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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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Image-guided radiation therapy (IGRT) is a visual image-guided radiotherapy technique that has many advantages such as increasing the dose of tumor target area and reducing the dose of normal organ exposure. Cone beam CT (CBCT) is one of the most commonly used medical images in IGRT, and the rapid and accurate targeting of CBCT and the segmentation of dangerous organs are of great significance for radiotherapy. The current research method mainly includes partitioning method based on registration and segmentation method based on deep learning. This study reviews the CBCT image segmentation method, existing problems and development directions.
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In the current clinical diagnosis, medical images have become an important basis for diagnosis, and different modes of medical images provide different tissue information and functional information. Single-mode images can only provide single diagnostic information, by which difficult and complicated diseases cannot be diagnosed, and comprehensive and accurate diagnostic results can be obtained only with the help of multiple diagnostic information. The multimodal fusion technology fuses multiple modes of medical images into single-mode images, and thus the single-mode images contain complementary information between multiple modes of images, so that sufficient information for clinical diagnosis can be obtained in a single image. In this paper, the multimodal medical image fusion methods are sorted into two types, namely the traditional fusion method and the fusion method based on deep learning.
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Surface guided radiation therapy (SGRT) is a method of radiation therapy with non-invasive and non-radiation image guidance technology, which uses continuous real-time imaging to monitor the whole course of treatment. This paper summarizes the characteristics, representative products, application in clinical research and treatment, and quality control of SGRT. This emerging technology plays an increasingly important role in delivering more precise, safe, and comfortable radiotherapy to patients.
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@#<b>Objective</b> To investigate the dosimetric effect of truncated regions in computed tomography (CT) images on the targets and organs at risk in volumetric modulated arc therapy (VMAT) for middle thoracic esophageal cancer. <b>Methods</b> CT images of 15 patients with middle thoracic esophageal cancer were selected. Circle masks were used to make the volume of the truncated region account for 10%, 20%, 30%, and 40% of the arm volume, and the corresponding truncated CT images were obtained. The real CT was denoted as CT0. Two radiotherapy plans were made on CT0. One plan was VMAT_1F with full arcs, and the other one was VMAT_3F with arm avoidance. The plans were transplanted to four truncated CT, respectively, and the dosimetric differences between different plans were compared using Wilcoxon signed-rank test. <b>Results</b> Compared with VMAT_1F in CT0, <i>D</i><sub>mean</sub> and <i>V</i><sub>5</sub> of the lung decreased in VMAT_3F, but <i>D</i><sub>max</sub> of the spinal cord, <i>D</i><sub>mean</sub> of the heart, and <i>V</i><sub>20</sub> of the lung increased. In VMAT_3F, there was no statistically significant difference between the dosimetric parameters in the four truncated CT and those in CT0 (all <i>P</i> > 0.05). In VMAT_1F, except for homogeneity index and <i>D</i><sub>max</sub> of the spinal cord, the dosimetric parameters in four truncated CT were significantly different from those in CT0 (<i>P</i> < 0.05). The dosimetric difference increased with the increase in truncated region-to-volume ratio. <b>Conclusion</b> Complete CT data should be collected in clinical practice, and the radiation field avoiding the truncated regionshould be set if necessary to reduce the influence of the truncated region on dosimetry.
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Medical images can provide clinicans with accurate and comprehensive patients’ information. Morphological or functional abnormalities caused by various diseases can be manifested in many aspects. Although MR images and CT images can highlight the medical image data of different tissue structures of patients, single MR images or CT images cannot fully reflect the complexity of diseases. Using MR image to predict CT image is one of the cross-modal prediction of medical images. In this paper, the methods of MR image prediction for CTmage are classified into four categoriesincluding registration based on atlas, based on image segmentationmethod, based on learning method and based on deep learning method. In our research, we concluded that the method based on deep learning should bemore promoted in the future by compering the existing problems and future development of MR image predicting CT image method.
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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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Magnetic resonance imaging (MRI) is a technology with no radiation and high resolution of soft tissues. Therefore, MRI-guided radiotherapy has become a hot spot in the field of radiotherapy. It is of great importance to accurately delineate the targets in radiation oncology. Currently, the delineation of targets is mostly completed by manual segmentation, which is time-consuming, subjective and inconsistent. Automatic segmentation can improve the efficiency and consistency without sacrificing the accuracy of segmentation. In this article, the automatic segmentation methods of MRI applied in radiotherapy were reviewed. The goals, challenges and methods of automatic segmentation for different radiotherapy sites including prostate, nasopharyngeal carcinoma, brain tumors and other organs were analyzed and discussed.
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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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With the improvement of the complexity of medical image synthesis and the demand for the accuracy of clinical radiotherapy, deep learning algorithm plays an increasingly important role in pseudo CT image synthesis and analysis. This paper classifies and analyzes the pseudo CT image synthesis technology based on deep learning method in terms of the types of image modes, and describes the ongoing application in radiotherapy.
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Image-guided techniques are critical to improving the accuracy of radiotherapy for tumors. Ultrasound images have been gradually applied in the set-up verification of clinical radiotherapy and adaptive radiotherapy because of the real-time, reproducible and non-radiative characteristics. In this paper, the application of ultrasound image-guided technology in radiotherapy was classified and analyzed, and the latest research progress was introduced.
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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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Objective An improved method for obtaining pseudo-computed tomography (CT ps) based on ultrasound deformation field.Methods The three-dimensional image data of computed tomography and ultrasound for three postoperative cervical cancer patients were selected,including the CT (CTsim) and ultrasound (USsim) images obtained during the simulated positioning stage,and the cone beam CT (CBCT) and ultrasound images obtained during the positioning verification stage of the treatment one week later.Binary masks of the OROI and OROW were created and applied in ultrasound image registration;thus,the deformation field was obtained.The deformation field was applied to CTsim images and different pseudo-CT images were obtained.Similarities between these pseudo-CT images and those of CBCT were compared,and registration accuracies between pseudo-CT images under different binary masks and CTsim were discussed.Results The averages of the correlation coefficient between pseudo-CT based on OROI,OROW,no binary mask and CBCT were 0.95,0.82 and 0.64 respectively.The average of the normalized mean square Error were 0.12,0.42 and 0.57 respectively.Conclusion The pseudo-CT based on OROI binary mask matches the best with CTsim and achieves the highest similarity with CBCT.
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Objective To compare the dose difference between the 12-bit and 16-bit CT images containing metallic implants calculated by different algorithms. Methods The titanium alloy rod was inserted into the phantom and subject to CT scan and then the 12-bit and 16-bit CT images were reconstructed. The CT images were online transmitted to the Monaco planning system and a 0° of single field was designed. The dose distribution was calculated by PB (Pencil Beam), CC (Collapsed Cone) and MC (Monte Carlo) algorithms, respectively. The CT-ED curve was expanded and the dose was recalculated. The depth dose curve through the center of the metallic implants along with the direction of the field was obtained by using the Matlab 8. 3 statistical software. The dose distribution curves between 12-bit and 16-bit CT images calculated by different algorithms and the dose difference of varying distances between the incident and the exit surfaces of metallic implants were statistically compared. The dose was measured by thimble chamber. Results The 16-bit CT images accurately read the CT values of the metallic implants. After the CT-ED curve was expanded, the dose on the incident surface of metallic implant was reduced by 5. 43% and that on the exit surface was increased by 25. 56% calculated by PB algorithm compared with MC algorithm. The dose on the posterior exit surface was higher than that of MC algorithm. The dose on the incident surface of metallic surface was decreased by 4. 5%, whereas that on the exit surface was reduced by 4. 31% using CC algorithm. The dose on the posterior exit surface was more significantly reduced. The calculated values by MC algorithm were the most close to the measured values. Conclusions Application of 16-bit CT image, CT-ED curve expansion of the treatment planning system combined with MC algorithm can enhance the accuracy of dose calculation for the patients containing metallic implants during radiotherapy.
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Objective To investigate the impact on registration accuracy with the different registration ranges of CBCT images and CT images.Methods CBCT and CT scans were performed on the of 5 patients.The registration ranges of five patients' images of abdomen,head and chest performed CBCT and CT scanning were processed with four modes.Mode 1:the registration range of CT images was larger than the registration range of CBCT images,mode 2:the registration range of CT images and CBCT images were equally,mode 3:taking a 5 cm translation of CT images range from mode 2,mode 4:The CBCT range and CT range reduced 2 cm both sides simultaneously from mode 2.Using the registration program from Insight Segmentation and Registration Toolkit (ITK) to the four modes,the Mean Square Difference (MSD) metric values of four modes after registration were compared and the relationship between mode 2 and another three modes was analyzed by paired t test.Results For the MSD metric values,mode 3 was maximum,mode 2 and 4 were minimum,and mode 1 was centered.The difference between the mode 2 and mode 4 was not statistically significant(P ＞ 0.05).The difference between the mode 2 and mode 1 was statistically significant(t =-4.586,-4.164,-5.618,P ＜ 0.05).The difference between the mode 2 and mode 3 was statistically significant(t =-6.423,-8.109,-19.601,P＜0.05).Conclusion The registration ranges of CBCT and CT images has a certain extent of influence on the accuracy of image registration.The closer the registration range of CBCT and CT is,the higher the registration accuracy is.
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Objective To apply metal artifact reduction algorithm to reduce metal artifacts based on 12 bit and 16 bit CT images,and aimed to analyze the effects on CT value and dose distribution.Methods The metal implant was inserted into the phantom,and the original 12 bit CT image and original 16 bit CT image were derived from CT scanning.The images were processed using NMAR algorithm,so the corrected 12 bit image and corrected 16 bit image were obtained.A patient's CT was chosen with artificial femur,and used the NMAR algorithm to reduce metal artifacts.Furthermore,the CT values of original images and corrected images were compared and analyzed.In the planning system,dose distribution was calculated based on each image by same radiation treatment plan.The dose distribution difference of each image was compared and analyzed.Results For the 12 bit image,the CT value of metal was 3 071 HU,which was much smaller than the metal's actual CT value ll 080 HU.The metal's CT value for the 16bit image was 11 098 HU,which was very close to the actual value.The original CT images contained a lot of artifacts around the metal,resulting in a large deviation of CT values from the reference image.After NMAR correction,metal artifacts were reduced significantly,and the CT values were close to the reference images.The dose distribution of the corrected 16 bit image was closest to that of the reference image.The maximum dose deviation on the central axis was 1.8％.The difference between the 12 bit image and the reference image downstream the metal was notable,and the maximum dose deviation on the central axis was 81.6％.The X-rays passed through the artifact region in original image,the dose distribution was obviously different from the reference image,and the maximum dose deviation was 21.6％.Conclusions For the patient with metal implant,using the NAMR algorithm on the 16 bit image result in accurate CT value of CT image,so that the accurate dose distribution will be obtained.
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Radiomics refers to the comprehensive quantification of information such as tumor biological feature and heterogeneity through assessing a large number of quantitative image features from ROI of CT,MRI and PET images.With the development of data science,radiomics has been paid more and more attention.Feature extraction is an important step of Radiomics.The processes in feature extraction of radiomics were reviewed in this paper.
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With the development of computer techniques and medical software in image analysis and visualization,medical image registration as a key step before image processing(e.g., image fusion)is important in research and medical diagnosis. Therefore, many studies have focused on medical registration methods and algorithms. Up to now, several registration methods have been applied in clinical practice. In this paper,we make a classification and analysis of registration methods clinically applied in radiotherapy. How to improve the accuracy,efficiency,and robustness of medical image registration remains an issue to be solved in the future.
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More and more patients were treated by surgery with metal implants in clinic.Metal artifacts in CT images caused by metal implants brought challenges for postoperative evaluation and diagnosis of tumor.It also led to the possibility of missed diagnosis and misdiagnosis.In recent decades,the improved methods based on filtered-backprojection and iterative reconstruction algorithms have great progresses in reducing effects of metal artifacts.The actuality of techniques for metal artifact reduction were reviewed in this article.
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Intensity-modulated radiation therapy is widely used in the treatment of tumor,and the dose distribution highly conforms to tumor target area in three-dimension.However,the factors such as complex beam,data error,algorithm error and machine error may cause large dose deviation in intensity-modulated radiation therapy,which may lead to unnecessary radiation accident.Therefore,standing on the patients' safety point of view,dose verification is usually performed before executing the treatment plan in order to ensure the safe implementation of the treatment plan and to avoid un-planned irradiation dose.Currently,there are many tools and methods of dose verification in clinic,including point dose verification tools like finger-shaped ionization chamber and thermoluminescence dosimeter;two-dimensional dose verification tools like Mapcheck,MatriXX and films;three-dimensional dose verification tools like ArcCHECK,Delta4 and the third-party software.These common clinical dose verification methods are reviewed in this paper.