mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification

Abstract

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multicenter trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixonbased scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-clusterpartitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less.

Existing System

Key techniques - knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and support vector machine (SVM)

Proposed System

The proposed TFC-ALC method consists of five phases and these phases can be further divided into three sub-procedures. In Sub-procedure 1, by means of the strategies of the weighted convolutional sum as well as grid partition, Phase I generates the seven-dimensional MR feature data from mDixon MR images for each subject. In Sub procedure 2, Phase II obtains the referenced class prototypes regarding the tissue types of the four key materials for transfer learning; Phase III, using two key machine learning techniques, KL-TFCM and AL-SVM, generates multiple candidate tissue distinguishable-operators (TDOs). In Sub-procedure 3, Phase IV recognizes the tissue types of voxels in target MR images using the multiple candidate TDOs and the voting strategy; Phase V synthesizes target CT images according to component tissue types of voxels.

Architecture

BLOCK DIAGRAM