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Hiroshi Honda - One of the best experts on this subject based on the ideXlab platform.
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comparison of positron emission tomography diffusion weighted imaging pet dwi Registration Quality in a pet mr scanner zoomed dwi vs conventional dwi
Journal of Magnetic Resonance Imaging, 2016Co-Authors: Koji Sagiyama, Yuji Watanabe, Ryotaro Kamei, Shingo Baba, Hiroshi HondaAbstract:Purpose To compare zoomed diffusion-weighted imaging (z-DWI) with reduced field of view (FOV) by spatially selective radiofrequency pulses and conventional echo planar imaging (EPI) DWI (c-DWI) with regard to Registration Quality using positron emission tomography / magnetic resonance (PET/MR) in patients with malignant tumors. Materials and Methods Fludeoxyglucose (18F) PET imaging, c-DWI, and z-DWI were conducted simultaneously in 21 patients with known or suspected malignancy using a PET/MR system. A fusion image showing the largest tumor area was generated for analysis. Registration accuracy between PET and DWI was assessed based on the area of maximum overlap and central point displacement of the tumor. EPI factor, echo time (TE), matching area, and displacement were compared between c-DWI and z-DWI by paired t-test. Agreement of apparent diffusion coefficient (ADC) acquired by the two sequences were also assessed with linear regression s and Bland–Altman plot analysis. Results Thirty-two lesions were detected on both PET and DWI (mean size 536.3 ± 471.8 mm2). At least one lesion was found in all subjects. In all cases, EPI factor was smaller with z-DWI than c-DWI (43.1 ± 15.6 vs. 62.0 ± 10.0, P < 0.0001), and TE was also shorter for z-DWI (53.6 ± 3.6 msec vs. 65.2 ± 3.6 msec, P < 0.0001). Registration accuracy was better with z-DWI in 30 of 32 lesions (93.8%), and both average matching area and central point displacement were significantly improved (79.8 ± 18.1% vs. 61.8 ± 22.9%, P < 0.0001 and 3.92 ± 2.69 mm vs. 7.51 ± 4.07 mm, P < 0.0001). ADC values calculated with c-DWI and z-DWI showed good agreement. Conclusion Zoomed DWI reduces image distortion and provides better Registration accuracy with PET images. J. Magn. Reson. Imaging 2015.
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Comparison of positron emission tomography diffusion-weighted imaging (PET/DWI) Registration Quality in a PET/MR scanner: Zoomed DWI vs. Conventional DWI.
Journal of magnetic resonance imaging : JMRI, 2015Co-Authors: Koji Sagiyama, Yuji Watanabe, Ryotaro Kamei, Shingo Baba, Hiroshi HondaAbstract:Purpose To compare zoomed diffusion-weighted imaging (z-DWI) with reduced field of view (FOV) by spatially selective radiofrequency pulses and conventional echo planar imaging (EPI) DWI (c-DWI) with regard to Registration Quality using positron emission tomography / magnetic resonance (PET/MR) in patients with malignant tumors. Materials and Methods Fludeoxyglucose (18F) PET imaging, c-DWI, and z-DWI were conducted simultaneously in 21 patients with known or suspected malignancy using a PET/MR system. A fusion image showing the largest tumor area was generated for analysis. Registration accuracy between PET and DWI was assessed based on the area of maximum overlap and central point displacement of the tumor. EPI factor, echo time (TE), matching area, and displacement were compared between c-DWI and z-DWI by paired t-test. Agreement of apparent diffusion coefficient (ADC) acquired by the two sequences were also assessed with linear regression s and Bland–Altman plot analysis. Results Thirty-two lesions were detected on both PET and DWI (mean size 536.3 ± 471.8 mm2). At least one lesion was found in all subjects. In all cases, EPI factor was smaller with z-DWI than c-DWI (43.1 ± 15.6 vs. 62.0 ± 10.0, P < 0.0001), and TE was also shorter for z-DWI (53.6 ± 3.6 msec vs. 65.2 ± 3.6 msec, P < 0.0001). Registration accuracy was better with z-DWI in 30 of 32 lesions (93.8%), and both average matching area and central point displacement were significantly improved (79.8 ± 18.1% vs. 61.8 ± 22.9%, P < 0.0001 and 3.92 ± 2.69 mm vs. 7.51 ± 4.07 mm, P < 0.0001). ADC values calculated with c-DWI and z-DWI showed good agreement. Conclusion Zoomed DWI reduces image distortion and provides better Registration accuracy with PET images. J. Magn. Reson. Imaging 2015.
Sanjiv S. Samant - One of the best experts on this subject based on the ideXlab platform.
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SU-GG-I-116: A Neural Network Based Registration Quality Evaluator for 2D-3D Image Registrations
Medical Physics, 2010Co-Authors: Martin J Murphy, Sanjiv S. SamantAbstract:Purpose: To construct a Registration Quality evaluator (RQE) for 2D–3D Registrations that can automatically identify unsuccessful Registration solutions. Method and Materials: Two orthogonal kV x‐ray projections of an anthropomorphic cranial phantom were acquired with the Elekta Synergy system. The training dataset for the RQE construction was generated by registering the two x‐ray images simultaneously to the CTimage of the same phantom 300 times. The Registration optimized the normalized mutual information (NMI) between the two radiographs and their corresponding digitally reconstructed radiographs that were computed from the CT dataset. For each Registration repetition, a random initial alignment was used. The average voxel deviations within the region‐of‐interest between the best known alignment and the Registration solutions were computed. The Registration solutions of the training dataset were categorized as “successful” and “unsuccessful” Registrations by comparing the average voxel deviations with a user defined error threshold. For each Registration solution, the symmetry and the distinctiveness that represent the local geometrical properties of the similarity measure function were computed. The supervised training was used to train a two‐layer feed‐forward neural network using above generated data. The network RQE was then used to evaluate Registrations in a test data set. The confusion matrices and receiver operating characteristic (ROC) curves were used to evaluate the performance of the RQE. Results: RQE training yielded a sensitivity and a specificity of 0.944 and 0.971, respectively, for the training dataset. The sensitivity and the specificity were 0.955 and 1.00, respectively, for the test dataset. The ROC curves also confirmed the very good performance of the RQE. Conclusion: Our phantom study showed RQE had very good performance in identifying unacceptable results in 2D–3D Registrations. As part of an automated patient positioning system, RQE can be combined with a 2D–3D Registration algorithm to avoid local optima and improve robustness.
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SU‐FF‐I‐69: A 2D‐3D Registration Quality Evaluator for Patient Positioning in Radiotherapy
Medical Physics, 2007Co-Authors: Sanjiv S. SamantAbstract:Purpose: To construct a Registration Quality evaluator (RQE) for 2D‐3D Registration to automatically determine the “goodness” of a Registration for user‐defined error tolerance based on average pixel shift in the region of interest, a more consistent quantification of Registration error, rather than conventional units in mm and degree. Method and Materials: RQE was automatically constructed from two orthogonal kilovoltage portal images and their corresponding CT dataset of an anthropomorphic cranial phantom. Digitally reconstructed radiographs (DRRs) were generated from a registered CT dataset by adding known arbitrary displacements. Then normalized mutual information (NMI) values between each pair of DRRs and their corresponding portal images were computed, and associated with the known Registration error. Based on a user‐defined error tolerance, each sample, which includes a NMI value and its known Registration error, was classified as a successful or unsuccessful Registration. Finally, supervised learning was performed to calculate a decision threshold on NMI. To determine the goodness of a Registration, the Registration error can be estimated by calculating the NMI related to the Registration output and comparing the value with the RQE threshold. To estimate RQE performance, radio‐opaque markers were attached to the phantom and marker‐based Registrations were carried out independently to establish a gold standard. In the absence of markers, this standard can be established by using multiple runs of a Registration algorithm alongside visual verification. Results: RQE training yielded a sensitivity and a specificity of 0.9804 (0.8955–0.9995) and 0.9388 (0.8313–0.9872) respectively at 95% confidence interval. Using test dataset, the sensitivity and the specificity of RQE were 0.939 and 0.937, respectively. Conclusion: Our phantom study showed RQE had very good performance in identifying Registration errors in 2D‐3D Registrations. As part of an automated patient positioning system, RQE can be combined with a 2D‐3D Registration algorithm to avoid local optima and improve robustness.
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Novel image Registration Quality evaluator (RQE) with an implementation for automated patient positioning in cranial radiation therapy
Medical physics, 2007Co-Authors: Sanjiv S. SamantAbstract:In external beam radiation therapy, digitally reconstructed radiographs (DRRs) and portal images are used to verify patient setup based either on a visual comparison or, less frequently, with automated Registration algorithms. A Registration algorithm can be trapped in local optima due to irregularity of patient anatomy, image noise and artifacts, and/or out-of-plane shifts, resulting in an incorrect solution. Thus, human observation, which is subjective, is still required to check the Registration result. We propose to use a novel image Registration Quality evaluator (RQE) to automatically identify misRegistrations as part of an algorithm-based decision-making process for verification of patient positioning. A RQE, based on an adaptive pattern classifier, is generated from a pair of reference and target images to determine the acceptability of a Registration solution given an optimization process. Here we applied our RQE to patient positioning for cranial radiation therapy. We constructed two RQEs-one for the evaluation of intramodal Registrations (i.e., portal-portal); the other for intermodal Registrations (i.e., portal-DRR). Mutual information, because of its high discriminatory ability compared with other measures (i.e., correlation coefficient and partitioned intensity uniformity), was chosen as the test function for both RQEs. We adopted 1 mm translation and 1 degree rotation as the maximal acceptable Registration errors, reflecting desirable clinical setup tolerances for cranial radiation therapy. Receiver operating characteristic analysis was used to evaluate the performance of the RQE, including computations of sensitivity and specificity. The RQEs showed very good performance for both intramodal and intermodal Registrations using simulated and phantom data. The sensitivity and the specificity were 0.973 and 0.936, respectively, for the intramodal RQE using phantom data. Whereas the sensitivity and the specificity were 0.961 and 0.758, respectively, for the intermodal RQE using phantom data. Phantom experiments also indicated our RQEs detected out-of-plane deviations exceeding 2.5 mm and 2.50. A preliminary retrospective clinical study of the RQE on cranial portal imaging also yielded good sensitivity > or = 0.857) and specificity (> or = 0.987). Clinical implementation of a RQE could potentially reduce the involvement of the human observer for routine patient positioning verification, while increasing setup accuracy and reducing setup verification time.
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Patient Positioning Using a Fast Robust 2D-3D Registration Strategy With a Registration Quality Evaluator
International Journal of Radiation Oncology*Biology*Physics, 2007Co-Authors: M. Kim, J. Peters, C Liu, J. Palta, Sanjiv S. SamantAbstract:International Journal of Radiation Oncology, Biology, Physics 69 (2007) S43-S43. doi:10.1016/j.ijrobp.2007.07.079Received by publisher: 0000-01-01Harvest Date: 2016-01-04 12:22:16DOI: 10.1016/j.ijrobp.2007.07.079Page Range: S43-S4
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Medical Imaging: Image Processing - Registration Quality evaluator: application to automated patient setup verification in radiotherapy
Medical Imaging 2004: Image Processing, 2004Co-Authors: Sanjiv S. SamantAbstract:An image Registration Quality evaluator (RQE) is proposed to automatically quantify the accuracy of Registrations. The RQE, based on an adaptive pattern classifier, is generated from a pair of reference and target images. It is unique to each patient, anatomical site and imaging modality. RQE is applied to patient positioning in cranial radiotherapy using portal/portal and portal/DRR Registrations. We adopted 1mm translation and 1° rotation as the maximal acceptable Registration errors, reflecting typical clinical setup tolerances. RQE is used to determine the acceptability of a Registration. The performance of RQE was evaluated using phantom images containing radio-opaque fiducial markers. Using receiver operating characteristic (ROC) analysis, we estimated the sensitivity and the specificity of the RQE are 0.95 (with 0.89-0.98 confidence interval (CI) at 95% significance level) and 0.95 (with 0.88-0.98 CI at 95% significance level) respectively for intramodal RQE. For intermodal RQE, the sensitivity and the specificity are 0.92 (with 0.81-0.98 CI at 95% significance level) and 0.98 (with 0.89-0.99 CI at 95% significance level) respectively. Clinical use of RQE could significantly reduce the involvement of the oncologist for routine pre-treatment patient positioning verification, while increasing setup accuracy.
Martin J Murphy - One of the best experts on this subject based on the ideXlab platform.
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A neural network based 3D/3D image Registration Quality evaluator for the head‐and‐neck patient setup in the absence of a ground truth
Medical Physics, 2010Co-Authors: Martin J MurphyAbstract:Purpose: To develop a neural network based Registration Quality evaluator (RQE) that can identify unsuccessful 3D/3D image Registrations for the head-and-neck patient setup in radiotherapy. Methods: A two-layer feed-forward neural network was used as a RQE to classify 3D/3D rigid Registration solutions as successful or unsuccessful based on the features of the similarity surface near the point-of-solution. The supervised training and test data sets were generated by rigidly registering daily cone-beam CTs to the treatment planning fan-beam CTs of six patients with head-and-neck tumors. Two different similarity metrics (mutual information and mean-squared intensity difference) and two different types of image content (entire image versus bony landmarks) were used. The best solution for each Registration pair was selected from 50 optimizing attempts that differed only by the initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error threshold to determine whether that solution was successful or not. The supervised training was then used to train the RQE. The performance of the RQE was evaluated using the test data set that consisted of Registration results that were not used in training. Results: The RQE constructed using the mutual information had very good performance when tested using the test data sets, yielding the sensitivity, the specificity, the positive predictive value, and the negative predictive value in the ranges of 0.960–1.000, 0.993–1.000, 0.983–1.000, and 0.909–1.000, respectively. Adding a RQE into a conventional 3D/3D image Registration system incurs only about 10%–20% increase of the overall processing time. Conclusions: The authors’ patient study has demonstrated very good performance of the proposed RQE when used with the mutual information in identifying unsuccessful 3D/3D Registrations for daily patient setup. The classifier had very good generality and required only to be trained once for each implementation. When the RQE is incorporated with an automated 3D/3D image Registration system, it can improve the robustness of the system.
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A neural network based 3D∕3D image Registration Quality evaluator for the head-and-neck patient setup in the absence of a ground truth
Medical physics, 2010Co-Authors: Martin J MurphyAbstract:Purpose: To develop a neural network based Registration Quality evaluator (RQE) that can identify unsuccessful 3D/3D image Registrations for the head-and-neck patient setup in radiotherapy. Methods: A two-layer feed-forward neural network was used as a RQE to classify 3D/3D rigid Registration solutions as successful or unsuccessful based on the features of the similarity surface near the point-of-solution. The supervised training and test data sets were generated by rigidly registering daily cone-beam CTs to the treatment planning fan-beam CTs of six patients with head-and-neck tumors. Two different similarity metrics (mutual information and mean-squared intensity difference) and two different types of image content (entire image versus bony landmarks) were used. The best solution for each Registration pair was selected from 50 optimizing attempts that differed only by the initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error threshold to determine whether that solution was successful or not. The supervised training was then used to train the RQE. The performance of the RQE was evaluated using the test data set that consisted of Registration results that were not used in training. Results: The RQE constructed using the mutual information had very good performance when tested using the test data sets, yielding the sensitivity, the specificity, the positive predictive value, and the negative predictive value in the ranges of 0.960–1.000, 0.993–1.000, 0.983–1.000, and 0.909–1.000, respectively. Adding a RQE into a conventional 3D/3D image Registration system incurs only about 10%–20% increase of the overall processing time. Conclusions: The authors’ patient study has demonstrated very good performance of the proposed RQE when used with the mutual information in identifying unsuccessful 3D/3D Registrations for daily patient setup. The classifier had very good generality and required only to be trained once for each implementation. When the RQE is incorporated with an automated 3D/3D image Registration system, it can improve the robustness of the system.
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SU‐GG‐I‐116: A Neural Network Based Registration Quality Evaluator for 2D–3D Image Registrations
Medical Physics, 2010Co-Authors: Martin J Murphy, Sunil Singh SamantAbstract:Purpose: To construct a Registration Quality evaluator (RQE) for 2D–3D Registrations that can automatically identify unsuccessful Registration solutions. Method and Materials: Two orthogonal kV x‐ray projections of an anthropomorphic cranial phantom were acquired with the Elekta Synergy system. The training dataset for the RQE construction was generated by registering the two x‐ray images simultaneously to the CTimage of the same phantom 300 times. The Registration optimized the normalized mutual information (NMI) between the two radiographs and their corresponding digitally reconstructed radiographs that were computed from the CT dataset. For each Registration repetition, a random initial alignment was used. The average voxel deviations within the region‐of‐interest between the best known alignment and the Registration solutions were computed. The Registration solutions of the training dataset were categorized as “successful” and “unsuccessful” Registrations by comparing the average voxel deviations with a user defined error threshold. For each Registration solution, the symmetry and the distinctiveness that represent the local geometrical properties of the similarity measure function were computed. The supervised training was used to train a two‐layer feed‐forward neural network using above generated data. The network RQE was then used to evaluate Registrations in a test data set. The confusion matrices and receiver operating characteristic (ROC) curves were used to evaluate the performance of the RQE. Results: RQE training yielded a sensitivity and a specificity of 0.944 and 0.971, respectively, for the training dataset. The sensitivity and the specificity were 0.955 and 1.00, respectively, for the test dataset. The ROC curves also confirmed the very good performance of the RQE. Conclusion: Our phantom study showed RQE had very good performance in identifying unacceptable results in 2D–3D Registrations. As part of an automated patient positioning system, RQE can be combined with a 2D–3D Registration algorithm to avoid local optima and improve robustness.
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SU-GG-I-116: A Neural Network Based Registration Quality Evaluator for 2D-3D Image Registrations
Medical Physics, 2010Co-Authors: Martin J Murphy, Sanjiv S. SamantAbstract:Purpose: To construct a Registration Quality evaluator (RQE) for 2D–3D Registrations that can automatically identify unsuccessful Registration solutions. Method and Materials: Two orthogonal kV x‐ray projections of an anthropomorphic cranial phantom were acquired with the Elekta Synergy system. The training dataset for the RQE construction was generated by registering the two x‐ray images simultaneously to the CTimage of the same phantom 300 times. The Registration optimized the normalized mutual information (NMI) between the two radiographs and their corresponding digitally reconstructed radiographs that were computed from the CT dataset. For each Registration repetition, a random initial alignment was used. The average voxel deviations within the region‐of‐interest between the best known alignment and the Registration solutions were computed. The Registration solutions of the training dataset were categorized as “successful” and “unsuccessful” Registrations by comparing the average voxel deviations with a user defined error threshold. For each Registration solution, the symmetry and the distinctiveness that represent the local geometrical properties of the similarity measure function were computed. The supervised training was used to train a two‐layer feed‐forward neural network using above generated data. The network RQE was then used to evaluate Registrations in a test data set. The confusion matrices and receiver operating characteristic (ROC) curves were used to evaluate the performance of the RQE. Results: RQE training yielded a sensitivity and a specificity of 0.944 and 0.971, respectively, for the training dataset. The sensitivity and the specificity were 0.955 and 1.00, respectively, for the test dataset. The ROC curves also confirmed the very good performance of the RQE. Conclusion: Our phantom study showed RQE had very good performance in identifying unacceptable results in 2D–3D Registrations. As part of an automated patient positioning system, RQE can be combined with a 2D–3D Registration algorithm to avoid local optima and improve robustness.
Koji Sagiyama - One of the best experts on this subject based on the ideXlab platform.
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comparison of positron emission tomography diffusion weighted imaging pet dwi Registration Quality in a pet mr scanner zoomed dwi vs conventional dwi
Journal of Magnetic Resonance Imaging, 2016Co-Authors: Koji Sagiyama, Yuji Watanabe, Ryotaro Kamei, Shingo Baba, Hiroshi HondaAbstract:Purpose To compare zoomed diffusion-weighted imaging (z-DWI) with reduced field of view (FOV) by spatially selective radiofrequency pulses and conventional echo planar imaging (EPI) DWI (c-DWI) with regard to Registration Quality using positron emission tomography / magnetic resonance (PET/MR) in patients with malignant tumors. Materials and Methods Fludeoxyglucose (18F) PET imaging, c-DWI, and z-DWI were conducted simultaneously in 21 patients with known or suspected malignancy using a PET/MR system. A fusion image showing the largest tumor area was generated for analysis. Registration accuracy between PET and DWI was assessed based on the area of maximum overlap and central point displacement of the tumor. EPI factor, echo time (TE), matching area, and displacement were compared between c-DWI and z-DWI by paired t-test. Agreement of apparent diffusion coefficient (ADC) acquired by the two sequences were also assessed with linear regression s and Bland–Altman plot analysis. Results Thirty-two lesions were detected on both PET and DWI (mean size 536.3 ± 471.8 mm2). At least one lesion was found in all subjects. In all cases, EPI factor was smaller with z-DWI than c-DWI (43.1 ± 15.6 vs. 62.0 ± 10.0, P < 0.0001), and TE was also shorter for z-DWI (53.6 ± 3.6 msec vs. 65.2 ± 3.6 msec, P < 0.0001). Registration accuracy was better with z-DWI in 30 of 32 lesions (93.8%), and both average matching area and central point displacement were significantly improved (79.8 ± 18.1% vs. 61.8 ± 22.9%, P < 0.0001 and 3.92 ± 2.69 mm vs. 7.51 ± 4.07 mm, P < 0.0001). ADC values calculated with c-DWI and z-DWI showed good agreement. Conclusion Zoomed DWI reduces image distortion and provides better Registration accuracy with PET images. J. Magn. Reson. Imaging 2015.
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Comparison of positron emission tomography diffusion-weighted imaging (PET/DWI) Registration Quality in a PET/MR scanner: Zoomed DWI vs. Conventional DWI.
Journal of magnetic resonance imaging : JMRI, 2015Co-Authors: Koji Sagiyama, Yuji Watanabe, Ryotaro Kamei, Shingo Baba, Hiroshi HondaAbstract:Purpose To compare zoomed diffusion-weighted imaging (z-DWI) with reduced field of view (FOV) by spatially selective radiofrequency pulses and conventional echo planar imaging (EPI) DWI (c-DWI) with regard to Registration Quality using positron emission tomography / magnetic resonance (PET/MR) in patients with malignant tumors. Materials and Methods Fludeoxyglucose (18F) PET imaging, c-DWI, and z-DWI were conducted simultaneously in 21 patients with known or suspected malignancy using a PET/MR system. A fusion image showing the largest tumor area was generated for analysis. Registration accuracy between PET and DWI was assessed based on the area of maximum overlap and central point displacement of the tumor. EPI factor, echo time (TE), matching area, and displacement were compared between c-DWI and z-DWI by paired t-test. Agreement of apparent diffusion coefficient (ADC) acquired by the two sequences were also assessed with linear regression s and Bland–Altman plot analysis. Results Thirty-two lesions were detected on both PET and DWI (mean size 536.3 ± 471.8 mm2). At least one lesion was found in all subjects. In all cases, EPI factor was smaller with z-DWI than c-DWI (43.1 ± 15.6 vs. 62.0 ± 10.0, P < 0.0001), and TE was also shorter for z-DWI (53.6 ± 3.6 msec vs. 65.2 ± 3.6 msec, P < 0.0001). Registration accuracy was better with z-DWI in 30 of 32 lesions (93.8%), and both average matching area and central point displacement were significantly improved (79.8 ± 18.1% vs. 61.8 ± 22.9%, P < 0.0001 and 3.92 ± 2.69 mm vs. 7.51 ± 4.07 mm, P < 0.0001). ADC values calculated with c-DWI and z-DWI showed good agreement. Conclusion Zoomed DWI reduces image distortion and provides better Registration accuracy with PET images. J. Magn. Reson. Imaging 2015.
Florian Dubost - One of the best experts on this subject based on the ideXlab platform.
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multi atlas image Registration of clinical data with automated Quality assessment using ventricle segmentation
Medical Image Analysis, 2020Co-Authors: Florian Dubost, Marleen De Bruijne, Marco J Nardin, Adrian V Dalca, Kathleen L Donahue, Annekatrin Giese, Mark R Etherton, Ona WuAbstract:Abstract Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, Registration can produce poor results. Visual assessment of Registration Quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the Quality of Registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image Registration to a general atlas by computing multiple Registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the Registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with Registration Quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct Registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the Registration Quality assessment method could improve the Quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image Registration Quality in clinical scans. This image Quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.
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multi atlas image Registration of clinical data with automated Quality assessment using ventricle segmentation
arXiv: Image and Video Processing, 2019Co-Authors: Florian Dubost, Marleen De Bruijne, Marco J Nardin, Adrian V Dalca, Kathleen L Donahue, Annekatrin Giese, Mark R Etherton, Ona WuAbstract:Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, Registration can produce poor results. Visual assessment of Registration Quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the Quality of Registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas' ventricles propagated to image space. We used the proposed method to improve clinical image Registration to a general atlas by computing multiple Registrations and then selecting the Registration that yielded the highest ventricle overlap. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct Registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the Registration Quality assessment method could improve the Quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image Registration Quality in clinical scans. This image Quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.