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A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging-Guided Online Adaptive Radiation Therapy: A Feasibility Study.

ABSTRACT: Purpose:Magnetic resonance-guided online adaptive radiation therapy (MRgOART) requires accurate and efficient segmentation. However, the performance of current autosegmentation tools is generally poor for magnetic resonance imaging (MRI) owing to day-to-day variations in image intensity and patient anatomy. In this study, we propose a patient-specific autosegmentation strategy using multiple-input deformable image registration (DIR; PASSMID) to improve segmentation accuracy and efficiency for MRgOART. Methods and materials:Longitudinal MRI scans acquired on a 1.5T MRI-Linac for 10 patients with abdominal cancer were used. The proposed PASSMID includes 2 steps: applying a patient-specific image processing pipeline to longitudinal MRI scans, and populating all contours from previous sessions/fractions to a new fractional MRI using multiple DIRs and combining the resulted contours using simultaneous truth and performance level estimation (STAPLE) to obtain the final consensus segmentation. Five contour propagation strategies were compared: planning computed tomography to fractional MRI scans through rigid body registration (RDR), pretreatment MRI to fractional MRI scans through RDR and DIR, and the proposed multi-input DIR/STAPLE without preprocessing, and the PASSMID. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) with ground truth contours were calculated slice by slice to quantify the contour accuracy. A quantitative index, defined as the ratio of acceptable slices, was introduced using a criterion of DSC > 0.8 and MDA < 2 mm. Results:The proposed PASSMID performed well with an average 2-dimensional DSC/MDA of 0.94/1.78 mm, 0.93/1.04 mm, 0.93/1.06 mm, 0.93/1.14 mm, 0.92/0.83 mm, 0.84/1.53 mm, 0.86/2.39 mm, 0.81/2.49 mm, 0.72/5.48 mm, and 0.70/5.03 mm for the liver, left kidney, right kidney, spleen, aorta, pancreas, stomach, duodenum, small bowel, and colon, respectively. Starting from the third fractions, the contour accuracy was significantly improved with PASSMID compared with the single-DIR strategy (P < .05). The mean ratio of acceptable slices were 13.9%, 17.5%, 60.8%, 70.6%, and 71.8% for the 5 strategies, respectively. Conclusions:The proposed PASSMID solution, by combining image processing, multi-input DIRs, and STAPLE, can significantly improve the accuracy of autosegmentation for intrapatient MRI scans, reducing the time required for further contour editing, thereby facilitating the routine practice of MRgOART.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7599267 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Project description:PurposeMagnetic resonance-guided online adaptive radiation therapy (MRgOART) requires accurate and efficient segmentation. However, the performance of current autosegmentation tools is generally poor for magnetic resonance imaging (MRI) owing to day-to-day variations in image intensity and patient anatomy. In this study, we propose a patient-specific autosegmentation strategy using multiple-input deformable image registration (DIR; PASSMID) to improve segmentation accuracy and efficiency for MRgOART.Methods and materialsLongitudinal MRI scans acquired on a 1.5T MRI-Linac for 10 patients with abdominal cancer were used. The proposed PASSMID includes 2 steps: applying a patient-specific image processing pipeline to longitudinal MRI scans, and populating all contours from previous sessions/fractions to a new fractional MRI using multiple DIRs and combining the resulted contours using simultaneous truth and performance level estimation (STAPLE) to obtain the final consensus segmentation. Five contour propagation strategies were compared: planning computed tomography to fractional MRI scans through rigid body registration (RDR), pretreatment MRI to fractional MRI scans through RDR and DIR, and the proposed multi-input DIR/STAPLE without preprocessing, and the PASSMID. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) with ground truth contours were calculated slice by slice to quantify the contour accuracy. A quantitative index, defined as the ratio of acceptable slices, was introduced using a criterion of DSC > 0.8 and MDA < 2 mm.ResultsThe proposed PASSMID performed well with an average 2-dimensional DSC/MDA of 0.94/1.78 mm, 0.93/1.04 mm, 0.93/1.06 mm, 0.93/1.14 mm, 0.92/0.83 mm, 0.84/1.53 mm, 0.86/2.39 mm, 0.81/2.49 mm, 0.72/5.48 mm, and 0.70/5.03 mm for the liver, left kidney, right kidney, spleen, aorta, pancreas, stomach, duodenum, small bowel, and colon, respectively. Starting from the third fractions, the contour accuracy was significantly improved with PASSMID compared with the single-DIR strategy (P < .05). The mean ratio of acceptable slices were 13.9%, 17.5%, 60.8%, 70.6%, and 71.8% for the 5 strategies, respectively.ConclusionsThe proposed PASSMID solution, by combining image processing, multi-input DIRs, and STAPLE, can significantly improve the accuracy of autosegmentation for intrapatient MRI scans, reducing the time required for further contour editing, thereby facilitating the routine practice of MRgOART.

Project description:The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B?spline?based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast?Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM?DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0~3.1mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0–1.9 mm in the prostate, 1.9–2.4 mm in the rectum, and 1.8–2.1 mm over the entire patient body. Sinusoidal errors induced by B?spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient?specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient?dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy. PACS numbers: 87.10.Kn, 87.55.km, 87.55.Qr, 87.57.nj

Project description:The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B-spline-based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast-Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM-DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0 ~ 3.1 mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0-1.9 mm in the prostate, 1.9-2.4mm in the rectum, and 1.8-2.1 mm over the entire patient body. Sinusoidal errors induced by B-spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient-specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy.

Project description:OBJECTIVE:To evaluate the feasibility of using computer-assisted, deformable image registration software to enable three-dimensional (3D), multi-parametric (mp) magnetic resonance imaging (MRI)-derived information on tumour location and extent, to inform the planning and conduct of focal high-intensity focused ultrasound (HIFU) therapy. PATIENTS AND METHODS:A nested pilot study of 26 consecutive men with a visible discrete focus on mpMRI, correlating with positive histology on transperineal template mapping biopsy, who underwent focal HIFU (Sonablate 500®) within a prospective, Ethics Committee-approved multicentre trial ('INDEX'). Non-rigid image registration software developed in our institution was used to transfer data on the location and limits of the index lesion as defined by mpMRI. Manual contouring of the prostate capsule and histologically confirmed MR-visible lesion was performed preoperatively by a urologist and uro-radiologist. A deformable patient-specific computer model, which captures the location of the target lesion, was automatically generated for each patient and registered to a 3D transrectal ultrasonography (US) volume using a small number (10-20) of manually defined capsule points. During the focal HIFU, the urologist could add additional sonications after image-registration if it was felt that the original treatment plan did not cover the lesion sufficiently with a margin. RESULTS:Prostate capsule and lesion contouring was achieved in <5 min preoperatively. The mean (range) time taken to register images was 6 (3-16) min. Additional treatment sonications were added in 13 of 26 cases leading to a mean (range) additional treatment time of 45 (9-90) s. CONCLUSION:Non-rigid MR-US registration is feasible, efficient and can locate lesions on US. The process has potential for improved accuracy of focal treatments, and improved diagnostic sampling strategies for prostate cancer. Further work on whether deformable MR-US registration impacts on efficacy is required.

Project description:OBJECTIVE:To compare the accuracy of different methods for image registration in image-guided adaptive brachytherapy (IGABT) for cervical cancer. METHODS:The last treatment planning CT images (CT1) and the first treatment planning CT images (CT2) were acquired from 15 patients with cervical cancer and registered with different match image qualities (retained/removed catheter source in images) and different match regions [target only (S Group)/ interested organ structure (M Group)/body (L Group)] in Velocity3.2 software. The dice similarity coefficient (DSC) between the clinical target volumes (CTV) of the CT1 and CT2 images (CTVCT1 and CTVCT2, respectively) and between the organs-at-risk (OAR) of the two imaging datasets (OARCT1 and OARCT2, respectively) were used to evaluate the image registration accuracy. RESULTS:The auto-segmentation volume of the catheter source using Velocity software based on the CT threshold was the closest to the actual volume within the CT value range of 1700-1800 HU. In the retained group, the DSC for the OARs of was better than or equal to that of the removed group, and the DSC value of the rectum was significantly improved (P < 0.05). For comparison of different match regions, the high-risk target volume (HRCTV) and the low-risk target volume (IRCTV) had the best precision for registration of the target area, which was significantly greater than that of M group and L group (P < 0.05). The M group had better registration accuracy of the target area and the best accuracy for the OARs. The DSC values of the bladder and rectum were significantly better than those of the other two groups (P < 0.05). CONCLUSIONS:The CT value range of 1700-1800 HU is optimal for automatic image segmentation using Velocity software. Automatic segmentation and shielding the volume of the catheter source can improve the image quality. We recommend the use of interested organ structures regions for image registration in image-guided adaptive brachytherapy for cervical cancer.

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A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging-Guided Online Adaptive Radiation Therapy: A Feasibility Study.

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