Improved efficiency for MRI-SPET registration based on mutual information

Image coregistration: quantitative processing framework for the assessment of brain lesions

The quantitative, multiparametric assessment of brain lesions requires coregistering different parameters derived from MRI sequences. This will be followed by analysis of the voxel values of the ROI within the sequences and calculated parametric maps, and deriving multiparametric models to classify imaging data. There is a need for an intuitive, automated quantitative processing framework that is generalized and adaptable to different clinical and research questions. As such flexible frameworks have not been previously described, we proceeded to construct a quantitative post-processing framework with commonly available software components. Matlab was chosen as the programming/integration environment, and SPM was chosen as the coregistration component. Matlab routines were created to extract and concatenate the coregistration transforms, take the coregistered MRI sequences as inputs to the process, allow specification of the ROI, and store the voxel values to the database for statistical analysis. The functionality of the framework was validated using brain tumor MRI cases. The implementation of this quantitative post-processing framework enables intuitive creation of multiple parameters for each voxel, facilitating near real-time in-depth voxel-wise analysis. Our initial empirical evaluation of the framework is an increased usage of analysis requiring post-processing and increased number of simultaneous research activities by clinicians and researchers with non-technical backgrounds. We show that common software components can be utilized to implement an intuitive real-time quantitative post-processing framework, resulting in improved scalability and increased adoption of post-processing needed to answer important diagnostic questions.

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.

RIGID MEDICAL IMAGE REGISTRATION AND ITS ASSOCIATION WITH MUTUAL INFORMATION

Image registration plays a crucial role in the computer vision and medical imaging field where it is used to develop a spatial mapping between different sets of data. These transformations can range from simple rigid registrations to complex nonrigid deformations. Mutual information (MI) is a popular entropy-based similarity measure which has recently experienced a prolific expansion in a number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it only assumes a statistical dependence between images. This paper provides a thorough introduction to the MI measure and its use in rigid medical image registration. A look at the extensions proposed to the original measure will also be provided. These were developed to improve the robustness of the measure and to avoid certain cases when maximizing MI does not lead to the correct spatial alignment.

Prospective study of p-[123I]iodo-L-phenylalanine and SPECT for the evaluation of newly diagnosed cerebral lesions: specific confirmation of glioma

PURPOSE

The differentiation between gliomas, metastases and gliotic or inflammatory lesions by imaging techniques remains a challenge. Gliomas frequently exhibit increased uptake of radiolabelled amino acids and are thus amenable to PET or SPECT imaging. Recently, p-[123I]iodo-L-phenylalanine (IPA) was validated for the visualization of glioma by SPECT and received orphan drug status. Here we investigated its diagnostic performance for differentiating indeterminate brain lesions.

METHODS

This prospective open study included 67 patients with newly diagnosed brain lesions suspicious for glioma (34 without and 33 with contrast enhancement in the MRI scan). Patients received 250 MBq IPA intravenously after overnight fasting. SPECT images at 30 min and 3 h post-injection were iteratively reconstructed and visually interpreted after image fusion with an MRI brain scan (fluid-attenuated inversion recovery sequence or T1-weighted contrast-enhanced image). Findings were correlated with results of stereotactic or open biopsies or serial imaging.

RESULTS

Twenty-seven low-grade (2 WHO I, 25 WHO II) and 24 high-grade gliomas (1 WHO III, 23 WHO IV), 3 metastases originating from lung cancer as well as 13 non-neoplastic lesions were proven. All non-neoplastic lesions and all metastases were negative with IPA SPECT. Forty gliomas were true-positive (TP) and 11 false-negative (FN) findings (8 WHO II, 1 WHO III, 2 WHO IV) occurred. There were no false-positive (FP) findings. For the differentiation of primary brain tumours and non-neoplastic lesions, sensitivity and specificity were 78 and 100%. In 34 lesions without contrast enhancement in MRI, IPA SPECT resulted in 17 TP, 8 true-negative, 9 FN and no FP findings (sensitivity 65%, specificity 100%).

CONCLUSION

In patients with suspected glioma, IPA SPECT shows a high specificity, but especially in low-grade gliomas FN findings may occur. Due to the high positive predictive value a positive finding allows a suspected glioma to be confirmed.

Information entropy measure for evaluation of image quality

This paper presents a simple and straightforward method for synthetically evaluating digital radiographic images by a single parameter in terms of transmitted information (TI). The features of our proposed method are (1) simplicity of computation, (2) simplicity of experimentation, and (3) combined assessment of image noise and resolution (blur). Two acrylic step wedges with 0-1-2-3-4-5 and 0-2-4-6-8-10 mm in thickness were used as phantoms for experiments. In the present study, three experiments were conducted. First, to investigate the relation between the value of TI and image noise, various radiation doses by changing exposure time were employed. Second, we examined the relation between the value of TI and image blurring by shifting the phantoms away from the center of the X-ray beam area toward the cathode end when imaging was performed. Third, we analyzed the combined effect of deteriorated blur and noise on the images by employing three smoothing filters. Experimental results show that the amount of TI is closely related to both image noise and image blurring. The results demonstrate the usefulness of our method for evaluation of physical image quality in medical imaging.

Automated method for small-animal PET image registration with intrinsic validation

PURPOSE

We propose and compare different registration approaches to align small-animal PET studies and a procedure to validate the results by means of objective registration consistency measurements.

PROCEDURES

We have applied a registration algorithm based on information theory, using different approaches to mask the reference image. The registration consistency allows for the detection of incorrect registrations. This methodology has been evaluated on a test dataset (FDG-PET rat brain images).

RESULTS

The results show that a multiresolution two-step registration approach based on the use of the whole image at the low resolution step, while masking the brain at the high resolution step, provides the best robustness (87.5% registration success) and highest accuracy (0.67-mm average).

CONCLUSIONS

The major advantages of our approach are minimal user interaction and automatic assessment of the registration error, avoiding visual inspection of the results, thus facilitating the accurate, objective, and rapid analysis of large groups of rodent PET images.

Intra-individual comparison of p-[123I]-iodo-L-phenylalanine and L-3-[123I]-iodo-α-methyl-tyrosine for SPECT imaging of gliomas

OBJECTIVES

Radioactive amino-acids accumulate in gliomas even with an intact blood-brain-barrier. L-3-[(123)I]-iodo-alpha-methyl-tyrosine (IMT) is well established for SPECT imaging of gliomas. Recently, we introduced p-[(123)I]-iodo-L-phenylalanine (IPA) for the characterisation of brain lesions. This study compares both tracers in glioma patients.

METHODS

Eleven patients with gliomas (1 WHO grade 1, 5 grade 2, 1 grade 3, 2 grade 4 gliomas, 1 unconfirmed upgrading and 1 post-therapeutic non-neoplastic lesion) underwent SPECT imaging with IPA (early and delayed acquisitions at 30 min and 3 h) and IMT (early only). Maximum tumour-to-brain ratios (TBR) were calculated using region-of-interest analysis to assess uptake of IMT and IPA. Imaging results were compared to histopathological findings.

RESULTS

Early TBRs of IMT and IPA were strongly correlated (r = 0.828, p = 0.002). TBRs were higher for IMT than IPA (1.95+/-0.50 versus 1.79+/-0.42; p < 0.05), but independent from tumour cell density (p > 0.1). Visual interpretation by different observers was more concordant for IMT-SPECT than IPA-SPECT (kappa 1.0 versus 0.774). No differences in early TBRs were observed between low-grade and high-grade gliomas for IMT (1.97+/-0.53 versus 2.21+/-0.44, p > 0.5) or IPA (1.70+/-0.23 versus 2.21+/-0.56, p = 0.167) with a trend to higher TBRs in low-grade tumours for IMT (p = 0.093). In contrast to the known wash-out of IMT, we observed persistent accumulation of IPA in gliomas.

CONCLUSIONS

IPA shows lower TBRs than IMT, especially in low-grade tumours, so IMT should be preferred for the delineation of low-grade gliomas by SPECT imaging. Due to its prolonged retention, however, IPA remains promising for therapeutic use in gliomas after labelling with I-131.

An automatic MRI/SPECT registration algorithm using image intensity and anatomical feature as matching characters: application on the evaluation of Parkinson's disease

Single-photon emission computed tomography (SPECT) of dopamine transporters with (99m)Tc-TRODAT-1 has recently been proposed to offer valuable information in assessing the functionality of dopaminergic systems. Magnetic resonance imaging (MRI) and SPECT imaging are important in the noninvasive examination of dopamine concentration in vivo. Therefore, this investigation presents an automated MRI/SPECT image registration algorithm based on a new similarity metric. This similarity metric combines anatomical features that are characterized by specific binding, the mean count per voxel in putamens and caudate nuclei, and the distribution of image intensity that is characterized by normalized mutual information (NMI). A preprocess, a novel two-cluster SPECT normalization algorithm, is also presented for MRI/SPECT registration. Clinical MRI/SPECT data from 18 healthy subjects and 13 Parkinson's disease (PD) patients are involved to validate the performance of the proposed algorithms. An appropriate color map, such as "rainbow," for image display enables the two-cluster SPECT normalization algorithm to provide clinically meaningful visual contrast. The proposed registration scheme reduces target registration error from >7 mm for conventional registration algorithm based on NMI to approximately 4 mm. The error in the specific/nonspecific (99m)Tc-TRODAT-1 binding ratio, which is employed as a quantitative measure of TRODAT receptor binding, is also reduced from 0.45+/-0.22 to 0.08+/-0.06 among healthy subjects and from 0.28+/-0.18 to 0.12+/-0.09 among PD patients.

Improved accuracy of brain MRI/SPECT registration using a two-cluster SPECT normalization algorithm and a combinative similarity measure: application to the evaluation of Parkinson's disease

OBJECTIVE

Single-photon emission computed tomography (SPECT) of dopamine transporters with technetium-99-labeled tropane derivative (99m)Tc-TRODAT-1 has recently been suggested to offer valuable information in assessing the functionality of dopaminergic systems. To facilitate the non-invasive examination of the dopamine concentration in vivo, registering magnetic resonance imaging (MRI) and SPECT image is important. This article proposes a new similarity measure for MRI/SPECT registration.

METHODS

The proposed similarity measure combines anatomic features that are characterized by specific binding of nuclear medicine and the distribution of image intensity that are characterized by the normalized mutual information (NMI). A preprocess, a novel two-cluster SPECT normalization algorithm, is also proposed.

RESULTS

Compared with the conventional NMI-based registration algorithm, the proposed registration framework reduces the target of registration error from >7 mm to approximately 4 mm. The error of the specific-to-non-specific (99m)Tc-TRODAT-1 binding ratio (BR), a quantitative measure of TRODAT receptor binding, is also reduced from 0.45 to 0.08 in the healthy subjects and from 0.28 to 0.12 in Parkinson's disease patients.

CONCLUSIONS

A suitable color map, such as "rainbow," for image display enables the two-cluster SPECT normalization algorithm to provide clinically meaningful visual contrast. In addition, registering MRI/SPECT based on the proposed similarity measure improves the accuracy compared with the conventional NMI-based algorithm.

Value of CT Thallium-201 SPECT Fusion Imaging over SPECT Alone for Detection and Localization of Nasopharyngeal and Maxillary Cancers

OBJECTIVE

The purpose of this study was to investigate the incremental clinical utility of CT and high-resolution SPECT fusion imaging.

MATERIALS AND METHODS

Eighteen patients with nasopharyngeal cancer or cancers around the maxilla were scanned with high-resolution SPECT at the time of initial diagnosis (18 studies) and during follow-up after chemoradiotherapy (23 studies). SPECT results were compared with histologic findings or the findings of other imaging techniques. In addition, automatic image registration without fiducial markers was performed from CT and SPECT data, and the effect of fusion imaging on the localization of abnormalities was evaluated.

RESULTS

All of the original 18 untreated lesions showed high uptake. Recurrent tumors had a tendency to show high uptake (seven of nine patients), whereas little or no uptake generally represented no recurrence (12 of 14 patients) (chi-square test with Yates correction: chi2 = 6.80, p < 0.01). In two patients, physiologic uptake in the unilateral prevertebral muscle was revealed on image fusion. In four of the nine recurrent nasopharyngeal cancers (44%), SPECT alone could not determine abnormalities in uptake sites, whereas CT/SPECT fusion imaging clearly localized the sites and was helpful for treatment strategy.

CONCLUSION

High-resolution thallium-201 (201Tl) SPECT has a very high detection rate in patients with nasopharyngeal cancer and cancers around the maxilla. However, the anatomic identification or localization of the uptake sites is sometimes difficult without CT/SPECT fusion imaging. This technique without external markers is practically feasible to generate clinically valid fusion images.

Fusion imaging of computed tomographic pulmonary angiography and SPECT ventilation/perfusion scintigraphy: initial experience and potential benefit

PURPOSE

The objective of this study was to examine the feasibility of fusing ventilation and perfusion data from single-photon emission computed tomography (SPECT) ventilation perfusion (V/Q) scintigraphy together with computed tomographic pulmonary angiography (CTPA) data. We sought to determine the accuracy of this fusion process. In addition, we correlated the findings of this technique with the final clinical diagnosis.

METHODS

Thirty consecutive patients (17 female, 13 male) who had undergone both CTPA and SPECT V/Q scintigraphy during their admission for investigation of potential pulmonary embolism were identified retrospectively. Image datasets from these two modalities were co-registered and fused using commercial software. Accuracy of the fusion process was determined subjectively by correlation between modalities of the anatomical boundaries and co-existent pleuro-parenchymal abnormalities.

RESULTS

In all 30 cases, SPECT V/Q images were accurately fused with CTPA images. An automated registration algorithm was sufficient alone in 23 cases (77%). Additional linear z-axis scaling was applied in seven cases. There was accurate topographical co-localisation of vascular, parenchymal and pleural disease on the fused images. Nine patients who had positive CTPA performed as an initial investigation had co-localised perfusion defects on the subsequent fused CTPA/SPECT images. Three of the 11 V/Q scans initially reported as intermediate could be reinterpreted as low probability owing to co-localisation of defects with parenchymal or pleural pathology.

CONCLUSION

Accurate fusion of SPECT V/Q scintigraphy to CTPA images is possible. This technique may be clinically useful in patients who have non-diagnostic initial investigations or in whom corroborative imaging is sought.

Simultaneous surface registration of ictal and interictal SPECT and magnetic resonance images for epilepsy studies

BACKGROUND

Subtraction of ictal and interictal single photon emission computed tomography (SPECT) images is known to be successful in localizing the seizure focus in the pre-surgical evaluation of patients with partial epilepsy. A computer-aided methods for producing subtraction ictal SPECT co-registered to the magnetic resonance image (MRI) (the SISCOM method) is commonly used. The two registrations involved in SISCOM are (1) between the ictal-interictal SPECT images, which was shown to be the more critical, and (2) between the ictal image and MRI.

OBJECTIVE

To improve the accuracy of ictal-interictal registration in SISCOM by registering all three images (ictal, interictal SPECT, MRI) simultaneously.

METHODS

The registration problem is formulated as the minimization of a cost function between three surfaces. Then, to achieve a global minimum of this cost function, the Powell algorithm with randomly distributed initial configurations is used. This technique is tested by a realistic simulation study, a phantom study and a patient study.

RESULTS

The results of the simulation study demonstrate that, in surface-based registration, the triple-registration method results in a smaller ictal-interictal SPECT registration error than the pair-wise registration method (P<0.05) for a range of values of the cost-function parameter. However, the improved registration error is still larger than that obtained by the normalized mutual information method (P<0.001), which is a voxel-based registration algorithm. The phantom and patient studies reveal no observable difference between registration results.

CONCLUSIONS

Although the improved accuracy of triple registration is slightly worse than voxel-based registration, it will soon be possible to apply the results of this study in research utilizing the triple-registration principle to improving voxel-based results of ictal-interictal registration.

Validation of brain tumour imaging with p-[123I]iodo-l-phenylalanine and SPECT

PURPOSE

The aims of this prospective study were to validate single-photon emission computed tomography (SPECT) with p-[(123)I]iodo-L-phenylalanine (IPA) in brain tumours and to evaluate its potential for the characterisation of indeterminate brain lesions.

METHODS

In 45 patients with indeterminate brain lesions or suspected progression of glioma, amino acid uptake was studied using IPA-SPECT and compared with the final diagnosis established by biopsy or serial imaging. After image fusion of IPA-SPECT and magnetic resonance imaging, the presence of tumour was visually determined by two independent observers. IPA uptake was quantified as the ratio between maximum uptake in the suspicious lesion and mean uptake in unaffected brain.

RESULTS

Primary brain tumours were present in 35 cases (12 low-grade and 23 high-grade gliomas). Non-neoplastic brain lesions were confirmed in seven cases (three dysplasias, three inflammatory lesions, one lesion after effective therapy). Visual analysis showed a high concordance between the two observers (kappa=0.90, p<0.001), with sensitivity and specificity of 86% and 100% for the discrimination of primary brain tumours and non-neoplastic lesions. At 30 min p.i., IPA uptake in primary brain tumours was higher than that in non-neoplastic lesions (1.70+/-0.36 vs 1.14+/-0.18, p<0.05). Brain metastases showed no increased uptake (1.13+/-0.22, n=3). The persistent retention of IPA in low-grade gliomas without disruption of the blood-brain barrier was visualised up to 24 h p.i. Low-grade and high-grade gliomas showed equivalent IPA uptake (1.72+/-0.37 vs 1.67+/-0.36 at 30 min, p=0.745).

CONCLUSION

IPA shows long and specific retention in gliomas. IPA is a promising and safe radiopharmaceutical for the visualisation of gliomas and the characterisation of indeterminate brain lesions.

Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images

We implemented a 3D co-registration technique based on mutual information (MI) including 2D image matching as a coarse pre-registration. The 2D coarse pre-registration was performed in the transverse, sagittal and coronal planes sequentially, and all six parameters were then optimized as fine registration. Normalized mutual information (NMI) was also examined as another entropy-based measure that was invariant to the overlapped area of two images. In order to compare accuracy and precision of the present method with a conventional two-level multiresolution approach, simulation was performed by 100 trials with the random initial mismatch of +/-10 degrees and +/-17.92 mm (Type-I) and +/-20 degrees and +/-40.32 mm (Type-II). For Type-I, no significant differences were found between registration errors of the multiresolution approach and the present method with the MI criterion. No biases were observed (< or =0.13 degrees and < or =0.57 mm for the multiresolution approach; < or =0.12 degrees and < or =0.57 mm for the present method) and the SDs were very small (< or =0.18 degrees and < or =0.12 mm for the multiresolution approach; < or =0.11 degrees and < or =0.11 mm for the present method). For Type-II, SDs for the multiresolution approach (< or =1.8 degrees and < or =0.88 mm) were markedly larger than those for the present method (< or =0.64 degrees and < or =0.20 mm) with MI. Success rate for the present method was 99.9%, which was higher than 97.6% for the multiresolution approach. Simulation also revealed that MI and NMI performance were almost equivalent. The choice of optimization strategy more affected accuracy and reproducibility than the choice of the registration criterion (MI or NMI) in our simulation condition. The present method is sufficiently accurate and reproducible for MRI-SPECT registration in clinical use.

Automatic image registration of three-dimensional images of the head of cats and dogs by use of maximization of mutual information

OBJECTIVE

To validate mutual information criterion as a ready-to-use technique for automated alignment (ie, registration) of 3-dimensional (3-D) multimodal image data of the head of cats and dogs.

SAMPLE POPULATION

Corresponding 3-D magnetic resonance imaging (MRI) and computed tomography (CT) brain scans of a 6-month-old Doberman Pinscher with a brain cyst; CT images of the head of a European shorthair cat with a meningioma before and immediately, 3, and 6 months after surgical resection; and CT and corresponding stacked anatomic cryosection images of the entire head of a 2-year-old sexually intact female Beagle.

PROCEDURE

All images were matched retrospectively by use of an in-house computer program developed on the basis of a mutual information image registration algorithm. Accuracy of the resulting registrations was evaluated by visual inspection.

RESULTS

All registrations were judged to be highly accurate. Additional manual corrections were not necessary.

CONCLUSIONS AND CLINICAL RELEVANCE

Mutual information registration criterion can by applied to 3-D multimodal head images of cats and dogs for full automatic rigid-body image registration. The combination of such aligned images would considerably facilitate efforts of veterinary clinicians as indicated by its widespread use in brain surgery and radiation therapy of humans.

Registration accuracy of 153Gd transmission images of the brain

PURPOSE

The aim of the study was to determine the accuracy of non-rigid nine-parameter image registrations based on 153Gd transmission computed tomography (TCT) images as compared with those based on 99mTc-ethyl cysteinate dimer (ECD) images and to assess whether normalised mutual information (NMI) or count difference (CD) should be used.

METHODS

TCT and ECD data were acquired in 25 randomly selected patients. Emission images were registered to an ECD template with a CD cost function. The same registration parameters were applied to the transmission images to create a TCT template. All TCT images were registered to the TCT template and the same registration parameters were applied to the ECD images. The procedure was repeated with NMI as cost function. Accuracy of both ECD-based and TCT-based registrations was assessed by comparing the normalisation parameter values and regional activities in the spatially normalised ECD images, using a mixed-model analysis of variance (ANOVA). Scheffe post hoc tests were performed.

RESULTS

No significant differences were found between ECD/CD, ECD/NMI and TCT/CD, suggesting that ECD registration can be done with either CD or NMI, and that TCT registration using CD is equally as accurate as ECD registration. The accuracy of TCT registration with NMI was lower, with discrepancies occurring in the frontal inferior region and the cerebellum. The analysis of normalisation parameters indicated that z-scaling is underestimated and yz-rotation overestimated with TCT/NMI registration.

CONCLUSION

We conclude that ECD registrations with CD or NMI are as accurate as TCT registrations with CD and that TCT registrations with NMI should be avoided.

Fast and robust registration of PET and MR images of human brain

In recent years, mutual information has proved to be an excellent criterion for registration of intra-individual images from different modalities. Multi-resolution coarse-to-fine optimization was proposed for speeding-up of the registration process. The aim of our work was to further improve registration speed without compromising robustness or accuracy. We present and evaluate two procedures for co-registration of positron emission tomography (PET) and magnetic resonance (MR) images of human brain that combine a multi-resolution approach with an automatic segmentation of input image volumes into areas of interest and background. We show that an acceleration factor of 10 can be achieved for clinical data and that a suitable preprocessing can improve robustness of registration. Emphasis was laid on creation of an automatic registration system that could be used routinely in a clinical environment. For this purpose, an easy-to-use graphical user interface has been developed. It allows physicians with no special knowledge of the registration algorithm to perform a fast and reliable alignment of images. Registration progress is presented on the fly on a fusion of images and enables visual checking during a registration.

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

Quantitative evaluation of brain MRI/SPECT fusion methods for normal and in particular pathological datasets is difficult, due to the frequent lack of relevant ground truth. We propose a methodology to generate MRI and SPECT datasets dedicated to the evaluation of MRI/SPECT fusion methods and illustrate the method when dealing with ictal SPECT. The method consists in generating normal or pathological SPECT data perfectly aligned with a high-resolution 3D T1-weighted MRI using realistic Monte Carlo simulations that closely reproduce the response of a SPECT imaging system. Anatomical input data for the SPECT simulations are obtained from this 3D T1-weighted MRI, while functional input data result from an inter-individual analysis of anatomically standardized SPECT data. The method makes it possible to control the 'brain perfusion' function by proposing a theoretical model of brain perfusion from measurements performed on real SPECT images. Our method provides an absolute gold standard for assessing MRI/SPECT registration method accuracy since, by construction, the SPECT data are perfectly registered with the MRI data. The proposed methodology has been applied to create a theoretical model of normal brain perfusion and ictal brain perfusion characteristic of mesial temporal lobe epilepsy. To approach realistic and unbiased perfusion models, real SPECT data were corrected for uniform attenuation, scatter and partial volume effect. An anatomic standardization was used to account for anatomic variability between subjects. Realistic simulations of normal and ictal SPECT deduced from these perfusion models are presented. The comparison of real and simulated SPECT images showed relative differences in regional activity concentration of less than 20% in most anatomical structures, for both normal and ictal data, suggesting realistic models of perfusion distributions for evaluation purposes. Inter-hemispheric asymmetry coefficients measured on simulated data were found within the range of asymmetry coefficients measured on corresponding real data. The features of the proposed approach are compared with those of other methods previously described to obtain datasets appropriate for the assessment of fusion methods.

Design and evaluation of an automatic procedure for detection of large misregistration of medical images

In many cases the combined assessment of three-dimensional anatomical and functional images [single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT)] is necessary to determine the precise nature and extent of lesions. It is important, prior to performing the addition, subtraction, or any other combination of the images, that they be adequately aligned and registered either by experienced radiologists via visual inspection, mental reorientation and overlap of slices, or by an automated registration algorithm. To be useful clinically, the latter case requires validation. The human capacity to evaluate registration results visually is limited and time consuming. This paper describes an algorithmic procedure to provide proxy measures for human assessment that discriminate between badly misregistered pairs of brain images and those likely to be clinically useful. The new algorithm consists of four major steps: segmentation of brain and skin/air boundaries, contour extraction, computation of the principal axes, and computation of the registration quality measures from the contour volumes. The test data were MR and CT brain images. The results of the present study indicate that the use of a measure based on the combination of brain and skin contours and a principal axis function is a good first step to reduce the number of badly registered images reaching the clinician.

Mutual-information-based registration of medical images: a survey

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

Software for image registration: algorithms, accuracy, efficacy

Image registration is finding increased clinical use both in aiding diagnosis and guiding therapy. There are numerous algorithms for registration, which all involve maximizing a measure of similarity between a transformed floating image and a fixed reference image. The choice of the similarity measure depends, to some extent, on the application. Methods based on the use of the joint intensity histogram have become popular because of their flexibility and robustness. A distinction is made between rigid-body and non-rigid transformations. The latter are needed for inter-subject registration or intra-subject registration in cases where the region of the body of interest is not considered rigid. Non-rigid transformation is normally achieved using a global model of the deformation but can also be defined by a set of locally rigid transformations, each constrained to a small block in the image. There is scope for further research on the incorporation of appropriate constraints, especially for the application of non-rigid transformations to nuclear medicine studies. Most of the initial practical concerns regarding image registration have been overcome and there is increasing availability of commercial software. There are several approaches to the validation of registration software, with validation of non-rigid algorithms being particularly difficult. Studies have demonstrated the accuracy on the order of half a pixel for both intra- and inter-modality registration (typically 2 to 3 mm). Although hardware-based registration has now become possible by using dual-modality instruments, software-based registration will continue to play an important role in nuclear medicine.

A new approach to elastography using mutual information and finite elements

Historically, increased mechanical stiffness during tissue palpation exams has been associated with assessing organ health as well as with detecting the growth of a potentially life-threatening cell mass. As such, techniques to image elasticity parameters (i.e., elastography) have recently become of great interest to scientists. In this work, a new method of elastography will be introduced within the context of mammographic imaging. The elastography method proposed represents a non-rigid iterative image registration algorithm that varies material properties within a finite element model to improve registration. More specifically, regional measures of image similarity are used within an objective function minimization framework to reconstruct elasticity images of tissue stiffness. Numerical simulations illustrate: (1) the encoding of stiffness information within the context of a regional image similarity criterion, (2) the methodology for an iterative elastographic imaging framework and (3) elasticity reconstruction simulations. The real strength in this approach is that images from any modality (e.g., magnetic resonance, computed tomography, ultrasound. etc) that have sufficient anatomically-based intensity heterogeneity and remain consistent from a pre- to a post-deformed state could be used in this paradigm.

Could automated template based quantification of benzodiazepine receptors in brain single photon emission tomography with 123I NNC 13-8241 be used to demonstrate neuronal damage in traumatic brain injury?

Automated methods are required for the analysis of brain single photon emission tomography images. We applied an automated method to assess the benzodiazepine receptor distribution in the brain. Images of 19 patients with mild traumatic brain injury who had received I NNC 13-8241 were compared with a mean brain template accumulated from 18 healthy volunteers. To obtain more information, we calculated the neuronal benzodiazepine receptor binding in the brain by using pre-defined anatomical regions and a voxel-by-voxel technique. The group of patients with mild traumatic brain injury differed significantly (P =0.015) from the group of healthy volunteers in the distribution of benzodiazepine receptors. This methodological work suggests that a reference based template and a three-dimensional brain model help in regional analysis and quantification and could be useful in demonstrating permanent neuronal damage after head injury.

Application of the mutual information criterion for feature selection in computer-aided diagnosis

The purpose of this study was to investigate an information theoretic approach to feature selection for computer-aided diagnosis (CAD). The approach is based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any assumptions about the nature of their underlying relationships. Consequently, MI can potentially offer some advantages over feature selection techniques that focus only on the linear relationships of variables. This study was based on a database of statistical texture features extracted from perfusion lung scans. The ultimate goal was to select the optimal subset of features for the computer-aided diagnosis of acute pulmonary embolism (PE). Initially, the study addressed issues regarding the approximation of MI in a limited dataset as it is often the case in CAD applications. The MI selected features were compared to those features selected using stepwise linear discriminant analysis and genetic algorithms for the same PE database. Linear and nonlinear decision models were implemented to merge the selected features into a final diagnosis. Results showed that the MI is an effective feature selection criterion for nonlinear CAD models overcoming some of the well-known limitations and computational complexities of other popular feature selection techniques in the field.