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D Low - One of the best experts on this subject based on the ideXlab platform.
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su e j 156 velocity based reference scan selection for Motion Model error reduction of 5dct
Medical Physics, 2015Co-Authors: L Yang, David Thomas, J Lamb, Tai Dou, D Oconnell, D LowAbstract:Purpose: A recently proposed 5DCT protocol uses fast helical CT scans to Model lung tissue Motion that requires one of the scans to be used as a reference for deformable image registration. We have found that, while the CT scans are acquired using fast helical acquisition, residual Motionartifacts remain for scans acquired during mid inspiration or exhalation. Selecting the scan with minimal Motion artifacts as the reference scan will improve image registration and Model accuracy. Methods: A free-breathing patient was scanned 25 successive times in alternating directions with a Siemens 64-slice CT scanner using a low-mA fast-helical protocol. Each of the 25 CT scans was successively used as the reference scan with the other 24 images registered to it. Motion-Model parameters were determined using the breathing-Motion Model. Voxel specific tissue velocity maps were generated for each of the 25 scan based on the breathing amplitude surrogate and its time derivative. The resulting 24 Model error maps were registered to the first CT scan for comparison. The relationships between regional Motion-Model error, reference scan Motion-artifacts and tissue-velocity were analyzed using linear-regression. Results: Motion-Model errors were linearly correlated with reference scan blurring artifacts and doubling artifacts. The effects of blurring-artifacts on Motion Model errors were stronger than doubling artifacts due to their impact on registration accuracy. Selecting the lowest tissue Motion velocity scan as the reference scan decreased the regional Motion Model error by 33.67%±43.12%. Conclusion: Motion-artifacts, especially blurring artifacts, that exist in the reference image induce Motion Model errors in 5DCT. A carefully selected reference scan based on the lowest tissue velocities can minimize the regional Motion Model error.
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a novel fast helical 4d ct acquisition technique to generate low noise sorting artifact free images at user selected breathing phases
International Journal of Radiation Oncology Biology Physics, 2014Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, Dan Ruan, Michael F Mcnittgray, D LowAbstract:Purpose To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove sorting artifacts. Methods and Materials Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific Motion Model parameters were determined using a breathing Motion Model. The tissue locations predicted by the Motion Model in the 25 images were compared against the deformably registered tissue locations, allowing a Model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The Motion Model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results Images produced using the Model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing Motion Model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.
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tu g 141 01 best in physics joint imaging therapy a novel 4d ct acquisition and analysis technique to generate low noise artifact free images at user selected breathing phases
Medical Physics, 2013Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, D LowAbstract:Purpose: To develop a novel 4DCT technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove Motion‐induced artifacts. Methods: Five patients were imaged under free breathing conditions 25 successive times in alternating directions with a 64‐slice CT scanner using a low dose fast helical protocol. A pneumatic bellows around the abdomen was used to as a breathing surrogate. The lungs were segmented from each image. Deformable registration was used to register the first to the subsequent 24 segmented images. Voxel‐based Motion Model parameters were determined using a published breathing Motion Model. A low‐noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical noise by a factor of 5. The Motion Model was used to deform the low noise image to any user‐selected breathing phase. Accurate HU values were assigned to each voxel in the reconstructed images. Results: Images produced using the Model at user‐selected breathing phases did not suffer from Motion artifacts. The mean discrepancy between the breathing Motion Model results and the measured positions corresponding to each scan was determined to be 0.7mm (standard deviation of 0.4mm). In each patient, regions near to the myocardium exhibited mean discrepancies greater than 1 mm, which were likely due to uncompensated cardiac Motion. Conclusion: The proposed technique can be employed as a clinical 4DCT technique providing Motion artifact free images at user‐selected breathing phases. It is robust in the presence of irregular breathing, and allows the entire imaging dose to contribute to the resulting image quality, providing Motion artifact free images at a patient dose similar to or less than current 4DCT techniques. We are currently modifying the protocol to work on 16‐slice CT scanners. This work supported in part by NIH R01CA096679
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application of the continuity equation to a breathing Motion Model
Medical Physics, 2010Co-Authors: D Low, Tianyu Zhao, B White, D Yang, S Mutic, C Noel, Jeffrey D Bradley, P ParikhAbstract:Purpose: To quantitatively test a breathing Motion Model using the continuity equation and clinical data. Methods: The continuity equation was applied to a lung tissue and lung tumor free breathing Motion Model to quantitatively test the Model performance. The Model used tidal volume and airflow as the independent variables and the ratio of Motion to tidal volume and Motion to airflow were defined as α and β vector fields, respectively. The continuity equation resulted in a prediction that the volume integral of the divergence of the α vector field was 1.11 for all patients. The integral of the divergence of the β vector field was expected to be zero. Results: For 35 patients, the α vector field prediction was 1.06±0.14, encompassing the expected value. For the β vector field prediction, the average value was 0.02±0.03. Conclusions: These results provide quantitative evidence that the breathing Motion Model yields accurate predictions of breathing dynamics.
J Lamb - One of the best experts on this subject based on the ideXlab platform.
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dependence of subject specific parameters for a fast helical ct respiratory Motion Model on breathing rate an animal study
Physics in Medicine and Biology, 2018Co-Authors: Dylan Oconnell, David Thomas, J Lamb, John H Lewis, Tai Dou, Jered Sieren, Melissa Saylor, Christian Hofmann, Eric A Hoffman, Percy LeeAbstract:To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory Motion Model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of Motion Model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The Model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one Model parameter, a vector field denoted [Formula: see text] which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean Model error of the three datasets was 1.00 ± 0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14 ± 0.42 mm with a 95th percentile of 1.95 mm. The mean difference in [Formula: see text] over all pairs of experiments was 4.7% ± 5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of [Formula: see text] was 5.0 ± 4.0 degrees, with a 95th percentile of 13.2 mm. The Motion Model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory Motion was 23.23 mm on average.
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su e j 156 velocity based reference scan selection for Motion Model error reduction of 5dct
Medical Physics, 2015Co-Authors: L Yang, David Thomas, J Lamb, Tai Dou, D Oconnell, D LowAbstract:Purpose: A recently proposed 5DCT protocol uses fast helical CT scans to Model lung tissue Motion that requires one of the scans to be used as a reference for deformable image registration. We have found that, while the CT scans are acquired using fast helical acquisition, residual Motionartifacts remain for scans acquired during mid inspiration or exhalation. Selecting the scan with minimal Motion artifacts as the reference scan will improve image registration and Model accuracy. Methods: A free-breathing patient was scanned 25 successive times in alternating directions with a Siemens 64-slice CT scanner using a low-mA fast-helical protocol. Each of the 25 CT scans was successively used as the reference scan with the other 24 images registered to it. Motion-Model parameters were determined using the breathing-Motion Model. Voxel specific tissue velocity maps were generated for each of the 25 scan based on the breathing amplitude surrogate and its time derivative. The resulting 24 Model error maps were registered to the first CT scan for comparison. The relationships between regional Motion-Model error, reference scan Motion-artifacts and tissue-velocity were analyzed using linear-regression. Results: Motion-Model errors were linearly correlated with reference scan blurring artifacts and doubling artifacts. The effects of blurring-artifacts on Motion Model errors were stronger than doubling artifacts due to their impact on registration accuracy. Selecting the lowest tissue Motion velocity scan as the reference scan decreased the regional Motion Model error by 33.67%±43.12%. Conclusion: Motion-artifacts, especially blurring artifacts, that exist in the reference image induce Motion Model errors in 5DCT. A carefully selected reference scan based on the lowest tissue velocities can minimize the regional Motion Model error.
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a novel fast helical 4d ct acquisition technique to generate low noise sorting artifact free images at user selected breathing phases
International Journal of Radiation Oncology Biology Physics, 2014Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, Dan Ruan, Michael F Mcnittgray, D LowAbstract:Purpose To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove sorting artifacts. Methods and Materials Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific Motion Model parameters were determined using a breathing Motion Model. The tissue locations predicted by the Motion Model in the 25 images were compared against the deformably registered tissue locations, allowing a Model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The Motion Model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results Images produced using the Model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing Motion Model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.
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tu g 141 01 best in physics joint imaging therapy a novel 4d ct acquisition and analysis technique to generate low noise artifact free images at user selected breathing phases
Medical Physics, 2013Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, D LowAbstract:Purpose: To develop a novel 4DCT technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove Motion‐induced artifacts. Methods: Five patients were imaged under free breathing conditions 25 successive times in alternating directions with a 64‐slice CT scanner using a low dose fast helical protocol. A pneumatic bellows around the abdomen was used to as a breathing surrogate. The lungs were segmented from each image. Deformable registration was used to register the first to the subsequent 24 segmented images. Voxel‐based Motion Model parameters were determined using a published breathing Motion Model. A low‐noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical noise by a factor of 5. The Motion Model was used to deform the low noise image to any user‐selected breathing phase. Accurate HU values were assigned to each voxel in the reconstructed images. Results: Images produced using the Model at user‐selected breathing phases did not suffer from Motion artifacts. The mean discrepancy between the breathing Motion Model results and the measured positions corresponding to each scan was determined to be 0.7mm (standard deviation of 0.4mm). In each patient, regions near to the myocardium exhibited mean discrepancies greater than 1 mm, which were likely due to uncompensated cardiac Motion. Conclusion: The proposed technique can be employed as a clinical 4DCT technique providing Motion artifact free images at user‐selected breathing phases. It is robust in the presence of irregular breathing, and allows the entire imaging dose to contribute to the resulting image quality, providing Motion artifact free images at a patient dose similar to or less than current 4DCT techniques. We are currently modifying the protocol to work on 16‐slice CT scanners. This work supported in part by NIH R01CA096679
Percy Lee - One of the best experts on this subject based on the ideXlab platform.
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dependence of subject specific parameters for a fast helical ct respiratory Motion Model on breathing rate an animal study
Physics in Medicine and Biology, 2018Co-Authors: Dylan Oconnell, David Thomas, J Lamb, John H Lewis, Tai Dou, Jered Sieren, Melissa Saylor, Christian Hofmann, Eric A Hoffman, Percy LeeAbstract:To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory Motion Model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of Motion Model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The Model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one Model parameter, a vector field denoted [Formula: see text] which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean Model error of the three datasets was 1.00 ± 0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14 ± 0.42 mm with a 95th percentile of 1.95 mm. The mean difference in [Formula: see text] over all pairs of experiments was 4.7% ± 5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of [Formula: see text] was 5.0 ± 4.0 degrees, with a 95th percentile of 13.2 mm. The Motion Model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory Motion was 23.23 mm on average.
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a novel fast helical 4d ct acquisition technique to generate low noise sorting artifact free images at user selected breathing phases
International Journal of Radiation Oncology Biology Physics, 2014Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, Dan Ruan, Michael F Mcnittgray, D LowAbstract:Purpose To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove sorting artifacts. Methods and Materials Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific Motion Model parameters were determined using a breathing Motion Model. The tissue locations predicted by the Motion Model in the 25 images were compared against the deformably registered tissue locations, allowing a Model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The Motion Model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results Images produced using the Model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing Motion Model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.
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tu g 141 01 best in physics joint imaging therapy a novel 4d ct acquisition and analysis technique to generate low noise artifact free images at user selected breathing phases
Medical Physics, 2013Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, D LowAbstract:Purpose: To develop a novel 4DCT technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove Motion‐induced artifacts. Methods: Five patients were imaged under free breathing conditions 25 successive times in alternating directions with a 64‐slice CT scanner using a low dose fast helical protocol. A pneumatic bellows around the abdomen was used to as a breathing surrogate. The lungs were segmented from each image. Deformable registration was used to register the first to the subsequent 24 segmented images. Voxel‐based Motion Model parameters were determined using a published breathing Motion Model. A low‐noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical noise by a factor of 5. The Motion Model was used to deform the low noise image to any user‐selected breathing phase. Accurate HU values were assigned to each voxel in the reconstructed images. Results: Images produced using the Model at user‐selected breathing phases did not suffer from Motion artifacts. The mean discrepancy between the breathing Motion Model results and the measured positions corresponding to each scan was determined to be 0.7mm (standard deviation of 0.4mm). In each patient, regions near to the myocardium exhibited mean discrepancies greater than 1 mm, which were likely due to uncompensated cardiac Motion. Conclusion: The proposed technique can be employed as a clinical 4DCT technique providing Motion artifact free images at user‐selected breathing phases. It is robust in the presence of irregular breathing, and allows the entire imaging dose to contribute to the resulting image quality, providing Motion artifact free images at a patient dose similar to or less than current 4DCT techniques. We are currently modifying the protocol to work on 16‐slice CT scanners. This work supported in part by NIH R01CA096679
D Thomas - One of the best experts on this subject based on the ideXlab platform.
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a novel fast helical 4d ct acquisition technique to generate low noise sorting artifact free images at user selected breathing phases
International Journal of Radiation Oncology Biology Physics, 2014Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, Dan Ruan, Michael F Mcnittgray, D LowAbstract:Purpose To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove sorting artifacts. Methods and Materials Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific Motion Model parameters were determined using a breathing Motion Model. The tissue locations predicted by the Motion Model in the 25 images were compared against the deformably registered tissue locations, allowing a Model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The Motion Model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results Images produced using the Model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing Motion Model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.
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tu g 141 01 best in physics joint imaging therapy a novel 4d ct acquisition and analysis technique to generate low noise artifact free images at user selected breathing phases
Medical Physics, 2013Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, D LowAbstract:Purpose: To develop a novel 4DCT technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove Motion‐induced artifacts. Methods: Five patients were imaged under free breathing conditions 25 successive times in alternating directions with a 64‐slice CT scanner using a low dose fast helical protocol. A pneumatic bellows around the abdomen was used to as a breathing surrogate. The lungs were segmented from each image. Deformable registration was used to register the first to the subsequent 24 segmented images. Voxel‐based Motion Model parameters were determined using a published breathing Motion Model. A low‐noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical noise by a factor of 5. The Motion Model was used to deform the low noise image to any user‐selected breathing phase. Accurate HU values were assigned to each voxel in the reconstructed images. Results: Images produced using the Model at user‐selected breathing phases did not suffer from Motion artifacts. The mean discrepancy between the breathing Motion Model results and the measured positions corresponding to each scan was determined to be 0.7mm (standard deviation of 0.4mm). In each patient, regions near to the myocardium exhibited mean discrepancies greater than 1 mm, which were likely due to uncompensated cardiac Motion. Conclusion: The proposed technique can be employed as a clinical 4DCT technique providing Motion artifact free images at user‐selected breathing phases. It is robust in the presence of irregular breathing, and allows the entire imaging dose to contribute to the resulting image quality, providing Motion artifact free images at a patient dose similar to or less than current 4DCT techniques. We are currently modifying the protocol to work on 16‐slice CT scanners. This work supported in part by NIH R01CA096679
B White - One of the best experts on this subject based on the ideXlab platform.
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a novel fast helical 4d ct acquisition technique to generate low noise sorting artifact free images at user selected breathing phases
International Journal of Radiation Oncology Biology Physics, 2014Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, Dan Ruan, Michael F Mcnittgray, D LowAbstract:Purpose To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove sorting artifacts. Methods and Materials Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific Motion Model parameters were determined using a breathing Motion Model. The tissue locations predicted by the Motion Model in the 25 images were compared against the deformably registered tissue locations, allowing a Model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The Motion Model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results Images produced using the Model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing Motion Model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.
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tu g 141 01 best in physics joint imaging therapy a novel 4d ct acquisition and analysis technique to generate low noise artifact free images at user selected breathing phases
Medical Physics, 2013Co-Authors: D Thomas, J Lamb, Percy Lee, B White, S Gaudio, S Jani, D LowAbstract:Purpose: To develop a novel 4DCT technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing Motion Model to remove Motion‐induced artifacts. Methods: Five patients were imaged under free breathing conditions 25 successive times in alternating directions with a 64‐slice CT scanner using a low dose fast helical protocol. A pneumatic bellows around the abdomen was used to as a breathing surrogate. The lungs were segmented from each image. Deformable registration was used to register the first to the subsequent 24 segmented images. Voxel‐based Motion Model parameters were determined using a published breathing Motion Model. A low‐noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical noise by a factor of 5. The Motion Model was used to deform the low noise image to any user‐selected breathing phase. Accurate HU values were assigned to each voxel in the reconstructed images. Results: Images produced using the Model at user‐selected breathing phases did not suffer from Motion artifacts. The mean discrepancy between the breathing Motion Model results and the measured positions corresponding to each scan was determined to be 0.7mm (standard deviation of 0.4mm). In each patient, regions near to the myocardium exhibited mean discrepancies greater than 1 mm, which were likely due to uncompensated cardiac Motion. Conclusion: The proposed technique can be employed as a clinical 4DCT technique providing Motion artifact free images at user‐selected breathing phases. It is robust in the presence of irregular breathing, and allows the entire imaging dose to contribute to the resulting image quality, providing Motion artifact free images at a patient dose similar to or less than current 4DCT techniques. We are currently modifying the protocol to work on 16‐slice CT scanners. This work supported in part by NIH R01CA096679
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application of the continuity equation to a breathing Motion Model
Medical Physics, 2010Co-Authors: D Low, Tianyu Zhao, B White, D Yang, S Mutic, C Noel, Jeffrey D Bradley, P ParikhAbstract:Purpose: To quantitatively test a breathing Motion Model using the continuity equation and clinical data. Methods: The continuity equation was applied to a lung tissue and lung tumor free breathing Motion Model to quantitatively test the Model performance. The Model used tidal volume and airflow as the independent variables and the ratio of Motion to tidal volume and Motion to airflow were defined as α and β vector fields, respectively. The continuity equation resulted in a prediction that the volume integral of the divergence of the α vector field was 1.11 for all patients. The integral of the divergence of the β vector field was expected to be zero. Results: For 35 patients, the α vector field prediction was 1.06±0.14, encompassing the expected value. For the β vector field prediction, the average value was 0.02±0.03. Conclusions: These results provide quantitative evidence that the breathing Motion Model yields accurate predictions of breathing dynamics.
