A methodology for generating normal and pathological brain perfusion SPECT images for evaluation of MRI/SPECT fusion methods: application in epilepsy

Design Study of an Ultrahigh Resolution Brain SPECT System Using a Synthetic Compound-Eye Camera Design With Micro-Slit and Micro-Ring Apertures

In this paper, we discuss the design study for a brain SPECT imaging system, referred to as the HelmetSPECT system, based on a spherical synthetic compound-eye (SCE) gamma camera design. The design utilizes a large number (  ∼ 500 ) of semiconductor detector modules, each coupled to an aperture with a very narrow opening for high-resolution SPECT imaging applications. In this study, we demonstrate that this novel system design could provide an excellent spatial resolution, a very high sensitivity, and a rich angular sampling without scanning motion over a clinically relevant field-of-view (FOV). These properties make the proposed HelmetSPECT system attractive for dynamic imaging of epileptic patients during seizures. In ictal SPECT, there is typically no prior information on where the seizures would happen, and both the imaging resolution and quantitative accuracy of the dynamic SPECT images would provide critical information for staging the seizures outbreak and refining the plans for subsequent surgical intervention.We report the performance evaluation and comparison among similar system geometries using non-conventional apertures, such as micro-ring and micro-slit, and traditional lofthole apertures. We demonstrate that the combination of ultrahigh-resolution imaging detectors, the SCE gamma camera design, and the micro-ring and micro-slit apertures would offer an interesting approach for the future ultrahigh-resolution clinical SPECT imaging systems without sacrificing system sensitivity and FOV.

Anatomical-functional image fusion by information of interest in local Laplacian filtering domain

A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods.

Including anatomical and functional information in MC simulation of PET and SPECT brain studies. Brain-VISET: a voxel-based iterative method

Monte Carlo (MC) simulation provides a flexible and robust framework to efficiently evaluate and optimize image processing methods in emission tomography. In this work we present Brain-VISET (Voxel-based Iterative Simulation for Emission Tomography), a method that aims to simulate realistic [ (99m) Tc]-SPECT and [ (18) F]-PET brain databases by including anatomical and functional information. To this end, activity and attenuation maps generated using high-resolution anatomical images from patients were used as input maps in a MC projector to simulate SPECT or PET sinograms. The reconstructed images were compared with the corresponding real SPECT or PET studies in an iterative process where the activity inputs maps were being modified at each iteration. Datasets of 30 refractory epileptic patients were used to assess the new method. Each set consisted of structural images (MRI and CT) and functional studies (SPECT and PET), thereby allowing the inclusion of anatomical and functional variability in the simulation input models. SPECT and PET sinograms were obtained using the SimSET package and were reconstructed with the same protocols as those employed for the clinical studies. The convergence of Brain-VISET was evaluated by studying the behavior throughout iterations of the correlation coefficient, the quotient image histogram and a ROI analysis comparing simulated with real studies. The realism of generated maps was also evaluated. Our findings show that Brain-VISET is able to generate realistic SPECT and PET studies and that four iterations is a suitable number of iterations to guarantee a good agreement between simulated and real studies.

FocusDET, a new toolbox for SISCOM analysis. Evaluation of the registration accuracy using Monte Carlo simulation

Subtraction of Ictal SPECT Co-registered to MRI (SISCOM) is an imaging technique used to localize the epileptogenic focus in patients with intractable partial epilepsy. The aim of this study was to determine the accuracy of registration algorithms involved in SISCOM analysis using FocusDET, a new user-friendly application. To this end, Monte Carlo simulation was employed to generate realistic SPECT studies. Simulated sinograms were reconstructed by using the Filtered BackProjection (FBP) algorithm and an Ordered Subsets Expectation Maximization (OSEM) reconstruction method that included compensation for all degradations. Registration errors in SPECT-SPECT and SPECT-MRI registration were evaluated by comparing the theoretical and actual transforms. Patient studies with well-localized epilepsy were also included in the registration assessment. Global registration errors including SPECT-SPECT and SPECT-MRI registration errors were less than 1.2 mm on average, exceeding the voxel size (3.32 mm) of SPECT studies in no case. Although images reconstructed using OSEM led to lower registration errors than images reconstructed with FBP, differences after using OSEM or FBP in reconstruction were less than 0.2 mm on average. This indicates that correction for degradations does not play a major role in the SISCOM process, thereby facilitating the application of the methodology in centers where OSEM is not implemented with correction of all degradations. These findings together with those obtained by clinicians from patients via MRI, interictal and ictal SPECT and video-EEG, show that FocusDET is a robust application for performing SISCOM analysis in clinical practice.

Generation of realistic HMPAO SPECT images using a subresolution sandwich phantom

Traditional interpretation of rCBF SPECT data is of a qualitative nature and is dependent on the observer's understanding of the normal distribution of the tracer. The use of a normal database in quantitative regional analysis facilitates the detection of functional abnormality in individual and group studies by accounting for inter-subject variability. The ability to simulate realistic images would allow various important areas related to the use of normal databases to be studied. These include the optimisation of the detection of abnormal blood flow and the portability of normal databases between gamma camera systems. To investigate this further we have constructed a hardware phantom and scanned various configurations of radioactive brain patterns and simulated skull configurations.

METHODS

A subresolution sandwich phantom was constructed with a simulated skull which was assembled using a high-resolution segmented MR scan printed with a (99m)TcO₄ - mixture and scanned using a double-headed gamma camera with parallel-hole collimators. Various different grey-to-white matter (GM:WM) ratios and aluminium simulated skull configurations were used. A single difference measure between the phantom data and a control database mean image was used for optimisation. The realism of phantom data was assessed using statistical parametric mapping (SPM) and ROI analysis.

RESULTS

Optimisation was achieved with a range of WM:GM ratios from 1.9 to 2.4:1 with various simulated skull configurations.

CONCLUSION

The ability to simulate realistic HMPAO SPECT scans has been demonstrated using a subresolution sandwich phantom. Further work, involving scanning the optimised phantom on different gamma camera systems and comparison with camera-specific normal databases should further refine the phantom configuration.

Comparison of heterogeneity quantification algorithms for brain SPECT perfusion images

Background

Several algorithms from the literature were compared with the original random walk (RW) algorithm for brain perfusion heterogeneity quantification purposes. Algorithms are compared on a set of 210 brain single photon emission computed tomography (SPECT) simulations and 40 patient exams.

Methods

Five algorithms were tested on numerical phantoms. The numerical anthropomorphic Zubal head phantom was used to generate 42 (6 × 7) different brain SPECT simulations. Seven diffuse cortical heterogeneity levels were simulated with an adjustable Gaussian noise function and six focal perfusion defect levels with temporoparietal (TP) defects. The phantoms were successively projected and smoothed with Gaussian kernel with full width at half maximum (FWHM = 5 mm), and Poisson noise was added to the 64 projections. For each simulation, 5 Poisson noise realizations were performed yielding a total of 210 datasets. The SPECT images were reconstructed using filtered black projection (Hamming filter: α = 0.5).

The five algorithms or measures tested were the following: the coefficient of variation, the entropy and local entropy, fractal dimension (FD) (box counting and Fourier power spectrum methods), the gray-level co-occurrence matrix (GLCM), and the new RW.

The heterogeneity discrimination power was obtained with a linear regression for each algorithm. This regression line is a mean function of the measure of heterogeneity compared to the different diffuse heterogeneity and focal defect levels generated in the phantoms. A greater slope denotes a larger separation between the levels of diffuse heterogeneity.

The five algorithms were computed using 40 99mTc-ethyl-cysteinate-dimer (ECD) SPECT images of patients referred for memory impairment. Scans were blindly ranked by two physicians according to the level of heterogeneity, and a consensus was obtained. The rankings obtained by the algorithms were compared with the physicians' consensus ranking.

Results

The GLCM method (slope = 58.5), the fractal dimension (35.9), and the RW method (31.6) can differentiate the different levels of diffuse heterogeneity. The GLCM contrast parameter method is not influenced by a focal defect contrary to the FD and RW methods. A significant correlation was found between the RW method and the physicians' classification (r = 0.86; F = 137; p < 0.0001).

Conclusions

The GLCM method can quantify the different levels of diffuse heterogeneity in brain-simulated SPECT images without an influence from the focal cortical defects. However, GLCM classification was not correlated with the physicians' classification (Rho = −0.099). The RW method was significantly correlated with the physicians' heterogeneity perception but is influenced by the existence of a focal defect.

Statistical parametric mapping demonstrates asymmetric uptake with Tc-99m ECD and Tc-99m HMPAO SPECT in normal brain

Tc-99m ethyl cysteinate diethylester (ECD) and Tc-99m hexamethyl propylene amine oxime (HMPAO) are commonly used for single-photon emission computed tomography (SPECT) studies of a variety of neurologic disorders. Although these tracers have been very helpful in diagnosing and guiding treatment of neurologic disease, data describing the distribution and laterality of these tracers in normal resting brain are limited. Advances in quantitative functional imaging have demonstrated the value of using resting studies from control populations as a baseline to account for physiologic fluctuations in cerebral perfusion. Here, we report results from 30 resting Tc-99m ECD SPECT scans and 14 resting Tc-99m HMPAO scans of normal volunteers with no history of neurologic disease. Scans were analyzed with regions of interest and with statistical parametric mapping, with comparisons performed laterally (left vs. right), as well as for age, gender, and handedness. The results show regions of significant asymmetry in the normal controls affecting widespread areas in the cerebral hemispheres, but most marked in superior parietotemporal region and frontal lobes. The results have important implications for the use of normal control SPECT images in the evaluation of patients with neurologic disease.

Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy

This study assesses the utility of compartmental analysis of SPECT data in lateralizing ictal onset in cases of a putative mesial temporal lobe epilepsy (mTLE). An institutional archival review provided 46 patients (18M, 28F) operated for a putative mTLE who achieved an Engel class Ia postoperative outcome. This established the standard to assure a true ictal origin. Ictal and interictal SPECT images were separately coregistered to T1-weighted (T1W) magnetic resonance (MR) image using a rigid transformation and the intensities matched with an l(1) norm minimization technique. The T1W MR image was segmented into separate structures using an atlas-based automatic segmentation technique with the hippocampi manually segmented to improve accuracy. Mean ictal-interictal intensity difference values were calculated for select subcortical structures and the accuracy of lateralization evaluated using a linear classifier. Hippocampal SPECT analysis yielded the highest lateralization accuracy (91%) followed by the amygdala (87%), putamen (67%) and thalamus (61%). Comparative FLAIR and volumetric analyses yielded 89% and 78% accuracies, respectively. A multi-modality analysis did not generate a higher accuracy (89%). A quantitative anatomically compartmented approach to SPECT analysis yields a particularly high lateralization accuracy in the case of mTLE comparable to that of quantitative FLAIR MR imaging. Hippocampal segmentation in this regard correlates well with ictal origin and shows good reliability in the preoperative analysis.

Development and validation of the random walk algorithm: application to the classification of diffuse heterogeneity in brain SPECT perfusion images

Heterogeneity analysis has been studied for radiological imaging, but few methods have been developed for functional images. Diffuse heterogeneous perfusion frequently appears in brain single photon emission computed tomography (SPECT) images, but objective quantification is lacking. An automatic method, based on random walk (RW) theory, has been developed to quantify perfusion heterogeneity. We assess the robustness of our algorithm in differentiating levels of diffuse heterogeneity even when focal defects are present.

METHODS

Heterogeneity is quantified by counting R (percentage), the mean rate of visited pixels in a fixed number of steps of the stochastic RW process. The algorithm has been tested on the numerical anthropomorphic Zubal head phantom. Seven diffuse cortical heterogeneity levels were simulated with an adjustable Gaussian function and 6 temporoparietal focal defects simulating Alzheimer Disease, leading to 42 phantoms. Data were projected and smoothed (full width at half maximum, 5.5 mm), and Poisson noise was added to the 64 projections. The SPECT data were reconstructed using filtered backprojection (Hamming filter, 0.5 c/p). R values for different levels of perfusion defect and diffuse heterogeneity were evaluated on 3 parameters: the number of slices studied (20 vs 40), the use of Talairach normalization versus original space, and the use of a cortical mask within the Talairach space. For each parameter, regression lines for heterogeneity and temporoparietal defect quantification were analyzed by covariance statistics. R values were also evaluated on SPECT images performed on 25 subjects with suspected focal dementia and on 15 normal controls. Scans were blindly ranked by 2 experienced nuclear physicians according to the degree of diffuse heterogeneity.

RESULTS

Variability of R was smaller than 0.17% for repeated measurements. R was more particularly influenced by diffuse heterogeneity compared with focal perfusion defect. The Talairach normalization had a significant influence on the heterogeneity quantification. The number of slices visited by the RW and the cortical masking have a weak influence on the heterogeneity quantification but only for very low heterogeneity levels. The Spearman coefficient between physicians' consensus and RW automatic ranking is 0.85, in the same order of magnitude as the Spearman coefficient between the rankings of the 2 senior physicians (0.86).

CONCLUSIONS

Random walk is an original and objective method and is able to quantify heterogeneous brain perfusion, even in presence of cortical defects. This method is repeatable, robust, and mainly influenced by spatial normalization.

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.

A new improved version of the realistic digital brain phantom

Image analysis methods must be tested and evaluated within a controlled environment. Simulations can be an extremely helpful tool for validation because ground truth is known. We created the digital brain phantom that is at the heart of our publicly available database of realistic simulated magnetic resonance image (MRI) volumes known as BrainWeb. Even though the digital phantom had l mm(3) isotropic voxel size and a small number of tissue classes, the BrainWeb database has been used in more than one hundred peer-reviewed publications validating different image processing methods. In this paper, we describe the next step in the natural evolution of BrainWeb: the creation of digital brain phantom II that includes three major improvements over the original phantom. First, the realism of the phantom, and the resulting simulations, was improved by modeling more tissue classes to include blood vessels, bone marrow and dura mater classes. In addition. a more realistic skull class was created. The latter is particularly useful for SPECT, PET and CT simulations for which bone attenuation has an important effect. Second, the phantom was improved by an eight-fold reduction in voxel volume to 0.125 mm(3). Third, the method used to create the new phantom was modified not only to take into account the segmentation of these new structures, but also to take advantage of many more automated procedures now available. The overall process has reduced subjectivity and manual intervention when compared to the original phantom, and the process may be easily applied to create phantoms from other subjects. MRI simulations are shown to illustrate the difference between the previous and the new improved digital brain phantom II. Example PET and SPECT simulations are also presented.

Realistic Simulated MRI and SPECT Databases

This paper describes the construction of simulated SPECT and MRI databases that account for realistic anatomical and functional variability. The data is used as a gold-standard to evaluate four SPECT/MRI similarity-based registration methods. Simulation realism was accounted for using accurate physical models of data generation and acquisition. MRI and SPECT simulations were generated from three subjects to take into account inter-subject anatomical variability. Functional SPECT data were computed from six functional models of brain perfusion. Previous models of normal perfusion and ictal perfusion observed in Mesial Temporal Lobe Epilepsy (MTLE) were considered to generate functional variability. We studied the impact noise and intensity non-uniformity in MRI simulations and SPECT scatter correction may have on registration accuracy. We quantified the amount of registration error caused by anatomical and functional variability. Registration involving ictal data was less accurate than registration involving normal data. MR intensity nonuniformity was the main factor decreasing registration accuracy. The proposed simulated database is promising to evaluate many functional neuroimaging methods, involving MRI and SPECT data.

Simulation of realistic abnormal SPECT brain perfusion images: application in semi-quantitative analysis

Simulation is useful in the validation of functional image analysis methods, particularly when considering the number of analysis techniques currently available lacking thorough validation. Problems exist with current simulation methods due to long run times or unrealistic results making it problematic to generate complete datasets. A method is presented for simulating known abnormalities within normal brain SPECT images using a measured point spread function (PSF), and incorporating a stereotactic atlas of the brain for anatomical positioning. This allows for the simulation of realistic images through the use of prior information regarding disease progression. SPECT images of cerebral perfusion have been generated consisting of a control database and a group of simulated abnormal subjects that are to be used in a UK audit of analysis methods. The abnormality is defined in the stereotactic space, then transformed to the individual subject space, convolved with a measured PSF and removed from the normal subject image. The dataset was analysed using SPM99 (Wellcome Department of Imaging Neuroscience, University College, London) and the MarsBaR volume of interest (VOI) analysis toolbox. The results were evaluated by comparison with the known ground truth. The analysis showed improvement when using a smoothing kernel equal to system resolution over the slightly larger kernel used routinely. Significant correlation was found between effective volume of a simulated abnormality and the detected size using SPM99. Improvements in VOI analysis sensitivity were found when using the region median over the region mean. The method and dataset provide an efficient methodology for use in the comparison and cross validation of semi-quantitative analysis methods in brain SPECT, and allow the optimization of analysis parameters.

Content-based image database system for epilepsy

We have designed and implemented a human brain multi-modality database system with content-based image management, navigation and retrieval support for epilepsy. The system consists of several modules including a database backbone, brain structure identification and localization, segmentation, registration, visual feature extraction, clustering/classification and query modules. Our newly developed anatomical landmark localization and brain structure identification method facilitates navigation through an image data and extracts useful information for segmentation, registration and query modules. The database stores T1-, T2-weighted and FLAIR MRI and ictal/interictal SPECT modalities with associated clinical data. We confine the visual feature extractors within anatomical structures to support semantically rich content-based procedures. The proposed system serves as a research tool to evaluate a vast number of hypotheses regarding the condition such as resection of the hippocampus with a relatively small volume and high average signal intensity on FLAIR. Once the database is populated, using data mining tools, partially invisible correlations between different modalities of data, modeled in database schema, can be discovered. The design and implementation aspects of the proposed system are the main focus of this paper.

From anatomic standardization analysis of perfusion SPECT data to perfusion pattern modeling: evidence of functional networks in healthy subjects and temporal lobe epilepsy patients

RATIONALE AND OBJECTIVES

In the general context of perfusion pattern modeling from single-photon emission computed tomographic (SPECT) data, the purpose of this study is to characterize interindividual functional variability and functional connectivity between anatomic structures in a set of SPECT data acquired from a homogeneous population of subjects.

MATERIALS AND METHODS

From volume of interest (VOI)-perfusion measurements performed on anatomically standardized SPECT data, we proposed to use correspondence analysis (CA) and hierarchical clustering (HC) to explore the structure of statistical dependencies among these measurements. The method was applied to study the perfusion pattern in two populations of subjects; namely, SPECT data from 27 healthy subjects and ictal SPECT data from 10 patients with mesio-temporal lobe epilepsy (MTLE).

RESULTS

For healthy subjects, anatomic structures showing statistically dependent perfusion patterns were classified into four groups; namely, temporomesial structures, internal structures, posterior structures, and remaining cortex. For patients with MTLE, they were classified as temporomesial structures, surrounding temporal structures, internal structures, and remaining cortex. Anatomic structures of each group showed similar perfusion behavior so that they may be functionally connected and may belong to the same network. Our main result is that the temporal pole and lenticular nucleus seemed to be highly relevant to characterize ictal perfusion in patients with MTLE. This exploratory analysis suggests that a network involving temporal structures, lenticular nucleus, brainstem, and cerebellum seems to be involved during MTLE seizures.

CONCLUSION

CA followed by HC is a promising approach to explore brain perfusion patterns from SPECT VOI measurements.