Purpose: There is an emerging interest of applying magnetic resonance imaging (MRI) to radiotherapy (RT) due to its superior soft tissue contrast for accurate target delineation as well as functional information for evaluating treatment response. MRI-based RT planning has great potential to enable dose escalation to tumors while reducing toxicities to surrounding normal tissues in RT treatments of nasopharyngeal carcinoma (NPC). Our study aims to generate synthetic CT from T2-weighted MRI using a deep learning algorithm. Methods: Thirty-three NPC patients were retrospectively selected for this study with local IRB's approval. All patients underwent clinical CT simulation and 1.5T MRI within the same week in our hospital. Prior to CT/MRI image registration, we had to normalize two different modalities to a similar intensity scale using the histogram matching method. Then CT and T2 weighted MRI were rigidly and deformably registered using intensity-based registration toolbox elastix (version 4.9). A U-net deep learning algorithm with 23 convolutional layers was developed to generate synthetic CT (sCT) using 23 NPC patients' images as the training set. The rest 10 NPC patients were used as the test set (~1/3 of all datasets). Mean absolute error (MAE) and mean error (ME) were calculated to evaluate HU differences between true CT and sCT in bone, soft tissue and overall region. Results: The proposed U-net algorithm was able to create sCT based on T2-weighted MRI in NPC patients, which took 7 s per patient on average. Compared to true CT, MAE of sCT in all tested patients was 97 ± 13 Hounsfield Unit (HU) in soft tissue, 131 ± 24 HU in overall region, and 357 ± 44 HU in bone, respectively. ME was -48 ± 10 HU in soft tissue, -6 ± 13 HU in overall region, and 247 ± 44 HU in bone, respectively. The majority soft tissue and bone region was reconstructed accurately except the interface between soft tissue and bone and some delicate structures in nasal cavity, where the inaccuracy was induced by imperfect deformable registration. One patient example was shown with almost no difference in dose distribution using true CT vs. sCT in the PTV regions in the sinus area with fine bone structures. Conclusion: Our study indicates that it is feasible to generate high quality sCT images based on T2-weighted MRI using the deep learning algorithm in patients with nasopharyngeal carcinoma, which may have great clinical potential for MRI-only treatment planning in the future.
In the radiotherapy of nasopharyngeal carcinoma (NPC), magnetic resonance imaging (MRI) is widely used to delineate tumor area more accurately. While MRI offers the higher soft tissue contrast, patient positioning and couch correction based on bony image fusion of computed tomography (CT) is also necessary. There is thus an urgent need to obtain a high image contrast between bone and soft tissue to facilitate target delineation and patient positioning for NPC radiotherapy. In this paper, our aim is to develop a novel image conversion between the CT and MRI modalities to obtain clear bone and soft tissue images simultaneously, here called bone-enhanced MRI (BeMRI).Thirty-five patients were retrospectively selected for this study. All patients underwent clinical CT simulation and 1.5T MRI within the same week in Shenzhen Second People's Hospital. To synthesize BeMRI, two deep learning networks, U-Net and CycleGAN, were constructed to transform MRI to synthetic CT (sCT) images. Each network used 28 patients' images as the training set, while the remaining 7 patients were used as the test set (~1/5 of all datasets). The bone structure from the sCT was then extracted by the threshold-based method and embedded in the corresponding part of the MRI image to generate the BeMRI image. To evaluate the performance of these networks, the following metrics were applied: mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR).In our experiments, both deep learning models achieved good performance and were able to effectively extract bone structure from MRI. Specifically, the supervised U-Net model achieved the best results with the lowest overall average MAE of 125.55 (P<0.05) and produced the highest SSIM of 0.89 and PSNR of 23.84. These results indicate that BeMRI can display bone structure in higher contrast than conventional MRI.A new image modality BeMRI, which is a composite image of CT and MRI, was proposed. With high image contrast of both bone structure and soft tissues, BeMRI will facilitate tumor localization and patient positioning and eliminate the need to frequently check between separate MRI and CT images during NPC radiotherapy.
According to the requirement of teaching reform,this paper does some beneficial reform and exploration in many aspects,such as teaching methods,teaching means,experimental teaching and the course project etc.,aiming at the characteristic of Power Engineering course.Some questions are thoroughly discussed such as heuristic,discussion and research technic method,multimedia teaching,comprehensive experiment and independent design in order to raise better scientific thought and the innovation consciousness of students,and to enhance practice skill of students.The teaching reform of this course has been implemented in my university,and it gets remarkable effect.In the meanwhile, the future work are proposed to consummate the teaching reform in the end of this paper.
Background: A comprehensive plan evaluation platform was established based on the daily cone-beam computed tomography (CBCT) to assess the treatment robustness quality between planning target volume-based intensity modulated proton therapy (PTV-IMPT) and clinical target volume (CTV)-based robust optimized IMPT (ro-IMPT) planning strategies in bilateral head and neck cancer (HNC) treatment. Methods: Nine bilateral HNC patients’ CT structure sets were used in this study. Daily CBCTs were converted into synthetic-CT (sCT) for dose reconstruction. The accuracy of the proton dose calculation in sCT is cross-validated via the same day’s verification-CT sim (vCT) with 3D gamma index comparison. PTV-IMPT and ro-IMPT were generated on the initial planning CT (pCT). CTV high-risk volume (CTV_high) received 70 Gy and CTV low/intermediate-risk (CTV_low) received 60 Gy. For PTV-IMPT, the PTVs were expanded 3 mm from the CTV; for ro-IMPT, robust optimization used a 3 mm setup and 3.5% range uncertainties. Dose accumulations were then calculated on the 35 sets of daily sCT, and the target coverages were compared to the initial plans. Results: The 3D gamma index dose comparison (3 mm/3%) showed an average pass rate of 98.2%±1.5% comparing the same day’s pair of sCT and vCT with both plans (total 38 pairs). Through the dose accumulation of 35 treatment fractions, the PTV-IMPT plan group’s mean V100 of CTV_high/CTV_low coverage degraded to 80.70%/85.73% compared to 96.72%/96.13% of the ro-IMPT group (P<0.002). One patient did have suboptimal coverage (CTV_low <90%) even with ro-IMPT. Significant weight loss was noted for this patient during the treatment course (>5 lbs). Conclusions: A comprehensive plan robustness evaluation platform based on the CBCT is established in our clinical workflow and enables dose accumulation and plan robustness evaluation on a daily basis. ro-IMPT demonstrated an optimal planning strategy over PTV-IMPT for bilateral HNC treatment. However, special cautions are needed for patients with significant weight or geometry changes.
Radiation therapy of liver cancer is limited by low tolerance of the liver to radiation. Radiosensitizers can effectively reduce the required radiation dose. AGuIX nanoparticles are small, multifunctional gadolinium-based nanoparticles that can carry radioisotopes or fluorescent markers for single-photon emission computed tomography (SPECT), positron emission tomography (PET), fluorescence imaging, and even multimodality imaging. In addition, due to the high atomic number of gadolinium, it can also serve as a tumor radiation sensitizer. It is critical to define the biodistribution and pharmacokinetics of these gadolinium-based nanoparticles to quantitate the magnitude and duration of their retention within the tumor microenvironment during radiotherapy. Therefore, in this study, we successfully labeled AGuIX with 64Cu through the convenient built-in chelator. The biodistribution studies indicated that the radiotracer 64Cu-AGuIX accumulates to high levels in the HepG2 xenograft of nude mice, suggesting that it would be a potential theranostic nanoprobe for image-guided radiotherapy in HCC. We also used a transmission electron microscope to confirm AGuIX uptake in the HepG2 cells. In radiation therapy studies, a decrease in 18F-FDG uptake was observed in the xenografts of the nude mice irradiated with AGuIX, which was injected 1 h before. These results provide proof-of-concept that AGuIX can be used as a theranostic radiosensitizer for PET imaging to guide radiotherapy for liver cancer.
Abstract Range verification in proton therapy is a critical quality assurance task. We studied the feasibility of online range verification based on proton-induced acoustic signals, using a bidirectional long-short-term-memory recurrent neural network and various signal processing techniques. Dose distribution of 1D pencil proton beams inside a CT image-based phantom was analytically calculated. The propagation of acoustic signal inside the phantom was modeled using the k-Wave toolbox. For signal processing, five methods were investigated: down-sampling (DS), DS + HT (Hilbert transform), Wavelet decomposition (Wavedec db1, db4 and db20). The performances were quantitatively evaluated in terms of mean absolute error, mean relative error (MRE) and the Bragg peak localization error ( ΔBP ). In addition, the study analyzed the impact of noise levels, the number of sensors, as well as the location of sensors. For the noiseless case (32 sensors), the Wavedec db1 method demonstrates the best performance: ΔBP is less than one pixel and the dose accuracy over the region adjacent to the Bragg peak (MRE50) is ∼3.04%. With the presence of noise, the Wavedec db1 method demonstrates the best noise immunity, achieving ΔBP less than 1 mm and an MRE50 of ∼12%. The proposed machine learning framework may become a useful tool allowing for online range verification in proton therapy.
Purpose Accurate dose calculation is a critical step in proton therapy. A novel machine learning‐based approach was proposed to achieve comparable accuracy to that of Monte Carlo simulation while reducing the computational time. Methods Computed tomography‐based patient phantoms were used and three treatment sites were selected (thorax, head, and abdomen), comprising different beam pathways and beam energies. The training data were generated using Monte Carlo simulations. A discovery cross‐domain generative adversarial network (DiscoGAN) was developed to perform the mapping between two domains: stopping power and dose, with HU values from CT images incorporated as auxiliary features. The accuracy of dose calculation was quantitatively evaluated in terms of mean relative error (MRE) and mean absolute error (MAE). The relationship between the DiscoGAN performance and other factors such as absolute dose, beam energy and location within the beam cross‐section (center and off‐center lines) was examined. Results The DiscoGAN model is found to be effective in dose calculation. For the abdominal case, the MRE is found to 1.47% (mean), 3.30% (maximum) and 0.67% (minimum). For the thoracic case, the MRE is found to ~2.43% (mean), 4.80% (maximum) and 0.71% (minimum). For the head case, the MRE is found to ~2.83% (mean), 4.84% (maximum) and 1.01% (minimum). Comparable accuracy is found in the independent validation dataset (different CT images), achieving a mean MRE of ~1.65% (thorax), 4.02% (head) and 1.64% (abdomen). For the energy span between 80 and 130 MeV, no strong dependency of accuracy on beam energy is found. The results imply that no systematic deviation, either over‐dose or under‐dose, occurs between the predicted dose and raw dose. Conclusion The DiscoGAN framework demonstrates great potential as a tool for dose calculation in proton therapy, achieving comparable accuracy yet being more efficient relative to Monte Carlo simulation. Its comparison with the pencil beam algorithm (PBA) will be the next step of our research. If successful, our proposed approach is expected to find its use in more advanced applications such as inverse planning and adaptive proton therapy.
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