We propose a learning method to generate synthetic CT (sCT) image for MRI-only radiation treatment planning. The proposed method integrated a dense-block concept into a cycle-generative adversarial network (cycle-GAN) framework, which is named as dense-cycle-GAN in this study. Compared with GAN, the cycle-GAN includes an inverse transformation between CT (ground truth) and sCT, which could further constrain the learning model. A 2.5D fully convolution neural network (FCN) with dense-block was introduced in generator to enable end-to-end transformation. A FCN is used in discriminator to urge the generator’s sCT to be similar with the ground-truth CT images. The well-trained model was used to generate the sCT of a new MRI. This proposed algorithm was evaluated using 14 patients’ data with both MRI and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the prediction algorithm. Overall, the MAE, PSNR and NCC were 60.9−11.7 HU, 24.6±0.9 dB, and 0.96±0.01. We have developed a novel deep learning-based method to generate sCT with a high accuracy. The proposed method makes the sCT comparable to that of the planning CT. With further evaluation and clinical implementation, this method could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.
We propose a method to generate patient-specific pseudo CT (pCT) from routinely-acquired MRI based on semantic information-based random forest and auto-context refinement. Auto-context model with patch-based anatomical features are integrated into classification forest to generate and improve semantic information. The concatenate of semantic information with anatomical features are then used to train a series of regression forests based on auto-context model. The pCT of new arrival MRI is generated by extracting anatomical features and feeding them into the well-trained classification and regression forests for pCT prediction. This proposed algorithm was evaluated using 11 patients’ data with brain MR and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) are 57.45±8.45 HU, 28.33±1.68 dB, and 0.97±0.01. The Dice similarity coefficient (DSC) for air, soft-tissue and bone are 97.79±0.76%, 93.32±2.35% and 84.49±5.50%, respectively. We have developed a novel machine-learning-based method to generate patient-specific pCT from routine anatomical MRI for MRI-only radiotherapy treatment planning. This pseudo CT generation technique could be a useful tool for MRI-based radiation treatment planning and MRI-based PET attenuation correction of PET/MRI scanner.
We develop a learning-based method to generate patient-specific pseudo computed tomography (CT) from routinely acquired magnetic resonance imaging (MRI) for potential MRI-based radiotherapy treatment planning. The proposed pseudo CT (PCT) synthesis method consists of a training stage and a synthesizing stage. During the training stage, patch-based features are extracted from MRIs. Using a feature selection, the most informative features are identified as an anatomical signature to train a sequence of alternating random forests based on an iterative refinement model. During the synthesizing stage, we feed the anatomical signatures extracted from an MRI into the sequence of well-trained forests for a PCT synthesis. Our PCT was compared with original CT (ground truth) to quantitatively assess the synthesis accuracy. The mean absolute error, peak signal-to-noise ratio, and normalized cross-correlation indices were 60.87 ± 15.10 HU, 24.63 ± 1.73 dB, and 0.954 ± 0.013 for 14 patients’ brain data and 29.86 ± 10.4 HU, 34.18 ± 3.31 dB, and 0.980 ± 0.025 for 12 patients’ pelvic data, respectively. We have investigated a learning-based approach to synthesize CTs from routine MRIs and demonstrated its feasibility and reliability. The proposed PCT synthesis technique can be a useful tool for MRI-based radiation treatment planning.
Magnetic resonance imaging (MRI) provides a number of advantages over computed tomography (CT) for radiation therapy treatment planning; however, MRI lacks the key electron density information necessary for accurate dose calculation. We propose a dictionary-learning-based method to derive electron density information from MRIs. Specifically, we first partition a given MR image into a set of patches, for which we used a joint dictionary learning method to directly predict a CT patch as a structured output. Then a feature selection method is used to ensure prediction robustness. Finally, we combine all the predicted CT patches to obtain the final prediction for the given MR image. This prediction technique was validated for a clinical application using 14 patients with brain MR and CT images. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), normalized cross-correlation (NCC) indices and similarity index (SI) for air, soft-tissue and bone region were used to quantify the prediction accuracy. The mean ± std of PSNR, MAE, and NCC were: 22.4 ± 1.9 dB, 82.6 ± 26.1 HU, and 0.91 ± 0.03 for the 14 patients. The SIs for air, soft-tissue, and bone regions are 0.98 ± 0.01, 0.88 ± 0.03, and 0.69 ± 0.08. These indices demonstrate the CT prediction accuracy of the proposed learning-based method. This CT image prediction technique could be used as a tool for MRI-based radiation treatment planning, or for PET attenuation correction in a PET/MRI scanner.
We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The welltrained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients’ data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.
We propose a high-resolution CT image retrieval method based on sparse convolutional neural network. The proposed framework is used to train the end-to-end mapping from low-resolution to high-resolution images. The patch-wise feature of low-resolution CT is extracted and sparsely represented by a convolutional layer and a learned iterative shrinkage threshold framework, respectively. Restricted linear unit is utilized to non-linearly map the low-resolution sparse coefficients to the high-resolution ones. An adaptive high-resolution dictionary is applied to construct the informative signature which is highly connected to a high-resolution patch. Finally, we feed the signature to a convolutional layer to reconstruct the predicted high-resolution patches and average these overlapping patches to generate high-resolution CT. The loss function between reconstructed images and the corresponding ground truth highresolution images is applied to optimize the parameters of end-to-end neural network. The well-trained map is used to generate the high-resolution CT from a new low-resolution input. This technique was tested with brain and lung CT images and the image quality was assessed using the corresponding CT images. Peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean absolute error (MAE) indexes were used to quantify the differences between the generated high-resolution and corresponding ground truth CT images. The experimental results showed the proposed method could enhance images resolution from low-resolution images. The proposed method has great potential in improving radiation dose calculation and delivery accuracy and decreasing CT radiation exposure of patients.
Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being
used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on
a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training
images and adopted as signatures for each voxel. The most robust and informative features are identified using
feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of
a new patient. This prediction technique was tested with human brain images and the prediction accuracy was
assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM)
indexes were used to quantify the differences between the pseudo and original CT images. The experimental
results showed the proposed method could accurately generate pseudo CT images from MR images. In
summary, we have developed a new pseudo CT prediction method based on patch-based random forest,
demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique
could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI