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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

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Published on: July 16, 2014

Adaptive template generation for amyloid PET using a deep learning approach.

Seung Kwan Kang^1,2, Seongho Seo³, Seong A Shin^1,4

¹Department of Biomedical Sciences, Seoul National University, Seoul, Korea.

Human Brain Mapping

|May 13, 2018

Summary

This summary is machine-generated.

Deep learning models create personalized templates for accurate spatial normalization of amyloid PET scans, eliminating the need for MRI. This enhances Alzheimer's disease assessment in clinical research.

Keywords:

amyloid PET deep learning quantification spatial normalization

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Rapid Generation of Amyloid from Native Proteins In vitro

Rapid Generation of Amyloid from Native Proteins In vitro

Published on: December 5, 2013

Area of Science:

Neuroimaging
Artificial Intelligence

Background:

Spatial normalization (SN) of amyloid PET images is crucial for Alzheimer's disease (AD) assessment but challenging without matched MRI.
Current methods using average PET templates can lead to inaccuracies.

Purpose of the Study:

To develop and validate deep neural networks for accurate, MRI-less spatial normalization of amyloid PET images.
To generate individually adaptive PET templates for improved quantitative accuracy.

Main Methods:

Trained convolutional auto-encoder (CAE) and generative adversarial network (GAN) models on 681 11C-PIB PET/MRI scan pairs.
Utilized augmented data (685,100 samples) for supervised training, with MRI-based SN as the label.
Tested models on 154 datasets to evaluate performance.

Main Results:

Deep learning models generated adaptive PET templates, significantly reducing SN errors compared to average templates.
The method achieved rapid template generation (0.02 s per image) without slice discontinuity.
Demonstrated enhanced quantitative accuracy in MRI-less amyloid PET assessment.

Conclusions:

Deep neural networks offer a robust and accurate solution for spatial normalization of amyloid PET images without requiring matched 3D MRI.
This MRI-less approach has significant potential for routine clinical and research analysis of amyloid PET data in Alzheimer's disease.
The method improves the efficiency and accuracy of Alzheimer's disease assessment using PET imaging.