The Experts below are selected from a list of 9477 Experts worldwide ranked by ideXlab platform

Lei Zhu - One of the best experts on this subject based on the ideXlab platform.

  • the role of off focus radiation in scatter correction for dedicated cone beam breast ct
    Medical Physics, 2018
    Co-Authors: Lei Zhu, Linxi Shi, Srinivasan Vedantham, Andrew Karellas
    Abstract:

    Purpose Dedicated cone beam breast CT (CBBCT) suffers from x-ray scatter contamination. We aim to identify the source of the significant difference between the scatter distributions estimated by two recent methods proposed by our group and to investigate its effect on CBBCT image quality. Method We recently proposed two novel methods of scatter correction for CBBCT, using a library based (LB) technique and a Forward Projection (FP) model. Despite similar enhancement on CBBCT image qualities, these two methods obtain very different scatter distributions. We hypothesize that the off-focus radiation (OFR) is the contributor and results in nontrivial signals in x-ray Projections, which is ignored in the scatter estimation via the LB method. Experiments using a thin wire test tool are designed to study the effect of OFR on CBBCT spatial resolution by measuring the point spread function (PSF) and the modulation transfer function (MTF). A narrow collimator setting is used to suppress the OFR-induced signals. In addition, "PSFs" and "MTFs" are measured on clinical CBBCT images obtained by the LB and FP methods using small calcifications as point sources. The improvement of spatial resolution achieved by suppressing OFR in the wire experiment as well as in the clinical study is quantified by the improvement ratios of PSFs and spatial frequencies at different MTF values. Our hypothesis that OFR causes the imaging difference between the FP and LB methods is verified if these ratios obtained from experimental and clinical data are consistent. Results In the wire experiment, the results show that suppression of OFR increases the maximum signal of the PSF by about 14% and reduces the full-width-at-half-maximum (FWHM) by about 12.0%. Similar improvement on spatial resolution is achieved by the FP method compared with the LB method in the patient study. The improvement ratios of spatial frequencies at different MTF values without OFR match very well in both studies at a level of around 16%, with an average root-mean-square difference of 0.47%. Conclusion The results of the wire experiment and the clinical study indicate that the main difference between the LB and FP methods is whether the OFR-induced signals are included after scatter correction. Our study further shows that OFR significantly affects the image spatial resolution of CBBCT, indicating that the visualization of micro-calcifications is susceptible to OFR contamination. Our finding is therefore important in further improvement of diagnostic performance of CBBCT.

  • quantitative cone beam ct imaging in radiation therapy using planning ct as a prior first patient studies
    Medical Physics, 2012
    Co-Authors: Tianye Niu, A Albasheer, Lei Zhu
    Abstract:

    Purpose: Quantitative cone-beam CT (CBCT) imaging is on increasing demand for high-performance image guided radiation therapy (IGRT). However, the current CBCT has poor image qualities mainly due to scatter contamination. Its current clinical application is therefore limited to patient setup based on only bony structures. To improve CBCT imaging for quantitative use, we recently proposed a correction method using planning CT (pCT) as the prior knowledge. Promising phantom results have been obtained on a tabletop CBCT system, using a correction scheme with rigid registration and without iterations. More challenges arise in clinical implementations of our method, especially because patients have large organ deformation in different scans. In this paper, we propose an improved framework to extend our method from bench to bedside by including several new components. Methods: The basic principle of our correction algorithm is to estimate the primary signals of CBCT Projections via Forward Projection on the pCT image, and then to obtain the low-frequency errors in CBCT raw Projections by subtracting the estimated primary signals and low-pass filtering. We improve the algorithm by using deformable registration to minimize the geometry difference between the pCT and the CBCT images. Since the registration performance relies on the accuracy of the CBCT image, we design an optional iterative scheme to update the CBCT image used in the registration. Large correction errors result from the mismatched objects in the pCT and the CBCT scans. Another optional step of gas pocket and couch matching is added into the framework to reduce these effects. Results: The proposed method is evaluated on four prostate patients, of which two cases are presented in detail to investigate the method performance for a large variety of patient geometry in clinical practice. The first patient has small anatomical changes from the planning to the treatment room. Our algorithm works well even without the optional iterations and the gas pocket and couch matching. The image correction on the second patient is more challenging due to the effects of gas pockets and attenuating couch. The improved framework with all new components is used to fully evaluate the correction performance. The enhanced image quality has been evaluated using mean CT number and spatial nonuniformity (SNU) error as well as contrast improvement factor. If the pCT image is considered as the ground truth, on the four patients, the overall mean CT number error is reduced from over 300 HU to below 16 HU in the selected regions of interest (ROIs), and the SNU error is suppressed from over 18% to below 2%. The average soft-tissue contrast is improved by an average factor of 2.6. Conclusions: We further improve our pCT-based CBCT correction algorithm for clinical use. Superior correction performance has been demonstrated on four patient studies. By providing quantitative CBCT images, our approach significantly increases the accuracy of advanced CBCT-based clinical applications for IGRT.

  • shading correction for on board cone beam ct in radiation therapy using planning mdct images
    Medical Physics, 2010
    Co-Authors: Tianye Niu, Josh Starlack, Hewei Gao, Mingshan Sun, Qiyong Fan, Lei Zhu
    Abstract:

    Purpose: Applications of cone-beam CT(CBCT) to image-guided radiation therapy (IGRT) are hampered by shading artifacts in the reconstructed images. These artifacts are mainly due to scatter contamination in the Projections but also can result from uncorrected beam hardening effects as well as nonlinearities in responses of the amorphous silicon flat panel detectors. While currently, CBCT is mainly used to provide patient geometry information for treatment setup, more demanding applications requiring high-quality CBCTimages are under investigation. To tackle these challenges, many CBCT correction algorithms have been proposed; yet, a standard approach still remains unclear. In this work, we propose a shading correction method for CBCT that addresses artifacts from low-frequency Projection errors. The method is consistent with the current workflow of radiation therapy. Methods: With much smaller inherent scatter signals and more accurate detectors, diagnostic multidetector CT (MDCT) provides high quality CTimages that are routinely used for radiation treatment planning. Using the MDCT image as “free” prior information, we first estimate the primary Projections in the CBCT scan via Forward Projection of the spatially registered MDCT data. Since most of the CBCT shading artifacts stem from low-frequency errors in the Projections such as scatter, these errors can be accurately estimated by low-pass filtering the difference between the estimated and raw CBCT Projections. The error estimates are then subtracted from the raw CBCT Projections. Our method is distinct from other published correction methods that use the MDCT image as a prior because it is Projection-based and uses limited patient anatomical information from the MDCT image. The merit of CBCT-based treatment monitoring is therefore retained. Results: The proposed method is evaluated using two phantom studies on tabletop systems. On the Catphan©600 phantom, our approach reduces the reconstruction error from 348 Hounsfield unit (HU) without correction to 4 HU around the object center after correction, and from 375 HU to 17 HU in the high-contrast regions. In the selected regions of interest (ROIs), the average image contrast is increased by a factor of 3.3. When noise suppression is implemented, the proposed correction substantially improves the contrast-to-noise ratio(CNR) and therefore the visibility of low-contrast objects, as seen in a more challenging pelvis phantom study. Besides a significant improvement in image uniformity, a low-contrast object of ∼ 25 HU , which is otherwise buried in the shading artifacts, can be clearly identified after the proposed correction due to a CNR increase of 3.1. Compared to a kernel-based scatter correction method coupled with an analytical beam hardening correction, our approach also shows an overall improved performance with some residual artifacts. Conclusions: By providing effective shading correction, our approach has the potential to improve the accuracy of more advanced CBCT-based clinical applications for IGRT, such as tumor delineation and dose calculation.

  • tu d 204b 01 scatter correction for on board cone beam ct in radiation therapy using planning mdct images
    Medical Physics, 2010
    Co-Authors: Tianye Niu, Josh Starlack, Hewei Gao, Mingshan Sun, Qiyong Fan, Lei Zhu
    Abstract:

    Purpose: The applications of cone‐beam CT(CBCT)imaging in radiation therapy are greatly hampered by the poor image quality mainly due to scatter artifacts. The current use of CBCT is only limited to treatment setup, and an optimal scatter correction solution still remains unclear. Here, we propose a new scatter correction method for CBCT based on the current workflow of radiation therapy.Methods and Materials: With much smaller inherent scatter signals, diagnostic multi‐detector CT (MDCT) provides more accurate CTimages and is routinely used for radiation treatment planning. Using the MDCT image as the “free” prior information, we first estimate the primary Projections in the CBCT scan via Forward Projection on MDCT data. Since the deviation between patient geometries in the CBCT and the registered MDCT data only leads to high‐frequency primary Projection differences and scatter has dominant low‐frequency components, CBCT scatter signals are accurately estimated by low‐pass filtering and effectively corrected for after subtraction. The proposed method is evaluated using two phantom studies on tabletop systems. Results: On the Catphan©600 phantom, the reconstruction error is reduced from 348 HU to 4 HU around the phantom center. In the selected regions of interest, the average imagecontrast is increased by a factor of 3.3. This contrast increase improves low‐contrast detectability, as seen in the pelvis phantom study. Besides a significant improvement of image quality, a 25 HU object, which is otherwise buried in the scatter artifacts, can be clearly identified after the proposed scatter correction. Compared to the kernel‐based method, our approach shows an improved performance. Conclusions: Effective scatter correction is achieved on CBCT using our MDCT‐based approach. The increased accuracy of CBCTimaging substantially facilitates CBCT‐based clinical applications, such as tumor delineation and dose calculation. As such, the proposed method will be very attractive in current radiation therapy.

Tianye Niu - One of the best experts on this subject based on the ideXlab platform.

  • scatter correction for a clinical cone beam ct system using an optimized stationary beam blocker in a single scan
    Medical Physics, 2019
    Co-Authors: Xiaokun Liang, Zhicheng Zhang, Qinxuan Zhou, Yangkang Jiang, Wei Zhao, Chen Luo, Jing Xiong, Xiaoming Yang, Jihong Sun, Tianye Niu
    Abstract:

    PURPOSE Scatter contamination in the cone-beam CT (CBCT) leads to CT number inaccuracy, spatial nonuniformity, and loss of image contrast. In our previous work, we proposed a single scan scatter correction approach using a stationary partial beam blocker. Although the previous method works effectively on a tabletop CBCT system, it fails to achieve high image quality on a clinical CBCT system mainly due to the wobble of the LINAC gantry during scan acquisition. Due to the mechanical deformation of CBCT gantry, the wobbling effect is observed in the clinical CBCT scan, and more missing data present using the previous blocker with the uniformly distributed lead strips. METHODS An optimal blocker distribution is proposed to minimize the missing data. In the objective function of the missing data, the motion of the beam blocker in each Projection is estimated using the segmentation due to its high contrast in the blocked area. The scatter signals from the blocker are also estimated using an air scan with the inserted blocker. The final image is generated using the Forward Projection to compensate for the missing data. RESULTS On the Catphan©504 phantom, our approach reduces the average CT number error from 86 Hounsfield unit (HU) to 9 HU and improves the image contrast by a factor of 1.45 in the high-contrast rods. On a head patient, the CT number error is reduced from 97 HU to 6 HU in the soft-tissue region and the image spatial nonuniformity is decreased from 27% to 5%. CONCLUSIONS The results suggest that the proposed method is promising for clinical applications.

  • quantitative cone beam ct imaging in radiation therapy using planning ct as a prior first patient studies
    Medical Physics, 2012
    Co-Authors: Tianye Niu, A Albasheer, Lei Zhu
    Abstract:

    Purpose: Quantitative cone-beam CT (CBCT) imaging is on increasing demand for high-performance image guided radiation therapy (IGRT). However, the current CBCT has poor image qualities mainly due to scatter contamination. Its current clinical application is therefore limited to patient setup based on only bony structures. To improve CBCT imaging for quantitative use, we recently proposed a correction method using planning CT (pCT) as the prior knowledge. Promising phantom results have been obtained on a tabletop CBCT system, using a correction scheme with rigid registration and without iterations. More challenges arise in clinical implementations of our method, especially because patients have large organ deformation in different scans. In this paper, we propose an improved framework to extend our method from bench to bedside by including several new components. Methods: The basic principle of our correction algorithm is to estimate the primary signals of CBCT Projections via Forward Projection on the pCT image, and then to obtain the low-frequency errors in CBCT raw Projections by subtracting the estimated primary signals and low-pass filtering. We improve the algorithm by using deformable registration to minimize the geometry difference between the pCT and the CBCT images. Since the registration performance relies on the accuracy of the CBCT image, we design an optional iterative scheme to update the CBCT image used in the registration. Large correction errors result from the mismatched objects in the pCT and the CBCT scans. Another optional step of gas pocket and couch matching is added into the framework to reduce these effects. Results: The proposed method is evaluated on four prostate patients, of which two cases are presented in detail to investigate the method performance for a large variety of patient geometry in clinical practice. The first patient has small anatomical changes from the planning to the treatment room. Our algorithm works well even without the optional iterations and the gas pocket and couch matching. The image correction on the second patient is more challenging due to the effects of gas pockets and attenuating couch. The improved framework with all new components is used to fully evaluate the correction performance. The enhanced image quality has been evaluated using mean CT number and spatial nonuniformity (SNU) error as well as contrast improvement factor. If the pCT image is considered as the ground truth, on the four patients, the overall mean CT number error is reduced from over 300 HU to below 16 HU in the selected regions of interest (ROIs), and the SNU error is suppressed from over 18% to below 2%. The average soft-tissue contrast is improved by an average factor of 2.6. Conclusions: We further improve our pCT-based CBCT correction algorithm for clinical use. Superior correction performance has been demonstrated on four patient studies. By providing quantitative CBCT images, our approach significantly increases the accuracy of advanced CBCT-based clinical applications for IGRT.

  • shading correction for on board cone beam ct in radiation therapy using planning mdct images
    Medical Physics, 2010
    Co-Authors: Tianye Niu, Josh Starlack, Hewei Gao, Mingshan Sun, Qiyong Fan, Lei Zhu
    Abstract:

    Purpose: Applications of cone-beam CT(CBCT) to image-guided radiation therapy (IGRT) are hampered by shading artifacts in the reconstructed images. These artifacts are mainly due to scatter contamination in the Projections but also can result from uncorrected beam hardening effects as well as nonlinearities in responses of the amorphous silicon flat panel detectors. While currently, CBCT is mainly used to provide patient geometry information for treatment setup, more demanding applications requiring high-quality CBCTimages are under investigation. To tackle these challenges, many CBCT correction algorithms have been proposed; yet, a standard approach still remains unclear. In this work, we propose a shading correction method for CBCT that addresses artifacts from low-frequency Projection errors. The method is consistent with the current workflow of radiation therapy. Methods: With much smaller inherent scatter signals and more accurate detectors, diagnostic multidetector CT (MDCT) provides high quality CTimages that are routinely used for radiation treatment planning. Using the MDCT image as “free” prior information, we first estimate the primary Projections in the CBCT scan via Forward Projection of the spatially registered MDCT data. Since most of the CBCT shading artifacts stem from low-frequency errors in the Projections such as scatter, these errors can be accurately estimated by low-pass filtering the difference between the estimated and raw CBCT Projections. The error estimates are then subtracted from the raw CBCT Projections. Our method is distinct from other published correction methods that use the MDCT image as a prior because it is Projection-based and uses limited patient anatomical information from the MDCT image. The merit of CBCT-based treatment monitoring is therefore retained. Results: The proposed method is evaluated using two phantom studies on tabletop systems. On the Catphan©600 phantom, our approach reduces the reconstruction error from 348 Hounsfield unit (HU) without correction to 4 HU around the object center after correction, and from 375 HU to 17 HU in the high-contrast regions. In the selected regions of interest (ROIs), the average image contrast is increased by a factor of 3.3. When noise suppression is implemented, the proposed correction substantially improves the contrast-to-noise ratio(CNR) and therefore the visibility of low-contrast objects, as seen in a more challenging pelvis phantom study. Besides a significant improvement in image uniformity, a low-contrast object of ∼ 25 HU , which is otherwise buried in the shading artifacts, can be clearly identified after the proposed correction due to a CNR increase of 3.1. Compared to a kernel-based scatter correction method coupled with an analytical beam hardening correction, our approach also shows an overall improved performance with some residual artifacts. Conclusions: By providing effective shading correction, our approach has the potential to improve the accuracy of more advanced CBCT-based clinical applications for IGRT, such as tumor delineation and dose calculation.

  • tu d 204b 01 scatter correction for on board cone beam ct in radiation therapy using planning mdct images
    Medical Physics, 2010
    Co-Authors: Tianye Niu, Josh Starlack, Hewei Gao, Mingshan Sun, Qiyong Fan, Lei Zhu
    Abstract:

    Purpose: The applications of cone‐beam CT(CBCT)imaging in radiation therapy are greatly hampered by the poor image quality mainly due to scatter artifacts. The current use of CBCT is only limited to treatment setup, and an optimal scatter correction solution still remains unclear. Here, we propose a new scatter correction method for CBCT based on the current workflow of radiation therapy.Methods and Materials: With much smaller inherent scatter signals, diagnostic multi‐detector CT (MDCT) provides more accurate CTimages and is routinely used for radiation treatment planning. Using the MDCT image as the “free” prior information, we first estimate the primary Projections in the CBCT scan via Forward Projection on MDCT data. Since the deviation between patient geometries in the CBCT and the registered MDCT data only leads to high‐frequency primary Projection differences and scatter has dominant low‐frequency components, CBCT scatter signals are accurately estimated by low‐pass filtering and effectively corrected for after subtraction. The proposed method is evaluated using two phantom studies on tabletop systems. Results: On the Catphan©600 phantom, the reconstruction error is reduced from 348 HU to 4 HU around the phantom center. In the selected regions of interest, the average imagecontrast is increased by a factor of 3.3. This contrast increase improves low‐contrast detectability, as seen in the pelvis phantom study. Besides a significant improvement of image quality, a 25 HU object, which is otherwise buried in the scatter artifacts, can be clearly identified after the proposed scatter correction. Compared to the kernel‐based method, our approach shows an improved performance. Conclusions: Effective scatter correction is achieved on CBCT using our MDCT‐based approach. The increased accuracy of CBCTimaging substantially facilitates CBCT‐based clinical applications, such as tumor delineation and dose calculation. As such, the proposed method will be very attractive in current radiation therapy.

David G Regan - One of the best experts on this subject based on the ideXlab platform.

  • adaptive markov chain monte carlo Forward Projection for statistical analysis in epidemic modelling of human papillomavirus
    Statistics in Medicine, 2013
    Co-Authors: Igor A Korostil, Gareth W Peters, Julien Cornebise, David G Regan
    Abstract:

    We develop a Bayesian statistical model and estimation methodology based on Forward Projection Adaptive Markov chain Monte Carlo in order to perform the calibration of a highdimensional non-linear system of Ordinary Differential Equations representing an epidemic model for Human Papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gender and sexual activity-group. Developing this model and a means to calibrate it efficiently is relevant sinc e HPV is a very multi-typed and common sexually transmitted infection with more than 100 types currently known. The two types studied in this paper, types 6 and 11, are causing about 90% of anogenital warts. We extend the development of a sexual mixing matrix for the population, based on a formulation first suggested by Garnett and Anderson. In particula r we consider a stochastic mixing matrix framework which allows us to jointly estimate unknown attributes and parameters of the mixing matrix along with the parameters involved in the calibration of the HPV epidemic model. This matrix describes the sexual interactions between members of the population under study and relies on several quantities which are a priori unknown. The Bayesian model developed allows one to estimate jointly the HPV-6 and HPV-11 epidemic model parameters such as the probability of transmission, HPV incubation period, duration of infection, duration of genital warts treatment, duration of immunity, the probability of seroconversion, per gender, age-group and sexual activity-group, as well as unknown sexual mixing matrix parameters related to assortativity. Finally, we explore the ability of an extension to the class o f adaptive Markov chain Monte Carlo algorithms to incorporate a Forward Projection simulation strategy for the ordinary differential equation state trajectories. Efficient explorat ion of the Bayesian posterior distribution developed for the ODE parameters provides a challenge for any Markov chain sampling methodology, hence the interest in adaptive Markov chain methods. We conclude with simulation studies on synthetic and actual data from studies undertaken recently in Australia.

  • adaptive markov chain monte carlo Forward simulation for statistical analysis in epidemic modelling of human papillomavirus
    arXiv: Applications, 2011
    Co-Authors: Igor A Korostil, Gareth W Peters, Julien Cornebise, David G Regan
    Abstract:

    We develop a Bayesian statistical model and estimation methodology based on Forward Projection Adaptive Markov chain Monte Carlo in order to perform the calibration of a high-dimensional non-linear system of Ordinary Differential Equations representing an epidemic model for Human Papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gender and sexual activity-group. Developing this model and a means to calibrate it efficiently is relevant since HPV is a very multi-typed and common sexually transmitted infection with more than 100 types currently known. The two types studied in this paper, types 6 and 11, are causing about 90% of anogenital warts. We extend the development of a sexual mixing matrix for the population, based on a formulation first suggested by Garnett and Anderson. In particular we consider a stochastic mixing matrix framework which allows us to jointly estimate unknown attributes and parameters of the mixing matrix along with the parameters involved in the calibration of the HPV epidemic model. This matrix describes the sexual interactions between members of the population under study and relies on several quantities which are a-priori unknown. The Bayesian model developed allows one to estimate jointly the HPV-6 and HPV-11 epidemic model parameters such as the probability of transmission, HPV incubation period, duration of infection, duration of genital warts treatment, duration of immunity, the probability of seroconversion, per gender, age-group and sexual activity-group, as well as unknown sexual mixing matrix parameters related to assortativity. We conclude with simulation studies on synthetic and actual data from studies undertaken recently in Australia.

Josh Starlack - One of the best experts on this subject based on the ideXlab platform.

  • digital tomosynthesis system geometry analysis using convolution based blur and add baa model
    IEEE Transactions on Medical Imaging, 2016
    Co-Authors: Sungwon Yoon, Edward G Solomon, Josh Starlack, Norbert J Pelc, Rebecca Fahrig
    Abstract:

    Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing Forward Projection and backProjection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration.

  • shading correction for on board cone beam ct in radiation therapy using planning mdct images
    Medical Physics, 2010
    Co-Authors: Tianye Niu, Josh Starlack, Hewei Gao, Mingshan Sun, Qiyong Fan, Lei Zhu
    Abstract:

    Purpose: Applications of cone-beam CT(CBCT) to image-guided radiation therapy (IGRT) are hampered by shading artifacts in the reconstructed images. These artifacts are mainly due to scatter contamination in the Projections but also can result from uncorrected beam hardening effects as well as nonlinearities in responses of the amorphous silicon flat panel detectors. While currently, CBCT is mainly used to provide patient geometry information for treatment setup, more demanding applications requiring high-quality CBCTimages are under investigation. To tackle these challenges, many CBCT correction algorithms have been proposed; yet, a standard approach still remains unclear. In this work, we propose a shading correction method for CBCT that addresses artifacts from low-frequency Projection errors. The method is consistent with the current workflow of radiation therapy. Methods: With much smaller inherent scatter signals and more accurate detectors, diagnostic multidetector CT (MDCT) provides high quality CTimages that are routinely used for radiation treatment planning. Using the MDCT image as “free” prior information, we first estimate the primary Projections in the CBCT scan via Forward Projection of the spatially registered MDCT data. Since most of the CBCT shading artifacts stem from low-frequency errors in the Projections such as scatter, these errors can be accurately estimated by low-pass filtering the difference between the estimated and raw CBCT Projections. The error estimates are then subtracted from the raw CBCT Projections. Our method is distinct from other published correction methods that use the MDCT image as a prior because it is Projection-based and uses limited patient anatomical information from the MDCT image. The merit of CBCT-based treatment monitoring is therefore retained. Results: The proposed method is evaluated using two phantom studies on tabletop systems. On the Catphan©600 phantom, our approach reduces the reconstruction error from 348 Hounsfield unit (HU) without correction to 4 HU around the object center after correction, and from 375 HU to 17 HU in the high-contrast regions. In the selected regions of interest (ROIs), the average image contrast is increased by a factor of 3.3. When noise suppression is implemented, the proposed correction substantially improves the contrast-to-noise ratio(CNR) and therefore the visibility of low-contrast objects, as seen in a more challenging pelvis phantom study. Besides a significant improvement in image uniformity, a low-contrast object of ∼ 25 HU , which is otherwise buried in the shading artifacts, can be clearly identified after the proposed correction due to a CNR increase of 3.1. Compared to a kernel-based scatter correction method coupled with an analytical beam hardening correction, our approach also shows an overall improved performance with some residual artifacts. Conclusions: By providing effective shading correction, our approach has the potential to improve the accuracy of more advanced CBCT-based clinical applications for IGRT, such as tumor delineation and dose calculation.

  • tu d 204b 01 scatter correction for on board cone beam ct in radiation therapy using planning mdct images
    Medical Physics, 2010
    Co-Authors: Tianye Niu, Josh Starlack, Hewei Gao, Mingshan Sun, Qiyong Fan, Lei Zhu
    Abstract:

    Purpose: The applications of cone‐beam CT(CBCT)imaging in radiation therapy are greatly hampered by the poor image quality mainly due to scatter artifacts. The current use of CBCT is only limited to treatment setup, and an optimal scatter correction solution still remains unclear. Here, we propose a new scatter correction method for CBCT based on the current workflow of radiation therapy.Methods and Materials: With much smaller inherent scatter signals, diagnostic multi‐detector CT (MDCT) provides more accurate CTimages and is routinely used for radiation treatment planning. Using the MDCT image as the “free” prior information, we first estimate the primary Projections in the CBCT scan via Forward Projection on MDCT data. Since the deviation between patient geometries in the CBCT and the registered MDCT data only leads to high‐frequency primary Projection differences and scatter has dominant low‐frequency components, CBCT scatter signals are accurately estimated by low‐pass filtering and effectively corrected for after subtraction. The proposed method is evaluated using two phantom studies on tabletop systems. Results: On the Catphan©600 phantom, the reconstruction error is reduced from 348 HU to 4 HU around the phantom center. In the selected regions of interest, the average imagecontrast is increased by a factor of 3.3. This contrast increase improves low‐contrast detectability, as seen in the pelvis phantom study. Besides a significant improvement of image quality, a 25 HU object, which is otherwise buried in the scatter artifacts, can be clearly identified after the proposed scatter correction. Compared to the kernel‐based method, our approach shows an improved performance. Conclusions: Effective scatter correction is achieved on CBCT using our MDCT‐based approach. The increased accuracy of CBCTimaging substantially facilitates CBCT‐based clinical applications, such as tumor delineation and dose calculation. As such, the proposed method will be very attractive in current radiation therapy.

Willi A Kalender - One of the best experts on this subject based on the ideXlab platform.

  • volume of interest voi imaging in c arm flat detector ct for high image quality at reduced dose
    Medical Physics, 2010
    Co-Authors: Daniel Kolditz, Yiannis Kyriakou, Willi A Kalender
    Abstract:

    Purpose: A novel method for flat-detector computed tomography was developed to enable volume-of-interest (VOI) imaging at high resolution, low noise, and reduced dose. For this, a full low-dose overview (OV) scan and a local high-dose scan of a VOI are combined. Methods: The first scan yields an overview of the whole object and enables the selection of an arbitrary VOI. The second scan of that VOI assures high image quality within the interesting volume. The combination of the two consecutive scans is based on a Forward Projection of the reconstructed OV volume that was registered to the VOI. The artificial Projection data of the OV scan are combined with the measured VOI data in the raw data domain. Different Projection values are matched by an appropriate transformation and weighting. The reconstruction is performed with a standard Feldkamp-type algorithm. In simulations, the combination of OV scan and VOI scan was investigated on a mathematically described phantom. In measurements, spatial resolution and noise were evaluated with image quality phantoms. Modulation transfer functions and noise values were calculated. Measurements of an anthropomorphic head phantom were used to validate the proposed method for realistic applications, e.g., imaging stents. In Monte Carlo simulations, 3D dose distributions were calculated and dose values were assessed quantitatively. Results: By the proposed combination method, an image is generated which covers the whole object and provides the VOI at high image quality. In the OV image, a resolution of 0.7 lp/mm (line pairs per millimeter) and noise of 63.5 HU were determined. Inside the VOI, resolution was increased to 2.4 lp/mm and noise was decreased to 18.7 HU. For the performed measurements, the cumulative dose was significantly reduced in comparison to conventional scans by up to 93%. The dose of a high-quality scan, for example, was reduced from 97 to less than 7 mGy, while keeping image quality constant within the VOI. Conclusions: The proposed VOI application with two scans is an effective way to ensure high image quality within the VOI while simultaneously reducing the cumulative patient dose.

  • a novel Forward Projection based metal artifact reduction method for flat detector computed tomography
    Physics in Medicine and Biology, 2009
    Co-Authors: Daniel Prell, Yiannis Kyriakou, Marcel Beister, Willi A Kalender
    Abstract:

    Metallic implants generate streak-like artifacts in flat-detector computed tomography (FD-CT) reconstructed volumetric images. This study presents a novel method for reducing these disturbing artifacts by inserting discarded information into the original rawdata using a three-step correction procedure and working directly with each detector element. Computation times are minimized by completely implementing the correction process on graphics processing units (GPUs). First, the original volume is corrected using a three-dimensional interpolation scheme in the rawdata domain, followed by a second reconstruction. This metal artifact-reduced volume is then segmented into three materials, i.e. air, soft-tissue and bone, using a threshold-based algorithm. Subsequently, a Forward Projection of the obtained tissue-class model substitutes the missing or corrupted attenuation values directly for each flat detector element that contains attenuation values corresponding to metal parts, followed by a final reconstruction. Experiments using tissue-equivalent phantoms showed a significant reduction of metal artifacts (deviations of CT values after correction compared to measurements without metallic inserts reduced typically to below 20 HU, differences in image noise to below 5 HU) caused by the implants and no significant resolution losses even in areas close to the inserts. To cover a variety of different cases, cadaver measurements and clinical images in the knee, head and spine region were used to investigate the effectiveness and applicability of our method. A comparison to a three-dimensional interpolation correction showed that the new approach outperformed interpolation schemes. Correction times are minimized, and initial and corrected images are made available at almost the same time (12.7 s for the initial reconstruction, 46.2 s for the final corrected image compared to 114.1 s and 355.1 s on central processing units (CPUs)).