  To achieve magnetic resonance (MR)-only radiotherapy, a method needs to be
employed to estimate a synthetic CT (sCT) for generating electron density maps
and patient positioning reference images. We investigated 2D and 3D
convolutional neural network (CNN) methods to generate a male pelvic sCT using
a T1-weighted MR image. A retrospective study was performed using CTs and
T1-weighted MR images of 20 prostate cancer patients. The proposed 2D CNN
model, which contained 27 convolutional layers, was modified from the SegNet
for better performance. 3D version of the CNN model was also developed. Both
CNN models were trained from scratch to map intensities of T1-weighted MR
images to CT Hounsfield Unit (HU) values. Each sCT was generated in a
five-fold-cross-validation framework and compared with the corresponding CT
using voxel-wise mean absolute error (MAE), and dice similarity coefficient
(DSC), recall, and precision for bony structures. Wilcoxon signed-rank tests
were performed to evaluate the differences between the both models. The MAE
averaged across all patients were 40.5 $\pm$ 5.4 HU and 37.6 $\pm$ 5.1 HU for
the 2D and 3D CNN models, respectively. The DSC, recall, and precision of the
bony structures were 0.81 $\pm$ 0.04, 0.85 $\pm$ 0.04, and 0.77 $\pm$ 0.09 for
the 2D CNN model, and 0.82 $\pm$ 0.04, 0.84 $\pm$ 0.04, and 0.80 $\pm$ 0.08 for
the 3D CNN model, respectively. P values of the Wilcoxon signed-rank tests were
less than 0.05 except for recall, which was 0.6. The 2D and 3D CNN models
generated accurate pelvic sCTs for the 20 patients using T1-weighted MR images.
The evaluation metrics and statistical tests indicated that the 3D model was
able to generate sCTs with better MAE, bone DSC, and bone precision. The
accuracy of the dose calculation and patient positioning using generated sCTs
will be tested and compared for the two models in the future.

Medical Physics