Small Animal Imaging Application and Innovation Platform for Detecting the Effects of Aging on the Brain and Musculoskeletal System

Small Animal Imaging Application and Innovation Platform for Detecting the Effects of Aging on the Brain and Musculoskeletal System

Funded by TUBITAK 1004 Grants (22AG016) 

Principal Investigators: Esin Ozturk Isik, Tolga Çukur

Co-PI: Pınar Özbay, Daniela Schulz, Cengizhan Öztürk, Can Yücesoy

Researchers: Arda Arpak, Said Aldemir

Project Summary: The main goal of this project is to create multimodal imaging protocols and develop innovative software systems at 7T PET/MR to detect and monitor effects of aging that could potentially be translated to human studies in the future. The first aim is to develop a magnetic resonance fingerprinting (MRF) sequence to estimate tissue parameters (T1, T2, R2*, QSM). The second aim is to develop a robust and fast MR spectroscopic imaging sequence. The third aim is to develop a brain atlas using PET/MR in rats. The fourth aim is to develop a deep learning software platform for high-sensitivity small animal imaging at 7T MRI that will have modules for accelerated data acquisition, spatial resolution enhancement, image reconstruction and unsupervised learning techniques to reduce the high data dependency of artificial neural networks.

Keywords: Brain imaging, aging, MRI reconstruction, 7T MRI

Objectives:

  • Development of metabolic imaging data acquisition and processing software for preclinical MR scanner.  
  • Neurochemical profiling of aging related changes in the brain based on 20 different neurochemical compounds– imaging at adult (8 weeks), middle-aged (12 months), and elderly (24 months) time points.
  • Development of deep learning based methods for fast metabolic imaging and its reconstruction and analysis.
  • Machine learning based analysis software for the detection of age-related metabolic impairments in the brain.

Results:

  • We have developed an MRF sequence software for preclinical imaging.
  • A state-of-the-art multiparametric mapping (MPM) software that leverages MRF technology has been created.

Figure. Illustration of generated quantitative multiparametric maps of tissues, including T1, T2, R2*, and QSM using MRF. These maps were used for the analysis of the detection of age-related metabolic impairments in the brain by deploying machine learning algorithms.

  • We have deployed machine learning to classify aging brains based on their multiparametric MR biomarkers, paving the way for more effective diagnosis and treatment of age-related neurological disorders.

Figure. Architecture of Deep CNN model that will be used for i) artifact suppression, ii) resolution enhancement , and iii) noise suppression.

 

DISSEMINATION

Conference Proceedings

  1. Arpak A, Sümer E, Samli A, Ozbay P, Pamuk U, Caglar K, Demir EM, Yumak M, Yucesoy C, Ozturk-Isik E. The Impact of Compressed Sensing on Radiomics of Fast Anatomical MRI of Rat Brain at 7T. International Society for Magnetic Resonance in Medicine. Toronto, Canada, June 3-8, 2023 (digital poster)