R. Van Heertum

SPECT perfusion imaging in the diagnosis of Alzheimer's disease: a clinical-pathologic study.

Objective: Numerous studies have suggested that temporoparietal hypoperfusion seen on brain imaging with SPECT may be useful in diagnosing AD during life. However, these studies have often been limited by lack of pathologic validation and unrepresentative samples. The authors performed this study to determine whether SPECT imaging provides diagnostically useful information in addition to that obtained from a clinical examination. Methods: Clinical data and SPECT images were collected prospectively, and patients were followed to autopsy. Clinical history, pathologic findings, and SPECT images were each evaluated by raters blind to other features, and clinical and SPECT diagnoses were compared with pathologic diagnoses. The study population consisted of 70 patients with dementia, followed to autopsy; 14 controls followed to autopsy; and 71 controls (no autopsy performed). The primary outcome was the likelihood of a pathologic diagnosis of AD given a positive clinical diagnosis, a positive SPECT diagnosis, and both. Results: When all participants (patients and controls) were included in the analysis, the clinical diagnosis of “probable” AD was associated with an 84% likelihood of pathologic AD. A positive SPECT scan raised the likelihood of AD to 92%, whereas a negative SPECT scan lowered the likelihood to 70%. SPECT was more useful when the clinical diagnosis was “possible” AD, with the likelihood of 67% without SPECT, 84% with a positive SPECT, and 52% with a negative SPECT. Similar results were found when only patients with dementia were included in the analysis. Conclusions: In the evaluation of dementia, SPECT imaging can provide clinically useful information indicating the presence of AD in addition to the information that is obtained from clinical evaluation.

Different brain networks mediate task performance in normal aging and AD Defining compensation

Objective: To determine whether the pathologic mechanisms of AD alter the brain networks subserving performance of a verbal recognition task. Background: Functional imaging studies comparing task-related activation in AD patients and controls generally have not used network analysis and have not controlled for task difficulty. Methods: H215O PET was used to measure regional cerebral blood flow in 14 patients and 11 healthy elders during the performance of a serial verbal recognition task under two conditions: low demand, with study list size (SLS) equal to one; and titrated demand, with SLS adjusted so that each subject recognized words at 75% accuracy. The Scaled Subprofile Model was used to identify networks of regionally covarying activity across these task conditions. Results: In the elders, higher SLS was associated with the recruitment of a network of brain areas involving left anterior cingulate and anterior insula (R2 = 0.94; p < 0.0001). Three patients also expressed this network. In the remaining patients, higher SLS was associated with the recruitment of an alternate network consisting of left posterior temporal cortex, calcarine cortex, posterior cingulate, and the vermis (R2 = 0.81, p < 0.001). Expression of this network was unrelated to SLS in the elders and more intact AD patients. Conclusions: The patients’ use of the alternate network may indicate compensation for processing deficits. The transition from the normal to the alternate network may indicate a point where brain disease has irreversibly altered brain function and thus may have important implications for therapeutic intervention.

An improved method for voxel-based partial volume correction in PET and SPECT

The limited spatial resolution of PET and SPECT leads to partial volume effects (PVE) that limit the quantification accuracy of these modalities. Partial volume correction (PVC) methods have been developed in the past utilizing high-resolution MRI images in combination with the known point-spread function (PSF) of the system. These methods can be broadly divided into voxel-based and volume of interest (VOI)-based methods. The voxel-based approach (Meltzer et al., JCAT, 1990, 14: 561–70) is based on segmentation of the MRI images into regions corresponding to gray matter (GM), white matter (WM) and cerebrospinal fluid. The correction is performed by assuming a uniform distribution within each region. A separate estimate of the WM value is required, and corrected values are obtained for voxels in the GM region only (target region). We have developed a new voxel-based PVC method in which a larger number of regions can be used, thereby reducing any bias introduced by the uniformity assumption. Also, with our new method, no estimate of the WM value is needed, and corrected values are obtained for each voxel in the whole image — not just one target region. Our new method (Multi-Target Correction (MTC)) involves an initial step using a VOI-based PVC method (Rousset et al. JNM, 1998, 39: 904–11). For the purpose of evaluation, simulated PET images were generated using a digital brain phantom, assuming a Gaussian PSF with 6 mm FWHM. The sensitivity of the method to various factors, such as errors in PSF determination, PET/MRI co-registration, and MRI segmentation, was determined. In addition, we examined the effects of tissue heterogeneity and noise. MTC was also applied to a PET study performed on a cylindrical phantom with 6 spherical inserts (diameters: 10, 12, 16, 20, 25, 32 mm, insert-to-background ratio 6) using an ECAT HR+ scanner (Siemens, Knoxville, TN, USA). MTC was performed using 7 VOIs. The simulations showed that MTC was reasonably robust with respect to errors in FWHM ( 2 mm), co-registration errors (<4.5 mm, or <6-), GM region dilation, as well as to region heterogeneity and noise. It was, however, quite sensitive to GM region erosion. Results from the phantom study are presented in the Fig. 1 below, showing that the whole image is corrected with improved contrast and definition of all the inserts. Further evaluations with human PET data are required before MTC can be used routinely.

Fusion of brushlet and wavelet denoising methods for nuclear images

This paper presents preliminary results on the fusion of denoised PET and SPECT data volumes from brushlet and wavelet thresholding methods. Texture-based brushlet denoising is well suited for enhancement of physiological information while wavelet-based denoising is better suited for enhancement of anatomical contours. A three-dimensional multiscale edge-based data fusion algorithm is applied to combine enhanced data from these two independent denoising methods. Preliminary results with qualitative evaluation of PET and SPECT data by an expert clinician showed great potential of this approach to combine enhancement of both anatomical and physiological signal information for improved image quality.

Using bootstrap identifiability as a metric for model selection for dynamic [/sup 11/C]DASB PET data

Numerous tracer kinetic models have been developed for estimation of neuroreceptor binding parameters from dynamic PET and SPECT brain studies. We have used the bootstrap technique to determine the variability of the parameter estimation as an aid in selecting the most appropriate kinetic model to use. This technique made it possible to take into account different sources of variability. We applied the method to data from 11 healthy subjects, each one scanned twice with the PET serotonin transporter ligand [11C]DASB. Tracer binding was quantified for different brain regions by kinetic analysis, based on metabolite corrected arterial plasma input functions. Six different analysis methods were used, including iterative as well as non-iterative implementations of 1- and 2-tissue compartmental models (1TC, 2TC, 1TCNI, 2TCNI), likelihood estimation in graphical analysis (LEGA), and basis pursuit (Basis). We applied the bootstrap technique to the PET data, as well as to the plasma and metabolite data. Standard errors (SE) were calculated for the total volume distribution (VT), as well as different binding potential estimates. The average and standard deviation (SD) of the estimated SE values were calculated across subjects. For comparison, we also estimated the variability of the outcome measures by bootstrapping only the tissue data. The results of the full bootstrap analysis showed that Basis was in general the best method. However, when only the tissue data were bootstrapped, the results indicated that 1TCNI was best. This shows that it can be important to take into account all sources of variability when using bootstrap identifiability for model selection

A new rebinning algorithm for 3D PET data

3D acquisition mode in PET is a way to increase the sensitivity of the scanner. To reconstruct the 3D data it is necessary to use either a fully 3D reconstruction algorithm or, alternatively, a rebinning algorithm followed by 2D reconstruction. The advantages of the 2nd approach are higher speed and reduced data size. Rebinning algorithms may become more important in the future with time-of-flight PET due to the increased dimensionality of the projection data. Although two exact rebinning algorithms have been developed, the most commonly used method today is the approximate Fourier rebinning algorithm. These three methods are based on analytic solutions in the frequency domain and involve geometric approximations, which may be significant for some scanner geometries. They also require attenuation corrected data, which can lead to complications since some reconstruction algorithms work better with non-attenuation corrected data. We have developed a novel rebinning algorithm, called reprojection rebinning (RP-RB), which is free from theoretical and geometric approximations as well as from the requirement of attenuation correction. It is based on an initial reconstruction of a 2D data-subset, which results in a somewhat sub-optimal utilization of the oblique data. The results can be improved, however, by an iterative procedure. We have tested RP-RB using simulated phantom data, showing an SD reduction in the central transaxial plane after the 1st, 2nd and 3rd iteration by a factor of 3.0, 3.5 and 4.1, respectively, as compared to 2D data only. There was also a very good spatial accuracy in images reconstructed from noiseless data, even for scanner geometries with large acceptance angles.

Widespread deficiency in brain serotonin transporter binding in bipolar disorder on positron emission tomography

Bipolar depression is clinically indistinguishable from that observed in Major Depressive Disorder. Localized deficiencies in serotonin transporters (5-HTT) binding on PET or SPECT are reported for major depressive disorder. However, research on alterations in serotonergic neurotransmission in bipolar depression remains scant. This study used Positron Emission Tomography and [11C](+)-McN 5652 with a metabolite corrected arterial function to determine in vivo brain 5-HTT binding potential (BP1, proportional to 5-HTT number) in a sample of medication-free bipolar depressed subjects (n = 18) compared to healthy volunteers (n = 41). Subjects were genotyped for a functional polymorphism in the serotonin transporter gene promotor assessing the triallelic 5-HTTLPR polymorphism. The relationship between 5-HTT binding and genotype was examined.

PET Studies of the Effects of ECT on Cerebral Physiology

ECT is a remarkably effective treatment for major depressive disorder [MDD]. Neuroimaging studies of rCBF and rCMR are helpful in addressing mechanisms of therapeutic effects. We have reported that reductions in rCBF in prefrontal regions are associated with positive response to ECT[1], and that a course of bilateral ECT is associated with marked reduction in prefrontal rCMR[2]. We now report on a new study of patients receiving ECT for MDD. This study provides the first comparison of the relative acute effects of seizures on both rCBF and rCMR.