Image correlation methods enable the complementary use of information from different medical images of a patient. These images can be obtained from different imaging devices (CT, MR, PET), or, from one imaging device taken at different times. Unfortunately, there are few cases in which the requirements for later image correlation are taken into account at the time of image acquisition. There is therefore a need for correlation techniques requiring no preparation in advance. We have developed two correlation methods, both based on three or more anatomical or artificial landmarks, to be defined in corresponding image data sets. These methods have been evaluated with phantom data as well as with patient data. We have improved these correlation methods by using more landmarks and special selection criteria. They are applicable to all medical tomograms and to x-ray pictures taken under stereotactical conditions. The results obtained have error ranges in the order of the three-dimensional image resolution.
"The location of the STN was determined based on Schaltenbrand–Wahren-atlas (SWA) (Schaltenbrand and Wahren, 1977) coordinates, stereotactic cranial computed tomography (CT), stereotactic high resolution magnetic resonance imaging (MRI; T1- and T2-weighted), fused images of both and on visual inspection (Ende et al., 1992; Voges et al., 2002). The target, i.e. the most distal contact of the quadripolar brain electrode, was initially determined anatomically (dorsolateral end of the STN contour) and was intraoperatively adjusted according to the results of microelectrode recordings (based on the transition from the STN-specific signal to a signal indicating substantia nigra pars reticulata activity; see also Reck et al., 2009). "
[Show abstract][Hide abstract] ABSTRACT: Under rest condition, beta-band (13-30Hz) activity in patients with Parkinson's disease (PD) is prominent in the subthalamic nucleus (STN). However, the beta-band coupling between STN and muscle activity, its distribution and relation to motor symptoms remains unclear.
Using up to five electrodes, we recorded local field potentials (LFPs) above (zona incerta, ZI) and within the STN at different recording heights in 20 PD patients during isometric contraction. Simultaneously, we registered activity of the contralateral flexor and extensor muscle. We analysed LFP-EMG coherence to estimate coupling in the frequency domain.
Coherence analysis showed beta-associated coupling in the ZI and STN with more significant LFP-EMG coherences in the STN. Coherence varied depending on the localisation of the LFP and muscles. We found significant difference between coherence of the extensor and the flexor muscle to the same LFP (p=0.045).
We demonstrated that coherence between beta-band oscillations and forearm muscles are differentially distributed in the subthalamic region and between the forearm muscles in Parkinson's disease during isometric contraction. However, the significant LFP-EMG coupling did not associate with motor deficits in PD patients.
The differential distribution of beta-band activity in the STN highlights the importance of a topographically distinct therapeutic modulation.
Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 07/2009; 120(8):1601-9. DOI:10.1016/j.clinph.2009.05.018 · 3.10 Impact Factor
"In the field of image registration (Toga 1999), there are landmark-based methods that bring image volumes from different imaging devices into spatial alignment for image-guided neurosurgery (Peters et al. 1996) and therapy planning, as well as for visualization and quantitative analysis. The anatomical point landmarks are usually identified by the user interactively (Hill et al. 1991; Ende et al. 1992; Fang et al. 1996; Strasters et al. 1997), but there are approaches that create surfaces from brain image volumes and then use differential geometry to automatically extract extremal points (Thirion 1994; Pennec et al. 2000). These methods usually minimize the distances between corresponding point landmarks with a least-squares approach (Arun et al. 1987) or an iterative transformation application (Besl and McKay 1992). "
[Show abstract][Hide abstract] ABSTRACT: Many brain image processing algorithms require one or more well-chosen seed points because they need to be initialized close to an optimal solution. Anatomical point landmarks are useful for constructing initial conditions for these algorithms because they tend to be highly-visible and predictably-located points in brain image scans. We introduce an empirical training procedure that locates user-selected anatomical point landmarks within well-defined precisions using image data with different resolutions and MRI weightings. Our approach makes no assumptions on the structural or intensity characteristics of the images and produces results that have no tunable run-time parameters. We demonstrate the procedure using a Java GUI application (LONI ICE) to determine the MRI weighting of brain scans and to locate features in T1-weighted and T2-weighted scans.
"Consequently, global rigid transformations (three-dimensional (3D) rotation, 3D translation , and 3D uniform scaling) would be performed. 2) No external markers would be required [Ende et al., 1991; Fright and Linney, 1993; Loats, 1993; Grabowski et al., 1995], which would allow retrospective registration . 3) No constraint would exist concerning the orientation, resolution, slice thickness or spacing, and total number of slices in each study. "
[Show abstract][Hide abstract] ABSTRACT: We present a robust intrasubject registration method for the synergistic use of multiple neuroimaging modalities, with applications to magnetic resonance imaging (MRI), functional MRI, perfusion MRI, MR spectroscopy, and single-photon emission computed tomography (SPECT). This method allows user-friendly processing of difficult examinations (low spatial resolution, advanced pathology, motion during acquisition, and large areas of focal activation). Registration of three-dimensional (3D) brain scans is initially estimated by first-order moment matching, followed by iterative anisotrophic chamfer matching of brain surfaces. Automatic brain surface extraction is performed in all imaging modalities. A new generalized distance definition and new specific methodologies allow registration of scans that cover only a limited range of brain surface. A new semiautomated supervision scheme allows fast and intuitive corrections of possible false automatic registration results. The accuracy of the MRI/SPECT anatomical-functional correspondence obtained was evaluated using simulations and two difficult clinical populations (tumors and degenerative brain disorders). The average discrimination capability of SPECT (12.4 mm in-plane resolution, 20 mm slice thickness) was found to be better than 5 mm after registration with MRI (5 mm slice thickness). Registration accuracy was always better than imaging resolution. Complete 3D MRI and SPECT registration time ranged between 6-11 min, in which surface matching represented 2-3 min. No registration failure occurred. In conclusion, the application of several new image processing techniques allowed efficient and robust registration. Hum. Brain Mapping 5:3-17, 1997. (c) 1997 Wiley-Liss, Inc.
Human Brain Mapping 01/1997; 5(1):3-17. DOI:10.1002/(SICI)1097-0193(1997)5:1<3::AID-HBM2>3.0.CO;2-7 · 5.97 Impact Factor
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