Name
Title | ||
|---|---|---|
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 multi-center 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 Dixon-based 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-cluster-partitioning (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. These experimental results distinctively demonstrate the effectiveness of our proposed TFCALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMI.2019.2935916 | IEEE transactions on medical imaging |
Keywords | DocType | Volume |
Synthetic CT generation,Dixon-based MR,abdomen,attenuation correction (AC),transfer fuzzy clustering (TFC),active learning-based classification (ALC) | Journal | 39 |
Issue | ISSN | Citations |
4 | 0278-0062 | 3 |
PageRank | References | Authors |
0.38 | 0 | 19 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| pengjiang qian | 1 | 30 | 5.48 |
| Yangyang Chen | 2 | 4 | 0.73 |
| Jung-Wen Kuo | 3 | 4 | 0.75 |
| Yudong Zhang | 4 | 251 | 25.00 |
| Yizhang Jiang | 5 | 382 | 27.24 |
| Kaifa Zhao | 6 | 19 | 3.65 |
| Rose Al Helo | 7 | 3 | 0.38 |
| Harry Friel | 8 | 3 | 0.38 |
| Atallah Baydoun | 9 | 4 | 0.75 |
| Feifei Zhou | 10 | 3 | 0.38 |
| Jin Uk Heo | 11 | 3 | 0.38 |
| Norbert Avril | 12 | 3 | 1.05 |
| Karin Herrmann | 13 | 3 | 0.38 |
| Rodney Ellis | 14 | 4 | 1.06 |
| Bryan Traughber | 15 | 7 | 2.13 |
| Robert S Jones | 16 | 3 | 0.38 |
| Shitong Wang | 17 | 1485 | 109.13 |
| Kuan-Hao Su | 18 | 24 | 5.46 |
| Raymond F Muzic | 19 | 5 | 1.42 |