Adalca at 15:38, 7 December 2015

2015-12-07T15:38:22Z

Adalca: Created page with 'NOTOC Image:PW-MIT2015.png|Projects List Image:WMH_T1.png|Clinical Stroke Image ==Key Investigators== - Adrian Dal…'

2015-12-07T15:34:40Z

Created page with '__NOTOC__ <gallery> Image:PW-MIT2015.png|Projects List Image:WMH_T1.png|Clinical Stroke Image </gallery> ==Key Investigators== - Adrian Dal…'

New page

__NOTOC__
<gallery>
Image:PW-MIT2015.png|[[2015_Winter_Project_Week#Projects|Projects List]]
Image:WMH_T1.png|Clinical Stroke Image
</gallery>

==Key Investigators==
- Adrian Dalca, Katie Bouman, Polina Golland, MIT

==Project Description==
<div style="margin: 20px;">
Most synthesis, in-painting or super-resolution methods require a training dataset which includes the desired-quality images. Unfortunately, in the clinical setting this is often now available.

Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, analysis, will fail.

To improve results for large datasets of clinical-quality data, we are investigating restoration methods without training datasets. Here, we are using a patch-based Gaussian Mixture Model approach with MRF priors and utilizing only the current dataset, without an external training dataset.

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* We will investigate a current model for learning and using Patch-based Gaussian Mixture Model to restore sparse-slice data..
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
</div>
</div>

@@ Line 6: / Line 6: @@
 ==Key Investigators==
-- Adrian Dalca, Katie Bouman, Polina Golland, MIT
+- Adrian Dalca, Andreea Bobu, Polina Golland, MIT
 ==Project Description==
 <div style="margin: 20px;">
-Most synthesis, in-painting or super-resolution methods require a training dataset which includes the desired-quality images. Unfortunately, in the clinical setting this is often now available.
+Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, and analysis, will fail.
+In part, this is because registration algorithm depend on assumptions of smooth anatomical structures and good quality images, which are not present in these sparse clinical acquisitions.
-Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, analysis, will fail.
+Here, we are investigating a patch-based discrete image registration which allows for more versatile image metrics and does not impose similar assumptions.
 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Objective</h3>
-* We will investigate a current model for learning and using Patch-based Gaussian Mixture Model to restore sparse-slice data..
+* We will investigate a current implementation for patch-based discrete registration on sparse-slice data.
 </div>
 <div style="width: 27%; float: left; padding-right: 3%;">

2015 Winter Project Week:PatchRegistration - Revision history

Adalca at 15:38, 7 December 2015

Adalca: Created page with '__NOTOC__ Image:PW-MIT2015.png|Projects List Image:WMH_T1.png|Clinical Stroke Image ==Key Investigators== - Adrian Dal…'

Adalca: Created page with 'NOTOC Image:PW-MIT2015.png|Projects List Image:WMH_T1.png|Clinical Stroke Image ==Key Investigators== - Adrian Dal…'