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Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

To observe the safety and efficacy of CT-guided 3D template-assisted radioactive 125I seed implantation in the treatment of unresectable advanced soft tissue sarcoma (STS). Sixty-two patients who underwent continuous 3D template-assisted radioactive 125I seed implantation for the treatment of unresectable advanced STS from August 2017 to August 2018 were selected from four tumor treatment centers for retrospective analysis. The postoperative adverse reactions and tumor response were recorded, and the postoperative complications were observed and treated at the same time. The overall survival (OS) rate was determined. All patients successfully completed 125I seed implantation. In practice, the median number of puncture needles used during the operation was 20, and the median number of 125I particles was 88. There were no statistical differences in the relative dosimetry parameters before and after the operation (P > 0.05). Tumor evaluation was performed 6 months after the operation. The effective rate was 61.3%, and the local control rate was 93.5%. As of March 2020, the 1-year survival rate was 85.2%, and the 2-year survival rate was 49.0%. The OS was 23 months. CT-guided 3D template-assisted 125I seed implantation for the treatment of unresectable STS has a high local control rate, thereby further prolonging the OS of patients with unresectable STS. [ABSTRACT FROM AUTHOR]

Abstract In order to analyze the influence of eccentric load on mechanical properties and damage process of concrete with initial damage, the eccentric load compression tests of concrete under different confining pressures were carried out with the help of PFC particle flow program. The results show that: the eccentric load does not change the relationship between peak stress, crack initiation stress and confining pressure of concrete under uniform load, but decrease the value of them. The peak stress increasing coefficient under uniform load is higher than that under eccentric load, and the peak stress increasing coefficient increases in a linear function with the confining pressure, and the increasing rate is approximately the same. Under uniaxial compression of eccentric load, a type I shear crack approximately parallel to the loading direction is formed, while under biaxial compression, a bending type shear crack with the lower tip of the initial crack as the inflection point is formed. The number of microcracks in concrete under uniform load and eccentric load can be divided into three stages: the calm period at the initial loading stage, the pre-peak expansion period from crack initiation point to peak point, and the rapid increase period after the peak.