RS
R.R. Schreuder
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The brain and central nervous system handle waste transport differently from the rest of the body. The pathways through which waste in the brain is transported is still a debated topic. The glymphatic system describes a pathway from the perivascular spaces around veins and arteries to the subarachnoid space. This pathway is filled with Cerebrospinal Fluid (CSF) and exploring this fluid and the orientation of the flow relative to the blood vessels is essential to get new insight into the waste transport and related neurological issues.
We analyze this system using a non-invasive diffusion weighted MRI sequencing technique called CSF-STREAM, which generates a DTI-like tensor field called CSF-Mobility, allowing us to track the CSF. The tensor data can be used to analyze the flow of the CSF by applying eigen-decomposition on the tensor matrix. We derive a vector field from the tensors' principal eigenvectors that represents the general flow of CSF.
We introduce a workflow aimed at analysis, comparison and interpretation of the CSF data. It begins by selecting a region that includes a vessel and the surrounding CSF. The vessel is segmented from the region of interest (ROI) and its centerline is extracted to represent the vessel's orientation. The vector field then visualized using a hedgehog plot with a color mapping that indicates the relative orientation, as well as with a streamlines visualization. The streamlines are restricted to the vicinity of the vessel by generating seed points within a spherical radius surrounding the centerline. To make the visualizations easier to interpret, we apply two different transformations on the vector field. A straightening transformation is applied to the vector field by using straightened Curved Planar Reformation (CPR) on the vessel centerline. An additional unfolding transformation is introduced in which the vectors are rotated around the straightened centerline, creating a cross-sectional view of the vector field. We conduct a user study to evaluate the workflow and compare the visualization methods and to to see which methods work best for interpreting the relation between the flow orientation and the vessels. Results show that the workflow and visualizations are indeed suitable techniques and that the straightening of the vector field along the vessel makes it easier to interpret the data, while the unfolding transformation makes the context too complex to understand with limited time and explanation. Overall, the workflow and tool set have the capability to give researchers more insight into the waste transport in the brain. ...
We analyze this system using a non-invasive diffusion weighted MRI sequencing technique called CSF-STREAM, which generates a DTI-like tensor field called CSF-Mobility, allowing us to track the CSF. The tensor data can be used to analyze the flow of the CSF by applying eigen-decomposition on the tensor matrix. We derive a vector field from the tensors' principal eigenvectors that represents the general flow of CSF.
We introduce a workflow aimed at analysis, comparison and interpretation of the CSF data. It begins by selecting a region that includes a vessel and the surrounding CSF. The vessel is segmented from the region of interest (ROI) and its centerline is extracted to represent the vessel's orientation. The vector field then visualized using a hedgehog plot with a color mapping that indicates the relative orientation, as well as with a streamlines visualization. The streamlines are restricted to the vicinity of the vessel by generating seed points within a spherical radius surrounding the centerline. To make the visualizations easier to interpret, we apply two different transformations on the vector field. A straightening transformation is applied to the vector field by using straightened Curved Planar Reformation (CPR) on the vessel centerline. An additional unfolding transformation is introduced in which the vectors are rotated around the straightened centerline, creating a cross-sectional view of the vector field. We conduct a user study to evaluate the workflow and compare the visualization methods and to to see which methods work best for interpreting the relation between the flow orientation and the vessels. Results show that the workflow and visualizations are indeed suitable techniques and that the straightening of the vector field along the vessel makes it easier to interpret the data, while the unfolding transformation makes the context too complex to understand with limited time and explanation. Overall, the workflow and tool set have the capability to give researchers more insight into the waste transport in the brain. ...
The brain and central nervous system handle waste transport differently from the rest of the body. The pathways through which waste in the brain is transported is still a debated topic. The glymphatic system describes a pathway from the perivascular spaces around veins and arteries to the subarachnoid space. This pathway is filled with Cerebrospinal Fluid (CSF) and exploring this fluid and the orientation of the flow relative to the blood vessels is essential to get new insight into the waste transport and related neurological issues.
We analyze this system using a non-invasive diffusion weighted MRI sequencing technique called CSF-STREAM, which generates a DTI-like tensor field called CSF-Mobility, allowing us to track the CSF. The tensor data can be used to analyze the flow of the CSF by applying eigen-decomposition on the tensor matrix. We derive a vector field from the tensors' principal eigenvectors that represents the general flow of CSF.
We introduce a workflow aimed at analysis, comparison and interpretation of the CSF data. It begins by selecting a region that includes a vessel and the surrounding CSF. The vessel is segmented from the region of interest (ROI) and its centerline is extracted to represent the vessel's orientation. The vector field then visualized using a hedgehog plot with a color mapping that indicates the relative orientation, as well as with a streamlines visualization. The streamlines are restricted to the vicinity of the vessel by generating seed points within a spherical radius surrounding the centerline. To make the visualizations easier to interpret, we apply two different transformations on the vector field. A straightening transformation is applied to the vector field by using straightened Curved Planar Reformation (CPR) on the vessel centerline. An additional unfolding transformation is introduced in which the vectors are rotated around the straightened centerline, creating a cross-sectional view of the vector field. We conduct a user study to evaluate the workflow and compare the visualization methods and to to see which methods work best for interpreting the relation between the flow orientation and the vessels. Results show that the workflow and visualizations are indeed suitable techniques and that the straightening of the vector field along the vessel makes it easier to interpret the data, while the unfolding transformation makes the context too complex to understand with limited time and explanation. Overall, the workflow and tool set have the capability to give researchers more insight into the waste transport in the brain.
We analyze this system using a non-invasive diffusion weighted MRI sequencing technique called CSF-STREAM, which generates a DTI-like tensor field called CSF-Mobility, allowing us to track the CSF. The tensor data can be used to analyze the flow of the CSF by applying eigen-decomposition on the tensor matrix. We derive a vector field from the tensors' principal eigenvectors that represents the general flow of CSF.
We introduce a workflow aimed at analysis, comparison and interpretation of the CSF data. It begins by selecting a region that includes a vessel and the surrounding CSF. The vessel is segmented from the region of interest (ROI) and its centerline is extracted to represent the vessel's orientation. The vector field then visualized using a hedgehog plot with a color mapping that indicates the relative orientation, as well as with a streamlines visualization. The streamlines are restricted to the vicinity of the vessel by generating seed points within a spherical radius surrounding the centerline. To make the visualizations easier to interpret, we apply two different transformations on the vector field. A straightening transformation is applied to the vector field by using straightened Curved Planar Reformation (CPR) on the vessel centerline. An additional unfolding transformation is introduced in which the vectors are rotated around the straightened centerline, creating a cross-sectional view of the vector field. We conduct a user study to evaluate the workflow and compare the visualization methods and to to see which methods work best for interpreting the relation between the flow orientation and the vessels. Results show that the workflow and visualizations are indeed suitable techniques and that the straightening of the vector field along the vessel makes it easier to interpret the data, while the unfolding transformation makes the context too complex to understand with limited time and explanation. Overall, the workflow and tool set have the capability to give researchers more insight into the waste transport in the brain.
In this paper, a method is proposed to artificially expand the dynamic range of screens with a limited dynamic range. This research is linked to a new film-making technology where, instead of using a green screen, the background of a scene is displayed on a screen in real time using a computer generated background. This provides real time lighting in the studio; however, due to the limited dynamic range of the screen, it can not fully replicate the brightness of light sources. Overcoming this problem involves capturing and synchronize frames that each display a small section of the wider dynamic range, defined as illumination maps. The method uses a pipeline in which the illumination maps are displayed on a monitor in a grouped order, which are then captured with a camera. The recording is processed by labeling the frames and selecting key frames. The key frames are then additively combined with compatible illumination maps, which result in a video of the full dynamic range.
A program was developed as a proof of concept, providing expected results. For various recording inputs, It was also found that the implemented program discarded a lot of the frames of the recordings. A variation of the proposed method also yielded a slight speed-up, for practically the same results.
The proposed method provides a good starting point tackling the problem of artificially extending the dynamic range. The program used is a step in the right direction, but has flaws that limit its usefulness.
...
A program was developed as a proof of concept, providing expected results. For various recording inputs, It was also found that the implemented program discarded a lot of the frames of the recordings. A variation of the proposed method also yielded a slight speed-up, for practically the same results.
The proposed method provides a good starting point tackling the problem of artificially extending the dynamic range. The program used is a step in the right direction, but has flaws that limit its usefulness.
...
In this paper, a method is proposed to artificially expand the dynamic range of screens with a limited dynamic range. This research is linked to a new film-making technology where, instead of using a green screen, the background of a scene is displayed on a screen in real time using a computer generated background. This provides real time lighting in the studio; however, due to the limited dynamic range of the screen, it can not fully replicate the brightness of light sources. Overcoming this problem involves capturing and synchronize frames that each display a small section of the wider dynamic range, defined as illumination maps. The method uses a pipeline in which the illumination maps are displayed on a monitor in a grouped order, which are then captured with a camera. The recording is processed by labeling the frames and selecting key frames. The key frames are then additively combined with compatible illumination maps, which result in a video of the full dynamic range.
A program was developed as a proof of concept, providing expected results. For various recording inputs, It was also found that the implemented program discarded a lot of the frames of the recordings. A variation of the proposed method also yielded a slight speed-up, for practically the same results.
The proposed method provides a good starting point tackling the problem of artificially extending the dynamic range. The program used is a step in the right direction, but has flaws that limit its usefulness.
A program was developed as a proof of concept, providing expected results. For various recording inputs, It was also found that the implemented program discarded a lot of the frames of the recordings. A variation of the proposed method also yielded a slight speed-up, for practically the same results.
The proposed method provides a good starting point tackling the problem of artificially extending the dynamic range. The program used is a step in the right direction, but has flaws that limit its usefulness.