UTE‐mDixon‐based thorax synthetic CT generation. Issue 8 (12th June 2019)

• Record Type: Journal Article
• Authors: Su, Kuan‐Hao; Friel, Harry T.; Kuo, Jung‐Wen; Al Helo, Rose; Baydoun, Atallah; Stehning, Christian; Crisan, Adina N.; Traughber, Melanie S.; Devaraj, Ajit; Jordan, David W.; Qian, Pengjiang; Leisser, Asha; Ellis, Rodney J.; Herrmann, Karin A.; Avril, Norbert; Traughber, Bryan J.; Muzic, Raymond F.
• Is Part Of: Medical physics. Volume 46:Issue 8(2019)
• Page Start: 3520
• Page End: 3531
• Publication Date: 2019-06-12
• Subjects: fuzzy c‐means clustering -- synthetic CT -- thoracic imaging -- UTE‐mDixon; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13574
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Accurate photon attenuation assessment from MR data remains an unmet challenge in the thorax due to tissue heterogeneity and the difficulty of MR lung imaging. As thoracic tissues encompass the whole physiologic range of photon absorption, large errors can occur when using, for example, a uniform, water‐equivalent or a soft‐tissue‐only approximation. The purpose of this study was to introduce a method for voxel‐wise thoracic synthetic CT (sCT) generation from MR data attenuation correction (AC) for PET/MR or for MR‐only radiation treatment planning (RTP). Methods: Acquisition: A radial stack‐of‐stars combining ultra‐short‐echo time (UTE) and modified Dixon (mDixon) sequence was optimized for thoracic imaging. The UTE‐mDixon pulse sequence collects MR signals at three TE times denoted as UTE, Echo1, and Echo2. Three‐point mDixon processing was used to reconstruct water and fat images. Bias field correction was applied in order to avoid artifacts caused by inhomogeneity of the MR magnetic field. Analysis: Water fraction and R2* maps were estimated using the UTE‐mDixon data to produce a total of seven MR features, that is UTE, Echo1, Echo2, Dixon water, Dixon fat, Water fraction, and R2*. A feature selection process was performed to determine the optimal feature combination for the proposed automatic, 6‐tissue classification for sCT generation. Fuzzy c‐means was used for the automatic classification which was followed by voxel‐wise attenuation coefficient assignment as a weighted sum of those of the component tissues. Performance evaluation: MR data collected using the proposed pulse sequence were compared to those using a traditional two‐point Dixon approach. Image quality measures, including image resolution and uniformity, were evaluated using an MR ACR phantom. Data collected from 25 normal volunteers were used to evaluate the accuracy of the proposed method compared to the template‐based approach. Notably, the template approach is applicable here, that is normal volunteers, but may not be robust enough for patients with pathologies. Results: The free breathing UTE‐mDixon pulse sequence yielded images with quality comparable to those using the traditional breath holding mDixon sequence. Furthermore, by capturing the signal before T2* decay, the UTE‐mDixon image provided lung and bone information which the mDixon image did not. The combination of Dixon water, Dixon fat, and the Water fraction was the most robust for tissue clustering and supported the classification of six tissues, that is, air, lung, fat, soft tissue, low‐density bone, and dense bone, used to generate the sCT. The thoracic sCT had a mean absolute difference from the template‐based (reference) CT of less than 50 HU and which was better agreement with the reference CT than the results produced using the traditional Dixon‐based data. Conclusion: MR thoracic acquisition and analyses have been established to automatically provide six distinguishable tissue types to generate sCT for MR‐based AC of PET/MR and for MR‐only RTP.