AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level.

PURPOSE: Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges estimated from measured images were deviated because of limited spatial resolutions of the developed x-ray camera as well as statistical noise in the images. To solve these problems, we proposed a method for predicting high-resolution dose images from SEB images with various count level using a deep learning (DL) approach for range and width verification. METHODS: In this study, we adopted the double U-Net model, which is a previously proposed deep convolutional network model. The first U-Net model in the double U-Net model was used to denoise the SEB images with various count level. The first U-Net model for denoising was trained on 8000 pairs of SEB images with various count level and noise-free images which were created by a sophisticated in-house developed model function. The second U-Net model for dose prediction was trained using 8000 pairs of denoised SEB images from the first U-Net model and high-resolution dose images generated by Monte Carlo simulation. RESULTS: For both simulation and measurement data, the trained DL model could successfully predict high-resolution dose images which showed a clear Bragg peak and no statistical noise. The difference of the range and width was less than 2.1 mm, even from the SEB images measured with a decrease in the number of irradiated protons to less than 11% of 3.2 × 1011 protons. CONCLUSIONS: High-resolution dose images from measured and simulated SEB images were successfully predicted by using the trained DL model for protons. Our proposed DL model was feasible to predict dose images accurately even with smaller number of irradiated protons.

A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation study.

PURPOSE: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the distribution and the Bragg peak (BP) position. In this work, we investigated the feasibility of using a deep learning approach to convert PG images to PD distributions. METHODS: We designed the Monte Carlo simulations using 20 digital brain phantoms irradiated with a 100-MeV proton pencil beam. Each phantom was used to simulate 200 pairs of PG images and PD distributions. A convolutional neural network based on the U-net architecture was trained to predict PD distributions from PG images. RESULTS: Our simulation results show that the pseudo PD distributions derived from the corresponding PG images agree well with the simulated ground truths. The mean of the BP position errors from each phantom was less than 0.4 mm. We also found that 2000 pairs of PG images and dose distributions would be sufficient to train the U-net. Moreover, the trained network could be deployed on the unseen data (i.e. different beam sizes, proton energies and real patient CT data). CONCLUSIONS: Our simulation study has shown the feasibility of predicting PD distributions from PG images using a deep learning approach, but the reliable prediction of PD distributions requires high-quality PG images. Image-degrading factors such as low counts and limited spatial resolution need to be considered in order to obtain high-quality PG images.

A general-threshold filtering method for improving intravoxel incoherent motion parameter estimates.

In this study, we present an image denoising method for diffusion-weighted magnetic resonance imaging (DW-MRI) data. Our aim is to improve the estimation of intravoxel incoherent motion (IVIM) parameters using denoised DW-MRI data. A general-threshold filtering (GTF) reconstruction via total variation minimization has been proposed to improve image quality in few-view computed tomography. Here, we applied the combination of GTF and total difference to image denoising. Voxel-wise IVIM analysis was performed using both real and simulated DW-MRI data. Using an institutional review board-approved protocol with written informed consent, DW-MRI imaging was performed at a 3 T hybrid PET/MR system in 10 patients with Hodgkin lymphoma lesions. A simulated phantom consisting of four organs (liver, pancreas, spleen and kidney) was used to generate noisy DW-MRI data according to the IVIM model at different noise levels. DW-MRI data were denoised before IVIM parameter estimation. The proposed image denoising method was compared with the image denoising method using joint rank and edge constraints (JREC). The results of simulated data show that at the lower signal-to-noise ratios the proposed image denoising method outperformed the JREC method in terms of the accuracy and precision of the IVIM parameter estimates. The experimental results also show that the proposed image denoising method could yield better parametric images than the JREC method in terms of noise reduction and edge preservation.

A low-count reconstruction algorithm for Compton-based prompt gamma imaging.

The Compton camera is an imaging device which has been proposed to detect prompt gammas (PGs) produced by proton-nuclear interactions within tissue during proton beam irradiation. Compton-based PG imaging has been developed to verify proton ranges because PG rays, particularly characteristic ones, have strong correlations with the distribution of the proton dose. However, accurate image reconstruction from characteristic PGs is challenging because the detector efficiency and resolution are generally low. Our previous study showed that point spread functions can be incorporated into the reconstruction process to improve image resolution. In this study, we proposed a low-count reconstruction algorithm to improve the image quality of a characteristic PG emission by pooling information from other characteristic PG emissions. PGs were simulated from a proton beam irradiated on a water phantom, and a two-stage Compton camera was used for PG detection. The results show that the image quality of the reconstructed characteristic PG emission is improved with our proposed method in contrast to the standard reconstruction method using events from only one characteristic PG emission. For the 4.44 MeV PG rays, both methods can be used to predict the positions of the peak and the distal falloff with a mean accuracy of 2 mm. Moreover, only the proposed method can improve the estimated positions of the peak and the distal falloff of 5.25 MeV PG rays, and a mean accuracy of 2 mm can be reached.