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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Robust-Deep: A Method for Increasing Brain Imaging Datasets to Improve Deep Learning Models' Performance and

Amirhossein Sanaat¹, Isaac Shiri¹, Sohrab Ferdowsi²

¹Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.

Journal of Digital Imaging

|February 9, 2022

Summary

This summary is machine-generated.

This study introduces a novel method to generate large, realistic medical imaging datasets for deep neural networks (DNNs). The technique enhances DNN performance by improving image quality and reducing bias in tasks like low-dose to full-dose PET conversion.

Keywords:

Attenuation correction Brain PET Data augmentation Deep learning Low-dose

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Area of Science:

Medical Imaging
Deep Learning
Computer Vision

Background:

Small datasets limit the performance of deep neural networks (DNNs) in medical imaging research.
Acquiring large clinical datasets is challenging and time-consuming.
Existing DNNs require extensive training data, which is often unavailable in medical imaging.

Purpose of the Study:

To propose an analytical method for generating large, realistic, and diverse medical imaging datasets.
To evaluate the effectiveness of synthesized images in improving DNN performance for image-to-image translation tasks.
To enhance the generalization and robustness of DNNs in medical imaging research.

Main Methods:

Utilized a dataset of 35 patients with registered brain PET/CT/MR images (full-dose, low-dose, NAC, MAC, CT, T1/T2 MRI).
Employed Laplacian blending, a classical computer vision technique, to synthesize realistic images without requiring DNN training data.
Implemented a modified ResNet DNN to perform four image-to-image translation tasks: LD to FD, LD+MR to FD, NAC to MAC, and MRI to CT.

Main Results:

The synthesized dataset (350 images) combined with real data (35 images) significantly improved DNN performance across all four tasks.
Demonstrated quantitative improvements: RMSE decreased by up to 40%, and SSIM increased by up to 11% compared to using only the small dataset.
Qualitative and quantitative analyses confirmed higher image quality, lower bias, and reduced variance in synthesized images compared to reference images.

Conclusions:

The proposed Laplacian blending method effectively generates realistic medical images, augmenting small datasets for DNN training.
Synthesized data significantly enhances the performance and reliability of DNNs in medical image analysis tasks.
This approach offers a viable solution to the data scarcity problem in medical imaging research, improving DNN generalization and robustness.