Biomedical subjects

Lothar Spies

Publications and source records attributed to Lothar Spies.

3 recordsLinked to original sources

PubMed2006

Ecstasy-induced reduction of the availability of the brain serotonin transporter as revealed by [11C](+)McN5652-PET and the multi-linear reference tissue model: loss of transporters or artifact of tracer kinetic modelling?

In a previous positron emission tomography (PET) study with the serotonin transporter (SERT) ligand [(11)C](+)McN5652, we found protracted reduction of the availability of the brain SERT in users of the drug ecstasy. However, the multi-linear reference tissue method for the quantification of SERT availability used in this study is prone to effects of altered levels of statistical noise that could simulate reduction of SERT. The aim of the present study was to take into account this confound by re-evaluation of the data now using a modelling approach that is rather insensitive to alterations in the level of statistical noise. A total of 116 subjects (30 current, 29 former ecstasy users, 29 drug-naive, 28 polydrug controls) in whom [(11)C](+)McN5652-PET had been performed previously were re-evaluated. The equilibrium specific-to-non-specific partition coefficient V"( 3) was obtained voxel-wise by application of the simplified reference tissue method (SRTM), which provides quite unbiased results up to rather large noise levels. Voxel-based comparisons between the groups were performed using statistical parametric mapping. V"(3) was reduced in the striatum and in the thalamus in current ecstasy users. This was confirmed by volume-of-interest-based analysis. This result suggests that the ecstasy-induced reduction of SERT availability in SERT-rich brain regions reported previously indicates reduced SERT binding potential rather than being an artifact of tracer kinetic modelling. SRTM analysis did not confirm previous findings in neocortical brain areas.

Adult↗

PubMed2006

The impact of dental metal artifacts on head and neck IMRT dose distributions.

BACKGROUND AND PURPOSES: To quantify the cold or hot spot induced in IMRT treatment plans due to the presence of metal artifact in CT image data sets stemming from dental work. PATIENTS AND METHODS: Metal artifact corrected image data sets of five patients have been analyzed. IMRT plans were generated using five different planning image data sets: (a) uncorrected (UC) (b) homogeneous uncorrected (HUC), (c) sinogram completion corrected (SCC), (d) minimum value corrected (MVC), and (e) image set (d) subsequently corrected with a streak artifacts reduction algorithm (SAR-MVC). The SAR-MVC data set is assumed to be the closest approximation to the absence of metal artifacts and has therefore been taken as the reference image data set. An IMRT plan was generated for each of the image datasets (a)-(e). The resulting IMRT treatment plans for data sets (a)-(d) were then projected onto the reference data set (e) and recalculated. The reference dose distribution (e) was then subtracted from these recalculated dose distributions. Using dose difference analysis, the cold and hot spots in organs at risk (OARs) and the target volumes (TVs) were quantified. RESULTS: When compared to the reference dose distribution, the UC, HUC, and SCC plans exhibited hot spots showing on average more than 1.0 Gy hot dose in the left and right parotids. For the UC, HUC, and SCC recalculated plans, subvolumes of the clinical target volumes (CTV) were under dosed on average by more than 0.9 Gy. On the other hand, the MVC plan showed less than 0.3 Gy hot dose in both parotids, and the cold dose in the CTVs were reduced by up to 0.8 Gy. CONCLUSIONS: The presence of dental metal artifacts in head and neck planning CT data sets can lead to relative hot spots in OARs and relative cold spots in regions of the TVs when compared to the reference data set that more closely approximates the patient anatomy. This effect can be reduced if a simple minimum value correction (MVC) method for the dental metal artifacts is employed.

Artifacts↗

PubMed2006

Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering.

High-density objects such as metal prostheses, surgical clips, or dental fillings generate streak-like artifacts in computed tomography images. We present a novel method for metal artifact reduction by in-painting missing information into the corrupted sinogram. The information is provided by a tissue-class model extracted from the distorted image. To this end the image is first adaptively filtered to reduce the noise content and to smooth out streak artifacts. Consecutively, the image is segmented into different material classes using a clustering algorithm. The corrupted and missing information in the original sinogram is completed using the forward projected information from the tissue-class model. The performance of the correction method is assessed on phantom images. Clinical images featuring a broad spectrum of metal artifacts are studied. Phantom and clinical studies show that metal artifacts, such as streaks, are significantly reduced and shadows in the image are eliminated. Furthermore, the novel approach improves detectability of organ contours. This can be of great relevance, for instance, in radiation therapy planning, where images affected by metal artifacts may lead to suboptimal treatment plans.

Algorithms↗