MaVirtualCT

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

A Deep Learning Approach to Synthesize Virtual CT based on Transmission Scan in hybrid PET/MR

Student: Deepa Gunashekar <\br> Supervision: Prof. Dr. Nassir Navab, Sailesh Conjeti, Kuangyu Shi, Shadi Albarqouni

Abstract

Computed Tomography (CT) is a mandatory imaging modality for radiation treatment planning while magnetic resonance imaging (MRI) and positron emission tomography (PET) have advantages in tumor delineation and dose prescriptions. With the advent of PET/MRI, this hybrid imaging modality has advantages of simultaneous acquisition of soft tissue morphological imaging and molecular imaging, which provides advanced information supporting clinical diagnosis and therapy planning (1). To avoid multiple scanning and additional high radiation doses, a new concept was proposed to integrate low dose transmission scan (TX) into a PET/MRI machine for the synthesis of virtual CT (VCT) for treatment planning (2). However, TX is usually extremely noisy with artifact spots and it is necessary to smooth the sinogram to obtain interpretable images. This leads to consequently blurred low-resolution images. The proposed Master thesis project will aim to synthesize high-resolution virtual CT planning based on low-resolution transmission scan in integrated PET/MRI. This VCT is aimed to substitute CT scans in several applications such as radiotherapy treatment planning and attenuation correction. In particular, this project will develop advanced deep learning approach to achieve imaging super-resolution. Project Description

Tasks:

  • Preparing the dataset
  • Employing Deep Learning to attack the problem
  • Validate the proposed algorithm

Requirements:

  • Good knowledge in machine learning and visualization.
  • Advanced skill in Matlab programming is required.
  • Basic knowledge in medical imaging would be beneficial.
  • Willingness to closely collaborate with physicians and experts in machine learning.

Location:

  • CAMP, Garching and Dept. Nuclear Medicine, Klinikum r. d. Isar

Literature

  • [1] Delso G, Fürst S, Jakoby B, et al. Performance Measurements of the Siemens mMR Integrated Whole-Body PET/MR Scanner. J Nucl Med. 2011;52(12):1914-22.
  • [2] Shi K, Cheng X, Navab N, et al. PET/MRI/VCT: Restoration of Virtual CT from Transmission Scan on PET/MRI using Joint-Anisotropic Diffusion. Computational Methods for Molecular Imaging: Springer; 2015

Resultant Paper


ProjectForm
Title: A Deep Learning Approach to Synthesize Virtual CT based on Transmission Scan in hybrid PET/MR
Abstract: Computed Tomography (CT) is a mandatory imaging modality for radiation treatment planning while magnetic resonance imaging (MRI) and positron emission tomography (PET) have advantages in tumor delineation and dose prescriptions. With the advent of PET/MRI, this hybrid imaging modality has advantages of simultaneous acquisition of soft tissue morphological imaging and molecular imaging, which provides advanced information supporting clinical diagnosis and therapy planning (1). To avoid multiple scanning and additional high radiation doses, a new concept was proposed to integrate low dose transmission scan (TX) into a PET/MRI machine for the synthesis of virtual CT (VCT) for treatment planning (2). However, TX is usually extremely noisy with artifact spots and it is necessary to smooth the sinogram to obtain interpretable images. This leads to consequently blurred low-resolution images. The proposed Master thesis project will aim to synthesize high-resolution virtual CT planning based on low-resolution transmission scan in integrated PET/MRI. This VCT is aimed to substitute CT scans in several applications such as radiotherapy treatment planning and attenuation correction. In particular, this project will develop advanced deep learning approach to achieve imaging super-resolution.
Student: Deepa Gunashekar
Director: Prof. Dr. Nassir Navab
Supervisor: Sailesh Conjeti, Kuangyu Shi, Shadi Albarqouni
Type: Master Thesis
Area: Machine Learning, Medical Imaging
Status: finished
Start:  
Finish:  
Thesis (optional):  
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Edit | Attach | Refresh | Diffs | More | Revision r1.6 - 10 Mar 2017 - 04:38 - ShadiAlbarqouni