A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis

Abstract

The authors showed that a patch-based method based on affine registrations and T1-weighted MRI could generate accurate pCTs of the pelvis. The main source of differences between pCT and CT was positional changes of air pockets and body outline.

Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

“…For both the target and the OARs, both sCT-based dose distributions differed from the corresponding CT-based dose distribution, on average, no more than 1% of the original dose. These results are comparable to those already presented in the literature [8], [9], [11], [19], [23], [24]. Mean percentages of passing points within the external contour of 98–100% and 94–97% were achieved for both methods and cancer sites for the 3D local Ɣ-analyses with constraints of 3%_3 mm and 2%_2 mm, respectively.…”

“…Mean percentages of passing points within the external contour of 98–100% and 94–97% were achieved for both methods and cancer sites for the 3D local Ɣ-analyses with constraints of 3%_3 mm and 2%_2 mm, respectively. Ɣ-pass rates of the same order were reported by Korhonen et al [11] (93–97% for 1%_1 mm 2D Ɣ-test), Jonsson et al [15] (99% for 3%_3 mm Ɣ-test), Dowling et al [19] (93–96% for 2%_2 mm 3D global Ɣ-test), Uh et al [23] (98–99% for 2%_2 mm Ɣ-test), Andreasen et al [24] (97% for 1%_1 mm 2D global Ɣ-test) and Siversson et al [25] (99–100% for 2%_1 mm local Ɣ-test). Furthermore, dose distributions based on sCT a showed a better PTV agreement and a more homogeneous gamma map with lower gamma values than sCT bda .…”

“…The MAE obtained within the external contour for the prostate patients was on average 49.8 ± 4.6 HU, which is lower than the error obtained by Kim et al [20] (74.3 ± 10.9 HU) and is of the same order as the MAE obtained by Siversson et al [25], Dowling et al [19] and Andreasen et al [24] (36.5 ± 4.1 HU, 40.5 ± 8.2 HU and 54 ± 8 HU, respectively), when taking into account the fact that the images used in the present study had a lower resolution. The synthesis error was higher for the H&N patients as the neck is a more challenging area for registration algorithms because of the mixture of bone and air, and due to the presence of large-scale postural changes between patients, such as flexion or extension of the neck and the position of the jawbone.…”

“…The fusion can be obtained by computing the voxelwise median [21], using a probabilistic Bayesian framework [22], an arithmetic mean process or pattern recognition with Gaussian process [23] or a local image similarity measure [18], [19]. Instead of using a database of images, Andreasen et al [24] employed a dictionary of MR and CT patches. The sCT was predicted by extracting patches from the test subject MRI, running an intensity-based nearest neighbour search in the patch database and fusing the selected CT patches using a similarity-weighted average.…”

Patch‐based generative adversarial neural network models for head and neck MR‐only planning

“…This process is computationally intense with times ranging from ten minutes to several hours to generate sCT images. [8][9][10][11][12] Note also that computational time increases with the number of atlas pairs used. Ultimately, this computational burden makes these methods largely unsuitable for clinical practice at present, especially looking forward to where MR is used for online treatment adaptation.…”