Kristof Baete

Evaluation of anatomy based reconstruction for partial volume correction in brain FDG-PET

UNLABELLED FDG-PET contributes to the diagnosis and management of neurological diseases. In some of these diseases, pathological gray matter (GM) areas may have a reduced FDG uptake. Detection of these regions can be difficult and some remain undiscovered using visual assessment. The main reason for this detection problem is the relatively small thickness of GM compared to the spatial resolution of PET, known as the partial volume effect. We have developed an anatomy-based maximum-a-posteriori reconstruction algorithm (A-MAP) which corrects for this effect during the reconstruction using segmented magnetic resonance (MR) data. Monte-Carlo based 3-D brain software phantom simulations were used to investigate the influence of the strength of anatomy-based smoothing in GM, the influence of misaligned MR data, and the effect of local segmentation errors. A human observer study was designed to assess the detection performance of A-MAP versus post-smoothed maximum-likelihood (ML) reconstruction. We demonstrated the applicability of A-MAP using real patient data. The results for A-MAP showed improved recovery values and robustness for local segmentation errors. Misaligned MR data reduced the recovery values towards those obtained by post-smoothed ML, for small registration errors. In the human observer study, detection accuracy of hypometabolic regions was significantly improved using A-MAP, compared to post-smoothed ML (P < 0.004). The patient study confirmed the applicability of A-MAP in clinical practice. CONCLUSION A-MAP is a promising technique for voxel-based partial volume correction of FDG-PET of the human brain.

Correlations of interictal FDG-PET metabolism and ictal SPECT perfusion changes in human temporal lobe epilepsy with hippocampal sclerosis

BACKGROUND The pathophysiological role of the extensive interictal cerebral hypometabolism in complex partial seizures (CPS) in refractory mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE-HS) is poorly understood. Our aim was to study ictal-interictal SPECT perfusion versus interictal fluorodeoxyglucose (FDG)-PET metabolic patterns. METHODS Eleven adults with refractory unilateral mTLE-HS, who were rendered seizure free after epilepsy surgery, were included. All had an interictal FDG-PET and an interictal and ictal perfusion SPECT scan. FDG-PET data were reconstructed using an anatomy-based reconstruction algorithm, which corrected for partial volume effects, and analyzed semi-quantitatively after normalization to white matter activity. Using Statistical Parametric Mapping (SPM), we compared interictal metabolism of the patient group with a control group. We correlated metabolic with ictal perfusion changes in the patient group. RESULTS Global cerebral grey matter glucose metabolism in patients was decreased 10-25% compared with control subjects. Interictal PET hypometabolism and ictal SPECT hypoperfusion were maximal in the ipsilateral frontal lobe. Ictal frontal lobe hypoperfusion was associated with crossed cerebellar diaschisis. The ipsilateral temporal lobe showed maximal ictal hyperperfusion and interictal hypometabolism, which was relatively mild compared with the degree of hypometabolism affecting the frontal lobes. CONCLUSION Interictal hypometabolism in mTLE-HS was greatest in the ipsilateral frontal lobe and represented a seizure-related dynamic process in view of further ictal decreases. Crossed cerebellar diaschisis suggested that there is a strong ipsilateral frontal lobe inhibition during CPS. We speculate that surround inhibition in the frontal lobe is a dynamic defense mechanism against seizure propagation, and may be responsible for functional deficits observed in mTLE.

Evaluation of Three MRI-Based Anatomical Priors for Quantitative PET Brain Imaging

In emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems.

Anatomical-based FDG-PET reconstruction for the detection of hypo-metabolic regions in epilepsy

Positron emission tomography (PET) of the cerebral glucose metabolism has shown to be useful in the presurgical evaluation of patients with epilepsy. Between seizures, PET images using fluorodeoxyglucose (FDG) show a decreased glucose metabolism in areas of the gray matter (GM) tissue that are associated with the epileptogenic region. However, detection of subtle hypo-metabolic regions is limited by noise in the projection data and the relatively small thickness of the GM tissue compared to the spatial resolution of the PET system. Therefore, we present an iterative maximum-a-posteriori based reconstruction algorithm, dedicated to the detection of hypo-metabolic regions in FDG-PET images of the brain of epilepsy patients. Anatomical information, derived from magnetic resonance imaging data, and pathophysiological knowledge was included in the reconstruction algorithm. Two Monte Carlo based brain software phantom experiments were used to examine the performance of the algorithm. In the first experiment, we used perfect, and in the second, imperfect anatomical knowledge during the reconstruction process. In both experiments, we measured signal-to-noise ratio (SNR), root mean squared (rms) bias and rms standard deviation. For both experiments, bias was reduced at matched noise levels, when compared to post-smoothed maximum-likelihood expectation-maximization (ML-EM) and maximum a posteriori reconstruction without anatomical priors. The SNR was similar to that of ML-EM with optimal post-smoothing, although the parameters of the prior distributions were not optimized. We can conclude that the use of anatomical information combined with prior information about the underlying pathology is very promising for the detection of subtle hypo-metabolic regions in the brain of patients with epilepsy.

Comparison between MAP and postprocessed ML for image reconstruction in emission tomography when anatomical knowledge is available

Previously, the noise characteristics obtained with penalized-likelihood reconstruction [or maximum a posteriori (MAP)] have been compared to those obtained with postsmoothed maximum-likelihood (ML) reconstruction, for emission tomography applications requiring uniform resolution. It was found that penalized-likelihood reconstruction was not superior to postsmoothed ML. In this paper, a similar comparison is made, but now for applications where the noise suppression is tuned with anatomical information. It is assumed that limited but exact anatomical information is available. Two methods were compared. In the first method, the anatomical information is incorporated in the prior of a MAP-algorithm and is, therefore, imposed during MAP-reconstruction. The second method starts from an unconstrained ML-reconstruction, and imposes the anatomical information in a postprocessing step. The theoretical analysis was verified with simulations: small lesions were inserted in two different objects, and noisy PET data were produced and reconstructed with both methods. The resulting images were analyzed with bias-noise curves, and by computing the detection performance of the nonprewhitening observer and a channelized Hotelling observer. Our analysis and simulations indicate that the postprocessing method is inferior, unless the noise correlations between neighboring pixels are taken into account. This can be done by applying a so-called prewhitening filter. However, because the prewhitening filter is shift variant and object dependent, it seems that MAP reconstruction is the more efficient method.

Comparison of diagnostic accuracy of (111)In-pentetreotide SPECT and (68)Ga-DOTATOC PET/CT: A lesion-by-lesion analysis in patients with metastatic neuroendocrine tumours.

AbstractObjectivesTo compare the diagnostic accuracy of 111In-pentetreotide-scintigraphy with 68Ga-DOTATOC-positron emission tomography (PET)/computed tomography (CT) in patients with metastatic-neuroendocrine tumour (NET) scheduled for peptide receptor radionuclide therapy (PRRT). Incremental lesions (ILs) were defined as lesions observed on only one modality.MethodsFifty-three metastatic-NET-patients underwent 111In-pentetreotide-scintigraphy (24 h post-injection; planar+single-photon emission CT (SPECT) abdomen) and whole-body 68Ga-DOTATOC-PET/CT. SPECT and PET were compared in a lesion-by-lesion and organ-by-organ analysis, determining the total lesions and ILs for both modalities.ResultsSignificantly more lesions were detected on 68Ga-DOTATOC-PET/CT versus 111In-pentetreotide-scintigraphy. More specifically, we observed 1,098 lesions on PET/CT (range: 1–105; median: 15) versus 660 on SPECT (range: 0–73, median: 9) (p<0.0001), with 439 PET-ILs (42/53 patients) and one SPECT-IL (1/53 patients). The sensitivity for PET/CT was 99.9 % (95 % CI, 99.3–100.0), for SPECT 60.0 % (95 % CI, 48.5–70.2). The organ-by-organ analysis showed that the PET-ILs were most frequently visualized in liver and skeleton.ConclusionGa-DOTATOC-PET/CT is superior for the detection of NET-metastases compared to 111In-pentetreotide SPECT.Key Points• Somatostatin receptor PET is superior to SPECT in detecting NET metastases • PET is the scintigraphic method for accurate depiction of NET tumour burden • The sensitivity of PET is twofold higher than the sensitivity of SPECT

Anatomy-based reconstruction of FDG-PET images with implicit partial volume correction improves detection of hypometabolic regions in patients with epilepsy due to focal cortical dysplasia diagnosed on MRI

PurposeDetection of hypometabolic areas on interictal FDG-PET images for assessing the epileptogenic zone is hampered by partial volume effects. We evaluated the performance of an anatomy-based maximum a-posteriori (A-MAP) reconstruction algorithm which combined noise suppression with correction for the partial volume effect in the detection of hypometabolic areas in patients with focal cortical dysplasia (FCD).MethodsFDG-PET images from 14 patients with refractory partial epilepsy were reconstructed using A-MAP and maximum likelihood (ML) reconstruction. In all patients, presurgical evaluation showed that FCD represented the epileptic lesion. Correspondence between the FCD location and regional metabolism on a predefined atlas was evaluated. An asymmetry index of FCD to normal cortex was calculated.ResultsHypometabolism at the FCD location was detected in 9/14 patients (64%) using ML and in 10/14 patients (71%) using A-MAP reconstruction. Hypometabolic areas outside the FCD location were detected in 12/14 patients (86%) using ML and in 11/14 patients (79%) using A-MAP reconstruction. The asymmetry index was higher using A-MAP reconstruction (0.61, ML 0.49, p=0.03).ConclusionThe A-MAP reconstruction algorithm improved visual detection of epileptic FCD on brain FDG-PET images compared to ML reconstruction, due to higher contrast and better delineation of the lesion. This improvement failed to reach significance in our small sample. Hypometabolism outside the lesion is often present, consistent with the observation that the functional deficit zone tends to be larger than the epileptogenic zone.

Anatomical based FDG-PET reconstruction for the detection of hypometabolic regions in epilepsy

Positron emission tomography (PET) of the cerebral glucose metabolism has shown to be useful in the presurgical evaluation of patients with epilepsy. An iterative reconstruction algorithm is derived for the detection of subtle hypometabolic regions in FDG-PET images of the brain of epilepsy patients. Prior anatomical information, derived from MR data, and pathophysiological knowledge was included in the reconstruction algorithm. Results showed an improved signal-to-noise ratio and a reduction of bias.

Evaluation of different MRI-based anatomical priors for PET brain imaging

Image reconstruction in emission tomography may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject. One way to implement this, is to use the anatomical image for defining the a-priori distribution in a maximum-a-posteriori reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate three different anatomical priors for PET brain imaging, using MRI for the anatomical image. The priors are: 1) a prior based on a segmentation of the MRI image; 2) the joint entropy prior; 3) a prior (proposed by Bowsher et al. [1]) that encourages smoothness within a position dependent neighborhood, computed from the MRI image. The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. The three priors produced a compromise between noise and bias that was significantly better than that obtained with post-smoothed MLEM. The performance of the joint entropy prior was slightly worse than that of the other two priors. In contrast to the joint entropy prior, the Bowsher prior is easily tuned and does not pose convergence problems due to local maxima.

Metabolic evidence for episodic memory plasticity in the nonepileptic temporal lobe of patients with mesial temporal epilepsy.

Purpose:  Metabolic changes have been described in the nonepileptic temporal lobe of patients with unilateral mesiotemporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS). To better understand the functional correlate of this metabolic finding, we have sought to characterize brain regions in patients with MTLE that show correlation between unilateral episodic memory performances, as assessed by intracarotid amobarbital test (IAT), and interictal regional cerebral metabolism measured by [18F]‐fluorodeoxyglucose positron emission tomography (FDG‐PET).