanoparticle–oligonucleotide conjugates to distinguish between malignant (MDA-MB-231) and healthy cells (MCF-10A) is tested using a T1-weighted image analysis algorithm based on three-dimensional, deformable model-based segmentation to extract the Volume of Interest (VOI). The gold nanoparticle/algorithm tandem was tested using contrast agent GNP-Gd(III)-DO3A-SH-AS1411) and nontargeted c-rich oligonucleotide (CRO) analogs and control (CTR) counterparts (GNP-Gd(III)-DO3A-SH-CRO/CTR) via in vitro studies. Remarkably, the cancer cells were notably distinguished from the nonmalignant cells, especially at nanomolar contrast agent concentrations. The T1-weighted image analysis algorithm provided similar results to the industry standard Varian software interface (VNMRJ) analysis of T1 maps at micromolar contrast agent concentrations, in which the VNMRJ produced a 19.5% better MRI contrast enhancement. However, our algorithm provided more sensitive and consistent results at nanomolar contrast agent concentrations, where our algorithm produced ~500% better MRI contrast enhancement.

A Spatial Registration Toolbox for Structural MR Imaging of the Aging Brain

During aging the brain undergoes a series of structural changes, in size, shape as well as tissue composition. In particular, cortical atrophy and ventricular enlargement are often present in the brain of elderly individuals. This poses serious challenges in the spatial registration of structural MR images. In this study, we addressed this open issue by proposing an enhanced framework for MR registration and segmentation. Our solution was compared with other approaches based on the tools available in SPM12, a widely used software package. Performance of the different methods was assessed on 229 T1-weighted images collected in healthy individuals, with age ranging between 55 and 90 years old. Our method showed a consistent improvement as compared to other solutions, especially for subjects with enlarged lateral ventricles. It also provided a superior inter-subject alignment in cortical regions, with the most marked improvement in the frontal lobe. We conclude that our method is a valid alternative to standard approaches based on SPM12, and is particularly suitable for the processing of structural MR images of brains with cortical atrophy and ventricular enlargement. The method is integrated in our software toolbox MRTool, which is freely available to the scientific community.

Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?

Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional neural networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analyzing nuclear brain imaging? Are spatial and intensity normalizations still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessings. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization - and its type - is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted.

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning

Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.

Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment

BACKGROUND AND OBJECTIVE

The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible disease abnormality identification, intervention and even possible prevention.

METHODS

The current study puts forward a multi-modal supervised within-class-similarity discriminative dictionary learning algorithm (SCDDL) we introduced previously for distinguishing MCI from NC. The proposed new algorithm was based on weighted combination and named as multi-modality SCDDL (mSCDDL). Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir PET data of 113 AD patients, 110 MCI patients and 117 NC subjects from the Alzheimer's disease Neuroimaging Initiative database were adopted for classification between MCI and NC, as well as between AD and NC.

RESULTS

Adopting mSCDDL, the classification accuracy achieved 98.5% for AD vs. NC and 82.8% for MCI vs. NC, which were superior to or comparable with the results of some other state-of-the-art approaches as reported in recent multi-modality publications.

CONCLUSIONS

The mSCDDL procedure was a promising tool in assisting early diseases diagnosis using neuroimaging data.

Voxel-based logistic analysis of PPMI control and Parkinson's disease DaTscans

A comprehensive analysis of the Parkinson's Progression Markers Initiative (PPMI) Dopamine Transporter Single Photon Emission Computed Tomography (DaTscan) images is carried out using a voxel-based logistic lasso model. The model reveals that sub-regional voxels in the caudate, the putamen, as well as in the globus pallidus are informative for classifying images into control and PD classes. Further, a new technique called logistic component analysis is developed. This technique reveals that intra-population differences in dopamine transporter concentration and imperfect normalization are significant factors influencing logistic analysis. The interactions with handedness, sex, and age are also evaluated.

Quantitative SPM Analysis Involving an Adaptive Template May Be Easily Applied to [18F]FDG PET Images of the Rat Brain

PURPOSE

The Statistical Parametric Mapping (SPM) software is frequently used for the quantitative analysis of patients' brain images obtained from 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography ([¹⁸F]FDG PET). However, its adaptation to small animals is difficult, particularly for the initial step of spatial normalization which requires a specific brain anatomical template. This study was aimed at determining whether SPM analysis can be applied to rat, and more specifically to the lithium-pilocarpine model of epilepsy, by using an adaptive template. This template developed for PET clinical imaging is constructed from a block matching algorithm.

PROCEDURES

SPM analysis of brain [¹⁸F]FDG PET images from Sprague-Dawley rats was used with the block matching (BM) adaptive template for the detection of brain abnormalities (1) artificially inserted within the initially normal brain images of 10 rats (50 % decrease in signal intensity within 40 spheres of 0.5 to 1.0 mm in diameter) and (2) occurring at 4 h (n = 16), 48 h (n = 15), and 8 days (n = 13) after lithium-pilocarpine treatment.

RESULTS

Concordant positive clusters were documented for all inserted abnormalities, whereas no aberrant clusters were documented in remote brain areas. Positive clusters were also detected on sites known to be involved in the epileptogenesis process of the lithium-pilocarpine model (piriform and entorhinal cortex, hippocampus), with the expected time-specific changes involving an early hypermetabolism followed by a severe hypometabolism and a subsequent partial recovery.

CONCLUSION

A quantitative SPM analysis of brain [¹⁸F]FDG PET images may be applied to the monitoring of rat brain function when using an adaptive BM template.

[Comparison of Arterial Spin-labeling Perfusion Images at Different Spatial Normalization Methods Based on Voxel-based Statistical Analysis]

PURPOSE

Spatial normalization is a significant image pre-processing operation in statistical parametric mapping (SPM) analysis. The purpose of this study was to clarify the optimal method of spatial normalization for improving diagnostic accuracy in SPM analysis of arterial spin-labeling (ASL) perfusion images.

METHODS

We evaluated the SPM results of five spatial normalization methods obtained by comparing patients with Alzheimer's disease or normal pressure hydrocephalus complicated with dementia and cognitively healthy subjects. We used the following methods: 3DT1-conventional based on spatial normalization using anatomical images; 3DT1-DARTEL based on spatial normalization with DARTEL using anatomical images; 3DT1-conventional template and 3DT1-DARTEL template, created by averaging cognitively healthy subjects spatially normalized using the above methods; and ASL-DARTEL template created by averaging cognitively healthy subjects spatially normalized with DARTEL using ASL images only.

RESULTS

Our results showed that ASL-DARTEL template was small compared with the other two templates. Our SPM results obtained with ASL-DARTEL template method were inaccurate. Also, there were no significant differences between 3DT1-conventional and 3DT1-DARTEL template methods. In contrast, the 3DT1-DARTEL method showed higher detection sensitivity, and precise anatomical location.

CONCLUSIONS

Our SPM results suggest that we should perform spatial normalization with DARTEL using anatomical images.

PETPVE12: an SPM toolbox for Partial Volume Effects correction in brain PET - Application to amyloid imaging with AV45-PET

Positron emission tomography (PET) allows detecting molecular brain changes in vivo. However, the accuracy of PET is limited by partial volume effects (PVE) that affects quantitative analysis and visual interpretation of the images. Although PVE-correction methods have been shown to effectively increase the correspondence of the measured signal with the true regional tracer uptake, these procedures are still not commonly applied, neither in clinical nor in research settings. Here, we present an implementation of well validated PVE-correction procedures as a SPM toolbox, PETPVE12, for automated processing. We demonstrate its utility by a comprehensive analysis of the effects of PVE-correction on amyloid-sensitive AV45-PET data from 85 patients with Alzheimer's disease (AD) and 179 cognitively normal (CN) elderly. Effects of PVE-correction on global cortical standard uptake value ratios (SUVR) and the power of diagnostic group separation were assessed for the region-wise geometric transfer matrix method (PVEc-GTM), as well as for the 3-compartmental voxel-wise "Müller-Gärtner" method (PVEc-MG). Both PVE-correction methods resulted in decreased global cortical SUVRs in the low to middle range of SUVR values, and in increased global cortical SUVRs at the high values. As a consequence, average SUVR of the CN group was reduced, whereas average SUVR of the AD group was increased by PVE-correction. These effects were also reflected in increased accuracies of group discrimination after PVEc-GTM (AUC=0.86) and PVEc-MG (AUC=0.89) compared to standard non-corrected SUVR (AUC=0.84). Voxel-wise analyses of PVEc-MG corrected data also demonstrated improved detection of regionally increased AV45 SUVR values in AD patients. These findings complement the growing evidence for a beneficial effect of PVE-correction in quantitative analysis of amyloid-sensitive PET data. The novel PETPVE12 toolbox significantly facilitates the application of PVE-correction, particularly within SPM-based processing pipelines. This is expected to foster the use of PVE-correction in brain PET for more widespread use. The toolbox is freely available at http://www.fil.ion.ucl.ac.uk/spm/ext/#PETPVE12.

Optimization of Statistical Single Subject Analysis of Brain FDG PET for the Prognosis of Mild Cognitive Impairment-to-Alzheimer's Disease Conversion

BACKGROUND

Positron emission tomography (PET) with the glucose analog F-18-fluorodeoxyglucose (FDG) is widely used in the diagnosis of neurodegenerative diseases. Guidelines recommend voxel-based statistical testing to support visual evaluation of the PET images. However, the performance of voxel-based testing strongly depends on each single preprocessing step involved.

OBJECTIVE

To optimize the processing pipeline of voxel-based testing for the prognosis of dementia in subjects with amnestic mild cognitive impairment (MCI).

METHODS

The study included 108 ADNI MCI subjects grouped as 'stable MCI' (n = 77) or 'MCI-to-AD converter' according to their diagnostic trajectory over 3 years. Thirty-two ADNI normals served as controls. Voxel-based testing was performed with the statistical parametric mapping software (SPM8) starting with default settings. The following modifications were added step-by-step: (i) motion correction, (ii) custom-made FDG template, (iii) different reference regions for intensity scaling, and (iv) smoothing was varied between 8 and 18 mm. The t-sum score for hypometabolism within a predefined AD mask was compared between the different settings using receiver operating characteristic (ROC) analysis with respect to differentiation between 'stable MCI' and 'MCI-to-AD converter'. The area (AUC) under the ROC curve was used as performance measure.

RESULTS

The default setting provided an AUC of 0.728. The modifications of the processing pipeline improved the AUC up to 0.832 (p = 0.046). Improvement of the AUC was confirmed in an independent validation sample of 241 ADNI MCI subjects (p = 0.048).

CONCLUSION

The prognostic value of voxel-based single subject analysis of brain FDG PET in MCI subjects can be improved considerably by optimizing the processing pipeline.

Brain fluorodeoxyglucose (FDG) PET in dementia

The purpose of this article is to present a selective and concise summary of fluorodeoxyglucose (FDG) positron emission tomography (PET) in dementia imaging. FDG PET is used to visualize a downstream topographical marker that indicates the distribution of neural injury or synaptic dysfunction, and can identify distinct phenotypes of dementia due to Alzheimer's disease (AD), Lewy bodies, and frontotemporal lobar degeneration. AD dementia shows hypometabolism in the parietotemporal association area, posterior cingulate, and precuneus. Hypometabolism in the inferior parietal lobe and posterior cingulate/precuneus is a predictor of cognitive decline from mild cognitive impairment (MCI) to AD dementia. FDG PET may also predict conversion of cognitively normal individuals to those with MCI. Age-related hypometabolism is observed mainly in the anterior cingulate and anterior temporal lobe, along with regional atrophy. Voxel-based statistical analyses, such as statistical parametric mapping or three-dimensional stereotactic surface projection, improve the diagnostic performance of imaging of dementias. The potential of FDG PET in future clinical and methodological studies should be exploited further.

Construction and comparative evaluation of different activity detection methods in brain FDG-PET

Aim

We constructed and evaluated reference brain FDG-PET databases for usage by three software programs (Computer-aided diagnosis for dementia (CAD4D), Statistical Parametric Mapping (SPM) and NEUROSTAT), which allow a user-independent detection of dementia-related hypometabolism in patients’ brain FDG-PET.

Methods

Thirty-seven healthy volunteers were scanned in order to construct brain FDG reference databases, which reflect the normal, age-dependent glucose consumption in human brain, using either software. Databases were compared to each other to assess the impact of different stereotactic normalization algorithms used by either software package. In addition, performance of the new reference databases in the detection of altered glucose consumption in the brains of patients was evaluated by calculating statistical maps of regional hypometabolism in FDG-PET of 20 patients with confirmed Alzheimer’s dementia (AD) and of 10 non-AD patients. Extent (hypometabolic volume referred to as cluster size) and magnitude (peak z-score) of detected hypometabolism was statistically analyzed.

Results

Differences between the reference databases built by CAD4D, SPM or NEUROSTAT were observed. Due to the different normalization methods, altered spatial FDG patterns were found. When analyzing patient data with the reference databases created using CAD4D, SPM or NEUROSTAT, similar characteristic clusters of hypometabolism in the same brain regions were found in the AD group with either software. However, larger z-scores were observed with CAD4D and NEUROSTAT than those reported by SPM. Better concordance with CAD4D and NEUROSTAT was achieved using the spatially normalized images of SPM and an independent z-score calculation. The three software packages identified the peak z-scores in the same brain region in 11 of 20 AD cases, and there was concordance between CAD4D and SPM in 16 AD subjects.

Conclusion

The clinical evaluation of brain FDG-PET of 20 AD patients with either CAD4D-, SPM- or NEUROSTAT-generated databases from an identical reference dataset showed similar patterns of hypometabolism in the brain regions known to be involved in AD. The extent of hypometabolism and peak z-score appeared to be influenced by the calculation method used in each software package rather than by different spatial normalization parameters.

Deformation field correction for spatial normalization of PET images

Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet the current state of the art in PET-to-PET registration is limited to the application of conventional deformable registration methods that were developed for structural images. A method is presented for the spatial normalization of PET images that improves their anatomical alignment over the state of the art. The approach works by correcting the deformable registration result using a model that is learned from training data having both PET and structural images. In particular, viewing the structural registration of training data as ground truth, correction factors are learned by using a generalized ridge regression at each voxel given the PET intensities and voxel locations in a population-based PET template. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation evaluation on 79 subjects shows that the proposed method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.

Assessment of brain damage and plasticity in the visual system due to early occipital lesion: comparison of FDG-PET with diffusion MRI tractography

PURPOSE

To determine the relation between glucose metabolic changes of the primary visual cortex, structural abnormalities of the corresponding visual tracts, and visual symptoms in children with Sturge-Weber syndrome (SWS).

MATERIALS AND METHODS

In 10 children with unilateral SWS (ages 1.5-5.5 years), a region-of-interest analysis was applied in the bilateral medial occipital cortex on positron emission tomography (PET) and used to track diffusion-weighted imaging (DWI) streamlines corresponding to the central visual pathway. Normalized streamline volumes of individual SWS patients were compared with values from age-matched control groups as well as correlated with normalized glucose uptakes and visual field deficit.

RESULTS

Lower glucose uptake and lower corresponding streamline volumes were detected in the affected occipital lobe in 9/10 patients, as compared to the contralateral side. Seven of these 9 patients had visual field deficit and normal or decreased streamline volumes on the unaffected side. The two other children had no visual symptoms and showed high contralateral visual streamline volumes. There was a positive correlation between the normalized ratios on DWI and PET, indicating that lower glucose metabolism was associated with lower streamline volume in the affected hemisphere (R = 0.70, P = 0.024).

CONCLUSION

We demonstrated that 18F-flurodeoxyglucose (FDG)-PET combined with DWI tractography can detect both brain damage on the side of the lesion and contralateral plasticity in children with early occipital lesions.