Synthetic CT for MRI-based liver stereotactic body radiotherapy treatment planning

Abstract

A technique for generating MRI-derived synthetic CT volumes (MRCTs) is demonstrated in support of adaptive liver stereotactic body radiation therapy (SBRT). Under IRB approval, 16 subjects with hepatocellular carcinoma were scanned using a single MR pulse sequence (T1 Dixon). Air-containing voxels were identified by intensity thresholding on T1-weighted, water and fat images. The envelope of the anterior vertebral bodies was segmented from the fat image and fuzzy-C-means (FCM) was used to classify each non-air voxel as mid-density, lower-density, bone, or marrow in the abdomen, with only bone and marrow classified within the vertebral body envelope. MRCT volumes were created by integrating the product of the FCM class probability with its assigned class density for each voxel. MRCTs were deformably aligned with corresponding planning CTs and 2-ARC-SBRT-VMAT plans were optimized on MRCTs. Fluence was copied onto the CT density grids, dose recalculated, and compared. The liver, vertebral bodies, kidneys, spleen and cord had median Hounsfield unit differences of less than 60. Median target dose metrics were all within 0.1 Gy with maximum differences less than 0.5 Gy. OAR dose differences were similarly small (median: 0.03 Gy, std:0.26 Gy). Results demonstrate that MRCTs derived from a single abdominal imaging sequence are promising for use in SBRT dose calculation.

Dosimetric evaluation of magnetic resonance-generated synthetic CT for radiation treatment of rectal cancer

“…The MR generated synCT has been evaluated for treatment planning of VMAT for brain [ 18 ] and liver tumors [ 19 ], IMRT for head and neck cancer [ 37 ], prostate tumor [ 20 ] and lung cancer [ 25 ]. Recently, a commercial synCT software has been evaluated for use in prostate radiotherapy [ 38 ].…”

“…However, there is no simple conversion from MR intensity to electron density value. To address the issue, techniques that generate synthetic CT (synCT) images from MR data are being developed [ 14 , 17 ], and have shown promising results in treatment planning including brain [ 18 ], liver [ 19 ], prostate [ 20 ], and pelvic tumors [ 21 ]. These methods typically can be classified as atlas-based and classification-based approaches.…”

Emerging role of MRI in radiation therapy

“…[44][45][46][47] However, it is important to note that including UTE and using multiple MR sequences increase scanning time, which may lead to motion artifacts and misalignment between images from different sequences. Other approaches that do not utilize additional imaging with UTE sequences include image segmentation methods such as a bone shape model 48 and active contour 49 to segment bone from T 1 -weighted or DIXON MR images before classification of the remaining voxels for nonbone tissue. However, the accuracy of bone segmentation may still suffer from nearby air and artifact.…”

Virtual 4DCT from 4DMRI for the management of respiratory motion in carbon ion therapy of abdominal tumors

“…Different strategies have been proposed recently to obtain synthetic CTs from MRI, in order to allow dose calculations. [21][22][23][24][25] As for the thoraco-abdominal site, an atlas-based approach was proposed for pediatric abdominal tumors by Guerreiro et al 22 Jonsson et al 26 investigated bulk density assignment for lung cancer, whereas a deep learning-based method was recently introduced by Liu et al 27 for liver patients. To our knowledge, however, no study has been conducted yet on the generation of synthetic CTs when dealing with respiratory-correlated images, including breathing motion in the CT generation process.…”