Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering. Issue 8 (31st July 2015)

• Record Type: Journal Article
• Authors: Su, Kuan‐Hao; Hu, Lingzhi; Stehning, Christian; Helle, Michael; Qian, Pengjiang; Thompson, Cheryl L.; Pereira, Gisele C.; Jordan, David W.; Herrmann, Karin A.; Traughber, Melanie; Muzic, Raymond F.; Traughber, Bryan J.
• Is Part Of: Medical physics. Volume 42:Issue 8(2015)Part 1
• Page Start: 4974
• Page End: 4986
• Publication Date: 2015-07-31
• Subjects: biomedical MRI -- bone -- brain -- computerised tomography -- data acquisition -- fats -- feature extraction -- fuzzy set theory -- image classification -- image enhancement -- image matching -- image reconstruction -- image sampling -- medical image processing -- neurophysiology -- pattern clustering -- spin‐spin relaxation -- water; Computed tomography -- Clinical applications -- Pulse sequences -- Reconstruction; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Computerised tomographs -- Compositions of oils, fats or waxes; Compositions of derivatives thereof -- Animal or vegetable oils, fats, fatty substances or waxes; Fatty acids therefrom; Detergents; Candles -- Methods or arrangements for processing data by operating upon the order or content of the data handled -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; MRI -- undersampling -- UTE -- CT -- clustering; Computed tomography -- Tissues -- Brain -- Cluster analysis -- Medical image reconstruction -- Image reconstruction -- Spatial resolution -- Medical image artifacts; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4926756
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: MR‐based pseudo‐CT has an important role in MR‐based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo‐CT generation of the brain using a single‐acquisition, undersampled ultrashort echo time (UTE)‐mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190‐s, undersampled, single acquisition UTE‐mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point‐spread functions of three external MR markers. Two‐point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R 2 ∗ images (1/ T 2 ∗ ) were then estimated and were used to provide bone information. Three image features, i.e., Dixon‐fat, Dixon‐water, and R 2 ∗, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c ‐means (FCM ) algorithm. A two‐step, automatic tissue‐assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo‐CTs were generated by a voxelwise linear combination of the membership functions of theFCM . A low‐dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μ s (max: 1.350 μ s; min: 0.180 μ s). TheFCM ‐estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo‐CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low‐dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo‐CT generation.