Technetium tc-99m exametazime

Ictal brain perfusion SPECT with the tracer 99mTc-HMPAO or 99mTc-ECD is widely used for identification of the epileptic seizure onset zone (SOZ) in presurgical evaluation if standard pointers are uncertain or inconsistent. For both tracers, there are theoretical arguments to favor it over the other for this task. The aim of this study was to compare the performance of ictal brain perfusion SPECT between 99mTc-HMPAO and 99mTc-ECD in a rather large patient sample.

The study retrospectively included 196 patients from clinical routine in whom ictal perfusion SPECT had been performed with stabilized 99mTc-HMPAO (n = 110) or 99mTc-ECD (n = 86). Lateralization and localization of the SOZ were obtained by the consensus of 2 independent readers based on visual inspection of the SPECT images.

The 99mTc-HMPAO group and the 99mTc-ECD group were well matched with respect to age, sex, age at first seizure, duration of disease, seizure frequency, history of previous brain surgery, and findings of presurgical MRI. The proportion of lateralizing ictal SPECT did not differ significantly between 99mTc-HMPAO and 99mTc-ECD (65.5% vs 72.1%, P = 0.36). Sensitivity of ictal perfusion SPECT (independent of the tracer) for correct localization of the SOZ in 62 patients with temporal lobe epilepsy and at least worthwhile improvement (Engel scale ≤ III) 12 months after temporal epilepsy surgery was 63%.

This study does not provide evidence to favor 99mTc-HMPAO or 99mTc-ECD for identification of the SOZ by ictal perfusion SPECT.

In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the regional activity concentration per unit volume (kBq.ml−1). This correction generally requires an attenuation map (μ map) denoting the attenuation coefficient at each voxel which is often derived from a CT or MRI scan. However, such an additional scan is not always available and the method may suffer from registration errors. Therefore, we propose a SPECT-only-based strategy for μ map estimation that we apply to a stationary multi-pinhole clinical SPECT system (G-SPECT-I) for 99mTc-HMPAO brain perfusion imaging. The method is based on the use of a convolutional neural network (CNN) and was validated with Monte Carlo simulated scans. Data acquired in list mode was used to employ the energy information of both primary and scattered photons to obtain information about the tissue attenuation as much as possible. Multiple SPECT reconstructions were performed from different energy windows over a large energy range. Locally extracted 4D SPECT patches (three spatial plus one energy dimension) were used as input for the CNN which was trained to predict the attenuation coefficient of the corresponding central voxel of the patch. Results show that Attenuation Correction using the Ground Truth μ maps (GT-AC) or using the CNN estimated μ maps (CNN-AC) achieve comparable accuracy. This was confirmed by a visual assessment as well as a quantitative comparison; the mean deviation from the GT-AC when using the CNN-AC is within 1.8% for the standardized uptake values in all brain regions. Therefore, our results indicate that a CNN-based method can be an automatic and accurate tool for SPECT attenuation correction that is independent of attenuation data from other imaging modalities or human interpretations about head contours.