Automatic Trajectory Planning for Deep Brain Stimulation: A Feasibility Study

Abstract. DBS for Parkinson’s disease involves an extensive planning to find a suitable electrode implantation path to the selected target. We have investigated the feasibility of improving the conventional planning with an automatic calculation of possible paths in 3D. This requires the segmentation of anatomical structures. Subsequently, the paths are calculated and visualized. After selection of a suitable path, the settings for the stereotactic frame are determined. A qualitative evaluation has shown that automatic avoidance of critical structures is feasible. The participating neurosurgeons estimate the time gain to be around 30 minutes.

External validation of deep learning-based contouring of head and neck organs at risk

Contains fulltext : 225324.pdf (publisher's version ) (Open Access

In vivo high-resolution structural imaging of large arteries in small rodents using two-photon laser scanning microscopy

In vivo (molecular) imaging of the vessel wall of large arteries at subcellular resolution is crucial for unraveling vascular pathophysiology. We previously showed the applicability of two-photon laser scanning microscopy (TPLSM) in mounted arteries ex vivo. However, in vivo TPLSM has thus far suffered from in-frame and between-frame motion artifacts due to arterial movement with cardiac and respiratory activity. Now, motion artifacts are suppressed by accelerated image acquisition triggered on cardiac and respiratory activity. In vivo TPLSM is performed on rat renal and mouse carotid arteries, both surgically exposed and labeled fluorescently (cell nuclei, elastin, and collagen). The use of short acquisition times consistently limit in-frame motion artifacts. Additionally, triggered imaging reduces between-frame artifacts. Indeed, structures in the vessel wall (cell nuclei, elastic laminae) can be imaged at subcellular resolution. In mechanically damaged carotid arteries, even the subendothelial collagen sheet (similar to 1 mu m) is visualized using collagen-targeted quantum dots. We demonstrate stable in vivo imaging of large arteries at subcellular resolution using TPLSM triggered on cardiac and respiratory cycles. This creates great opportunities for studying (diseased) arteries in vivo or immediate validation of in vivo molecular imaging techniques such as magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET)

Regions significantly connected to the <i>left</i> STN based on structural and functional connectivity measures.

<p>Regions significantly connected to the <i>left</i> STN. The -value for the structural connectivity (SC) was calculated using a -test on , using  = 8. The -score and cluster extent (in voxels) for the functional connectivity (FC) were determined using correlations with the 10 atlas-based STN ROIs. Here only the regions with or significant functional connectivity are shown. For the latter cases, the -value for structural connectivity was added if lower than 0.050.</p

Significant functional connectivity clusters for (a) the right and (b) the left atlas-based STN ROIs, shown on three coronal slices of the MNI152 template.

<p>The yellow lines on the axial image on the left-hand side show the position of the coronal slices. Red clusters exhibit positive regression coefficients, while blue clusters yield negative coefficients.</p

MRI parameters.

<p>Parameters of MRI sequences used for this study.</p

Functional connectivity per STN voxel in atlas space after applying the reverse regression procedure.

<p>(a) Connectivity to motor areas per voxel of the left and right STN, cumulated over all subjects. (b) Connectivity to limbic areas per voxel of the left and right STN, cumulated over all subjects. Each sphere in (a) and (b) represents one voxel and is color-coded by functional connectivity: dark red means low connectivity, while yellow means high connectivity.</p

Regions significantly connected to the <i>right</i> STN based on structural and functional connectivity measures.

<p>Regions significantly connected to the <i>right</i> STN. The -value for the structural connectivity (SC) was calculated using a -test on , using  = 8. The -score and cluster extent (in voxels) for the functional connectivity (FC) were determined using correlations with the 10 atlas-based STN ROIs. Here only the regions with or significant functional connectivity are shown. For the latter cases, the -value for structural connectivity was added if lower than 0.050.</p

Flowchart of data analysis steps for structural and functional connectivity.

<p>Flowchart of data analysis steps for structural and functional connectivity.</p