Synthetic CT generation from CBCT images via deep learning

Abstract

Purpose Cone‐beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on‐treatment CBCT) for accurate patient setup in image‐guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U‐net‐based approach that synthesizes CT‐like images with accurate numbers from planning CT, while keeping the same anatomical structure as on‐treatment CBCT. Methods We formulated the CT synthesis problem under a deep learning framework, where a deep U‐net architecture was used to take advantage of the anatomical structure of on‐treatment CBCT and image intensity information of planning CT. U‐net was chosen because it exploits both global and local features in the image spatial domain, matching our task to suppress global scattering artifacts and local artifacts such as noise in CBCT. To train the synthetic CT generation U‐net (sCTU‐net), we include on‐treatment CBCT and initial planning CT of 37 patients (30 for training, seven for validation) as the input. Additional replanning CT images acquired on the same day as CBCT after deformable registration are utilized as the corresponding reference. To demonstrate the effectiveness of the proposed sCTU‐net, we use another seven independent patient cases (560 slices) for testing. Results We quantitatively compared the resulting synthetic CT (sCT) with the original CBCT image using deformed same‐day pCT images as reference. The averaged accuracy measured by mean absolute error (MAE) between sCT and reference CT (rCT) on testing data is 18.98 HU, while MAE between CBCT and rCT is 44.38 HU. Conclusions The proposed sCTU‐net can synthesize CT‐quality images with accurate CT numbers from on‐treatment CBCT and planning CT. This potentially enables advanced CBCT applications for adaptive treatment planning.

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

“…In fact, pCT generated from CBCT are used to monitor delivered doses or to estimate the cumulative delivered dose during the treatment course in the context of dose-guided adaptive radiotherapy. Studies using DL for pCT generation from CBCT are scarce for brain, 55 H&N, [13][14][15] pancreas, 38 or prostate cancer. [35][36][37]55,56 The studies involved an imaging analysis (pCT vs. reference CT), but only half of them evaluated the dose accuracy.…”

“…[35][36][37]55,56 The studies involved an imaging analysis (pCT vs. reference CT), but only half of them evaluated the dose accuracy. Among the three H&N studies using DL for pCT generation from CBCT, [13][14][15] one involved training a U-Net neural network on 50 coregistered CBCT/CT images and performing a test based on data from 10 patients. 13 MAEs from 6 to 27 HU were obtained for the pCTs generated from Varian CBCTs.…”

“…Several methods to perform CBCT dose calculation have been proposed: (a) calibration curve between the HU and densities (HU-D curve), [4][5][6] (b) density assignment method (DAM), 5,[7][8][9][10] (c) deformable image registration (DIR) between CT and CBCT, 3,[10][11][12] and (d) machine learning to generate a pseudo-CT (pCT). [13][14][15] (a) The HU-D curve established from a CBCT image can be used to convert CBCT HUs to densities for dose calculation. This curve can be defined with either an "adapted" phantom 6 (according to anatomical localization) or patient CBCT images.…”

“…32 Some studies have recently proposed DLMs for pCT generation from CBCT, mainly for scatter correction. 33,34 Other studies proposed the DLM for pCT generation from CBCT in prostate, [35][36][37] pancreas, 38 and H&N, [13][14][15] for dose calculation. H&N studies have been performed using either U-Net or cycleGAN architectures to generate pCT from CBCT.…”

Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy

“…32 Generation of synthetic CT images from CBCT images has been proposed using various deep-learning-based frameworks. 33,34 Kurz et al recently published a deep-learning-based CBCT correction method, which they validated on photon and proton treatments for prostate cancer. 35 The goal of these studies has been to synthesize a CT image that can be used for treatment planning based on the patient's current setup on the treatment table.…”