ABSTRACT
Purpose: Brain metastasis is a common, life-threatening neurological problem for patients with cancer. Single-isocenter volumetric modulated arc therapy (VMAT) has been popularly used due to its highly conformal dose and short treatment time. Accurate prediction of its dose distribution can provide a general standard for evaluating the quality of treatment plan. In this study, a deep learning model is applied to the dose prediction of a single-isocenter VMAT treatment plan for radiotherapy of multiple brain metastases. Method: A U-net with residual networks (U-ResNet) is employed for the task of dose prediction. The deep learning model is first trained from a database consisting of hundreds of historical treatment plans. The 3D dose distribution is then predicted with the input of the CT image and contours of regions of interest (ROIs). A total of 150 single-isocenter VMAT plans for multiple brain metastases are used for training and testing. The model performance is evaluated based on mean absolute error (MAE) and mean absolute differences of multiple dosimetric indexes (DIs), including (D max and D mean) for OARs, (D 98, D 95, D 50, and D 2) for PTVs, homogeneity index, and conformity index. The similarity between the predicted and clinically approved plan dose distribution is also evaluated. Result: For 20 tested patients, the largest and smallest MAEs are 3.3% ± 3.6% and 1.3% ± 1.5%, respectively. The mean MAE for the 20 tested patients is 2.2% ± 0.7%. The mean absolute differences of D 98, D 95, D 50, and D2 for PTV60, PTV52, PTV50, and PTV40 are less than 2.5%, 3.0%, 2.0%, and 3.0%, respectively. The prediction accuracy of OARs for D max and D mean is within 3.2% and 1.2%, respectively. The average DSC ranges from 0.86 to 1 for all tested patients. Conclusion: U-ResNet is viable to produce accurate dose distribution that is comparable to those of the clinically approved treatment plans. The predicted results can be used to improve current treatment planning design, plan quality, efficiency, etc.
ABSTRACT
Background: Cine-magnetic resonance imaging (MRI) is currently used in real-time tumor tracking during magnetic resonance (MR)-guided radiotherapy. As a type of MRI specified for motion tracking, a few minutes' acquisition results in thousands of 2-dimensional (2D) images. For MR-guided radiotherapy consisting of multiple treatment fractions, the large number of cine-MRI images would be disproportionate to the tight clinical data storage available. To alleviate this issue, the feasibility of compression of cine-MRI via video encoders was investigated in this study. Methods: The cine-MRI images were first sorted into 3 sequences according to their plane orientations. Then, each sequence was reordered according to their acquisition times [time-based (TB)] or content similarities [similarity-based (SB)]. As a result, 3 sequences were obtained for 3 plan orientations. Next, the obtained sequences were processed by a video encoder and the corresponding 3 video files were achieved. We employed 3 popular video encoders: Motion JPEG (M-JPEG), Advanced Video Coding (AVC), and High Efficiency Video Coding (HEVC). The performances of the sequence reordering methods and video encoders were evaluated based on a total of 150 image sets. Results: The mean correlation quantities for SB sequences were higher than those for TB sequences by 3% (sagittal), 2% (coronal), and 1% (transverse), respectively. The average compression ratio (CR) yielded by the SB sequences was higher than that achieved by the TB sequences. Comparing with M-JPEG, the CRs obtained by AVC and HEVC were increased by 58% and 62% (sagittal), 16% and 23% (coronal), and 48% and 56% (transverse), respectively. Among the 3 video encoders, the highest CRs and restoration accuracy were achieved by HEVC. Conclusions: HEVC with inter-frame coding is more effective in reducing the redundant information in consecutive images. It is feasible to implement the video encoder for high-performance cine-MRI compression.
ABSTRACT
Background: Magnetic resonance imaging (MRI) is currently used for online target monitoring and plan adaptation in modern image-guided radiotherapy. However, storing a large amount of data accumulated during patient treatment becomes an issue. In this study, the feasibility to compress MRI images accumulated in MR-guided radiotherapy using video encoders was investigated. Methods: Two sorting algorithms were employed to reorder the slices in multiple MRI sets for the input sequence of video encoder. Three cropping algorithms were used to auto-segment regions of interest for separate data storage. Four video encoders, motion-JPEG (M-JPEG), MPEG-4 (MP4), Advanced Video Coding (AVC or H.264) and High Efficiency Video Coding (HEVC or H.265) were investigated. The compression performance of video encoders was evaluated by compression ratio and time, while the restoration accuracy of video encoders was evaluated by mean square error (MSE), peak signal-to-noise ratio (PSNR), and video quality matrix (VQM). The performances of all combinations of video encoders, sorting methods, and cropping algorithms were investigated and their effects were statistically analyzed. Results: The compression ratios of MP4, H.264 and H.265 with both sorting methods were improved by 26% and 5%, 42% and 27%, 72% and 43%, respectively, comparing to those of M-JPEG. The slice-prioritized sorting method showed a higher compression ratio than that of the location-prioritized sorting method for MP4 (P=0.00000), H.264 (P=0.00012) and H.265 (P=0.00000), respectively. The compression ratios of H.265 were improved significantly with the applications of morphology algorithm (P=0.01890 and P=0.00530), flood-fill algorithm (P=0.00510 and P=0.00020) and level-set algorithm (P=0.02800 and P=0.00830) for both sorting methods. Among the four video encoders, H.265 showed the best compression ratio and restoration accuracy. Conclusions: The compression ratio and restoration accuracy of video encoders using inter-frame coding (MP4, H.264 and H.265) were higher than that of video encoders using intra-frame coding (M-JPEG). It is feasible to implement video encoders using inter-frame coding for high-performance MRI data storage in MR-guided radiotherapy.
ABSTRACT
Purpose: Treatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed. Methods: First, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA). Results: The results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively. Conclusion: The autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.
