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Objective:To develop the real-time radiotherapy monitoring system of three-dimensional (3D) point cloud by using depth camera and verify its feasibility.Methods:Taking the depth camera coordinate system as the world coordinate system, the conversion relationship between the simulation CT coordinate system and the world coordinate system was obtained from the calibration module. The patient's simulation CT point cloud was transformed into the world coordinate system through the above relationship, and registered with the patient's surface point cloud obtained in real-time manner by the depth camera to calculate the six-dimensional (6D) error, and complete the positioning verification and fractional internal position error monitoring in radiotherapy. Mean and standard deviation of 6D calculation error, Hausdorff distance of point cloud after registration and the running time of each part of the program were calculated to verify the feasibility of the system. Fifteen real patients were selected to calculate the 6D error between the system and cone beam CT (CBCT).Results:In the phantom experiment, the errors of the system in the x, y and z axes were (1.292±0.880)mm, (1.963±1.115)mm, (1.496±1.045)mm, respectively, and the errors in the rotation, pitch and roll directions were 0.201°±0.181°, 0.286°±0.326°, 0.181°±0.192°, respectively. For real patients, the translational error of the system was within 2.6 mm, the rotational error was approximately 1°, and the program run at 1-2 frames/s. The precision and speed met the radiotherapy requirement. Conclusion:The 3D point cloud radiotherapy real-time monitoring system based on depth camera can automatically complete the positioning verification before radiotherapy, real-time monitoring of body position during radiotherapy, and provide error visual feedback, which has potential clinical application value.
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Objective:To explore the volume resolution of prostate motion target by four-dimensional (4D) ultrasound.Methods:The prostate ultrasound model was selected, and the group comparison study was conducted using 4D ultrasound to outline the prostate target under different motion amplitudes (A) and motion period (T). The simulated A value was set as 0.5 mm, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm, respectively. The T value was set as 1 s, 2 s, 3 s, and 4 s, respectively. The volume of the target of the model prostate was calculated, and the static ultrasound image of the target was used as the control group to analyze the difference between two groups.Results:When the model was still, the size of the target of ultrasound was consistent with that of CT scan ( P>0.05). When the A values were 0.5 mm and 1 mm, there was no statistical difference between the volume in period 1-4 s and the volume in the target at rest (all P>0.05). When the A values were 2 mm and 3 mm, and the T values were 1 s, 2 s and 3 s there was statistical difference between the volume of target and that of of static ultrasonic target (all P<0.05). When the A value was 2 mm and the T value was 4 s, there was no statistical difference between the target volume and the static target volume ( P=0.710). The range within the group was 6.7 cm 3, and the standard deviation was 1.15 cm 3. When the A value was 3 mm and the T value was 4 s, the volume repeatability of the target was poor, and the range within the group was 14.4 cm 3; when the A values were 4 mm and 5 mm, and the T values were 1-4 s, the range within the group was 3.27-17.63 cm 3 and 6.51-21.02 cm 3, respectively. The volume repeatability of the target under each period was extremely poor, which could not meet the clinical requirements. Conclusion:4D ultrasound can provide reliable reference data for patients′ target delineation within 1-4 s of motion cycle and within 1 mm of motion amplitude, which exerts on effect upon the original position of probe.
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Objective:To develop a 3D visualization technology-assisted patient positioning system for radiotherapy and compare it with traditional patient positioning method for breast and pelvic radiotherapy.Methods:A total of 40 patients receiving radiotherapy in Changzhou No.2 People′s Hospital from June 2020 to April 2021 were selected for this study, including 20 patients with breast cancer and 20 patients with pelvic cancer.3D visualization reconstruction was carried out using the CT data of the patients for positioning. Then the 3D visualization models were integrated with the real treatment environment and were then shifted to the isocentral positions of accelerators through interactive operations. Based on this, the patients were actually positioned. Every week, all of the patients were firstly treated with traditional positioning, followed by 3D visualization-guided positioning. As a result, 240 times of positioning data of all patients were collected in three weeks. They were compared with the data of cone-beam CT(CBCT)-guided positioning, which served as the gold standard.Results:The absolute positioning errors of 3D visualization-guided positioning along x, y and z axes were (1.92±1.23), (2.04±1.16), and (1.77±1.37)mm, respectively for patients with breast cancer and were (2.07±1.08), (1.33±0.88), and (1.99±1.25)mm, respectively for patients with pelvic cancer. Compared with traditional positioning method , the accuracy of 3D visualization-guided positioning along x、 y, and z axes was increased by 38.83%, 52.40% and 33%, respectively for patients with breast cancer and was improved by 36.84%, 54.04% and 52.58% for patients with pelvic cancer, with all differences being statistically significant along y and z axes ( t=2.956-5.734, P< 0.05). Meanwhile, the error distribution of the two positioning method was statistically significant along in y axis for patients with breast cancer( χ2=7.481, P<0.05) and was statistically significant along each axis for patients with pelvic cancer( χ2=5.900, 6.415, 7.200, P<0.05). Conclusions:The positioning method guided by 3D visualization technology can effectively improve the positioning accuracy of patients with breast cancer and patients with pelvic cancer and is of value in potential clinical application.
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Objective: To generate virtual non-contrast (VNC) computed tomography (CT) from intravenous enhanced CT through convolutional neural networks (CNN) and compare calculated dose among enhanced CT, VNC, and real non-contrast scanning. Method: 50 patients who accepted non-contrast and enhanced CT scanning before and after intravenous contrast agent injections were selected, and two sets of CT images were registered. A total of 40 and 10 groups were used as training and test datasets, respectively. The U-Net architecture was applied to learn the relationship between the enhanced and non-contrast CT. VNC images were generated in the test through the trained U-Net. The CT values of non-contrast, enhanced and VNC CT images were compared. The radiotherapy treatment plans for esophageal cancer were designed, and dose calculation was performed. Dose distributions in the three image sets were compared. Results: The mean absolute error of CT values between enhanced and non-contrast CT reached 32.3 ± 2.6 HU, and that between VNC and non-contrast CT totaled 6.7 ± 1.3 HU. The average CT values in enhanced CT of great vessels, heart, lungs, liver, and spinal cord were all significantly higher than those of non-contrast CT (p < 0.05), with the differences reaching 97, 83, 42, 40, and 10 HU, respectively. The average CT values of the organs in VNC CT showed no significant differences from those in non-contrast CT. The relative dose differences of the enhanced and non-contrast CT were -1.2, -1.3, -2.1, and -1.5% in the comparison of mean doses of planned target volume, heart, great vessels, and lungs, respectively. The mean dose calculated by VNC CT showed no significant difference from that by non-contrast CT. The average γ passing rate (2%, 2 mm) of VNC CT image was significantly higher than that of enhanced CT image (0.996 vs. 0.973, p < 0.05). Conclusion: Designing a treatment plan based on enhanced CT will enlarge the dose calculation uncertainty in radiotherapy. This paper proposed the generation of VNC CT images from enhanced CT images based on U-Net architecture. The dose calculated through VNC CT images was identical with that obtained through real non-contrast CT.
ABSTRACT
Medical images play an important role in clinical diagnosis and treatment. During the radiotherapy, CT can be available for the location and definition of the target volume. The medical images from multiple modalities are used to obtain the information on pathological body from many angles. However, obtaining multiple-modality medical images could be more resource-consuming, and difficult to guarantee the consistency of patients′ state. Medical image translation between multiple modalities can achieve the predication from one modality to another. The studies on medical images from multiple modalities such as CT, ultrasound, MRI and PET are reviewed in detail in this paper, , with discussions provided about characteristics of multiple modalities and challenges faced, as well as the research areas to be developed.
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Modern MRI technology has been applied to all aspects of radiotherapy, which is helpful for qualitative, localized and quantitative diagnosis of tumors, also being benefit to the processing of radiotherapy and evaluation on therapeutic responses. The application progresses of MRI in target area delineation, multimodal functional MRI, CT based on MRI data and four-dimensional MRI (4D-MRI) in radiotherapy were reviewed in this article.
