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Yasuhiko Nakamura - One of the best experts on this subject based on the ideXlab platform.
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investigation of interfractional shape variations based on statistical Point Distribution model for prostate cancer radiation therapy
Medical Physics, 2017Co-Authors: Yusuke Shibayama, Norimasa Matsushita, Hidetaka Arimura, Taka Aki Hirose, Takahiro Nakamoto, Tomonari Sasaki, Saiji Ohga, Yoshiyuki Umezu, Yasuhiko NakamuraAbstract:Purpose The setup errors and organ motion errors pertaining to clinical target volume (CTV) have been considered as two major causes of uncertainties in the determination of the CTV-to-planning target volume (PTV) margins for prostate cancer radiation treatment planning. We based our study on the assumption that interfractional target shape variations are not negligible as another source of uncertainty for the determination of precise CTV-to-PTV margins. Thus, we investigated the interfractional shape variations of CTVs based on a Point Distribution model (PDM) for prostate cancer radiation therapy. Materials and Methods To quantify the shape variations of CTVs, the PDM was applied for the contours of 4 types of CTV regions (low-risk, intermediate- risk, high-risk CTVs, and prostate plus entire seminal vesicles), which were delineated by considering prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The standard deviations (SDs) of the interfractional random errors for shape variations were obtained from covariance matrices based on the PDMs, which were generated from vertices of triangulated CTV surfaces. The correspondences between CTV surface vertices were determined based on a thin plate spline robust Point matching algorithm. The systematic error for shape variations was defined as the average deviation between surfaces of an average CTV and planning CTVs, and the random error as the average deviation of CTV surface vertices for fractions from an average CTV surface. Results The means of the SDs of the systematic errors for the 4 types of CTVs ranged from 1.0 to 2.0 mm along the anterior direction, 1.2 to 2.6 mm along the posterior direction, 1.0 to 2.5 mm along the superior direction, 0.9 to 1.9 mm along the inferior direction, 0.9 to 2.6 mm along the right direction, and 1.0 to 3.0 mm along the left direction. Concerning the random errors, the means of the SDs ranged from 0.9 to 1.2 mm along the anterior direction, 1.0 to 1.4 mm along the posterior direction, 0.9 to 1.3 mm along the superior direction, 0.8 to 1.0 mm along the inferior direction, 0.8 to 0.9 mm along the right direction, and 0.8 to 1.0 mm along the left direction. Conclusions Since the shape variations were not negligible for intermediate and high risk CTVs, they should be taken into account for the determination of the CTV-to-PTV margins in radiation treatment planning of prostate cancer. This article is protected by copyright. All rights reserved.
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we ab 207b 03 a computational methodology for determination of ctv to ptv margins with inter fractional shape variations based on a statistical Point Distribution model for prostate cancer radiation therapy
Medical Physics, 2016Co-Authors: Yusuke Shibayama, Hidetaka Arimura, Taka Aki Hirose, Tomonari Sasaki, Yoshiyuki Umezu, Yasuhiko Nakamura, Katsumasa Nakamura, Shouichi Ohga, Hiroyuki HondaAbstract:Purpose: Our assumption was that interfractional shape variations of target volumes could not be negligible for determination of clinical target volume (CTV)-to-planning target volume (PTV) margins. The aim of this study was to investigate this assumption as a simulation study by developing a computational framework of CTV-to-PTV margins with taking the interfractional shape variations into account based on Point Distribution model (PDM) Methods: The systematic and random errors for interfractional shape variations and translations of target volumes were evaluated for four types of CTV regions (only a prostate, a prostate plus proximal 1-cm seminal vesicles, a prostate plus proximal 2-cm seminal vesicles, and a prostate plus whole seminal vesicles). The CTV regions were delineated depending on prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The random and systematic errors for shape variations of CTV regions were derived from PDMs of CTV surfaces for all fractions of each patient. Systematic errors of shape variations of CTV regions were derived by comparing PDMs between planning CTV surfaces and average CTV surfaces. Finally, anisotropic CTV-to-PTV margins with shape variations in 6 directions (anterior, posterior, superior, inferior, right, and left) were computed by using a van Herk margin formula. Results: Differences between CTV-to-PTV margins with and without shape variations ranged from 0.7 to 1.7 mm in anterior direction, 1.0 to 2.8 mm in posterior direction, 0.8 to 2.8 mm in superior direction, 0.6 to 1.6 mm in inferior direction, 1.4 to 4.4 mm in right direction, and 1.3 to 5.2 mm in left direction. Conclusion: More than 1.0 mm additional margins were needed at least in 3 directions to guarantee CTV coverage due to shape variations. Therefore, shape variations should be taken into account for the determination of CTV-to-PTV margins.
Yusuke Shibayama - One of the best experts on this subject based on the ideXlab platform.
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investigation of interfractional shape variations based on statistical Point Distribution model for prostate cancer radiation therapy
Medical Physics, 2017Co-Authors: Yusuke Shibayama, Norimasa Matsushita, Hidetaka Arimura, Taka Aki Hirose, Takahiro Nakamoto, Tomonari Sasaki, Saiji Ohga, Yoshiyuki Umezu, Yasuhiko NakamuraAbstract:Purpose The setup errors and organ motion errors pertaining to clinical target volume (CTV) have been considered as two major causes of uncertainties in the determination of the CTV-to-planning target volume (PTV) margins for prostate cancer radiation treatment planning. We based our study on the assumption that interfractional target shape variations are not negligible as another source of uncertainty for the determination of precise CTV-to-PTV margins. Thus, we investigated the interfractional shape variations of CTVs based on a Point Distribution model (PDM) for prostate cancer radiation therapy. Materials and Methods To quantify the shape variations of CTVs, the PDM was applied for the contours of 4 types of CTV regions (low-risk, intermediate- risk, high-risk CTVs, and prostate plus entire seminal vesicles), which were delineated by considering prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The standard deviations (SDs) of the interfractional random errors for shape variations were obtained from covariance matrices based on the PDMs, which were generated from vertices of triangulated CTV surfaces. The correspondences between CTV surface vertices were determined based on a thin plate spline robust Point matching algorithm. The systematic error for shape variations was defined as the average deviation between surfaces of an average CTV and planning CTVs, and the random error as the average deviation of CTV surface vertices for fractions from an average CTV surface. Results The means of the SDs of the systematic errors for the 4 types of CTVs ranged from 1.0 to 2.0 mm along the anterior direction, 1.2 to 2.6 mm along the posterior direction, 1.0 to 2.5 mm along the superior direction, 0.9 to 1.9 mm along the inferior direction, 0.9 to 2.6 mm along the right direction, and 1.0 to 3.0 mm along the left direction. Concerning the random errors, the means of the SDs ranged from 0.9 to 1.2 mm along the anterior direction, 1.0 to 1.4 mm along the posterior direction, 0.9 to 1.3 mm along the superior direction, 0.8 to 1.0 mm along the inferior direction, 0.8 to 0.9 mm along the right direction, and 0.8 to 1.0 mm along the left direction. Conclusions Since the shape variations were not negligible for intermediate and high risk CTVs, they should be taken into account for the determination of the CTV-to-PTV margins in radiation treatment planning of prostate cancer. This article is protected by copyright. All rights reserved.
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we ab 207b 03 a computational methodology for determination of ctv to ptv margins with inter fractional shape variations based on a statistical Point Distribution model for prostate cancer radiation therapy
Medical Physics, 2016Co-Authors: Yusuke Shibayama, Hidetaka Arimura, Taka Aki Hirose, Tomonari Sasaki, Yoshiyuki Umezu, Yasuhiko Nakamura, Katsumasa Nakamura, Shouichi Ohga, Hiroyuki HondaAbstract:Purpose: Our assumption was that interfractional shape variations of target volumes could not be negligible for determination of clinical target volume (CTV)-to-planning target volume (PTV) margins. The aim of this study was to investigate this assumption as a simulation study by developing a computational framework of CTV-to-PTV margins with taking the interfractional shape variations into account based on Point Distribution model (PDM) Methods: The systematic and random errors for interfractional shape variations and translations of target volumes were evaluated for four types of CTV regions (only a prostate, a prostate plus proximal 1-cm seminal vesicles, a prostate plus proximal 2-cm seminal vesicles, and a prostate plus whole seminal vesicles). The CTV regions were delineated depending on prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The random and systematic errors for shape variations of CTV regions were derived from PDMs of CTV surfaces for all fractions of each patient. Systematic errors of shape variations of CTV regions were derived by comparing PDMs between planning CTV surfaces and average CTV surfaces. Finally, anisotropic CTV-to-PTV margins with shape variations in 6 directions (anterior, posterior, superior, inferior, right, and left) were computed by using a van Herk margin formula. Results: Differences between CTV-to-PTV margins with and without shape variations ranged from 0.7 to 1.7 mm in anterior direction, 1.0 to 2.8 mm in posterior direction, 0.8 to 2.8 mm in superior direction, 0.6 to 1.6 mm in inferior direction, 1.4 to 4.4 mm in right direction, and 1.3 to 5.2 mm in left direction. Conclusion: More than 1.0 mm additional margins were needed at least in 3 directions to guarantee CTV coverage due to shape variations. Therefore, shape variations should be taken into account for the determination of CTV-to-PTV margins.
C Taylor - One of the best experts on this subject based on the ideXlab platform.
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automatic landmark generation for Point Distribution models
British Machine Vision Conference, 1994Co-Authors: A Hill, C TaylorAbstract:Point Distribution Models (PDMs) are statistically derived flexible templates which are trained on sets of examples of the object(s) to be modelled. They require that each example is represented by a set of Points (landmaiks) and that each landmark represents the same location on each of the examples. Generating the landmarks from 2D boundaries or 3D surfaces has previously been a manual process. Here, we describe a method for automatically generating PDMs from a training set of pixel- lated boundaries in 2D. The algorithm is a two-stage process in which a pair-wise corresponder is first used to establish an approximate set of landmarks on each of the example boundaries; in the second phase the landmarks are refined using an iterative non-linear optimisation scheme to generate a more compact PDM. We present results for two objects - the right hand and a chamber of the heart. The mo- dels generated using the automatically placed landmarks are shown to be better than those derived from landmarks located manually.
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combining Point Distribution models with shape models based on finite element analysis
British Machine Vision Conference, 1994Co-Authors: Timothy F Cootes, C TaylorAbstract:This paper describes a method of combining two approaches to modelling flexible objects. Modal Analysis using Finite Element Methods (FEMs) generates a set of vibrational modes for a single shape. Point Distribution Models (PDMs) generate a statistical model of shape and shape variation from a set of example shapes. A new approach is described which generates vibrational modes when few example shapes are available and changes smoothly to using more statistical modes of variation when a large data set is presented. Results are given for both synthetic and real examples. Experiments using the models for image search show that the combined version performs better than either the PDM or FEM models alone.
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training models of shape from sets of examples
British Machine Vision Conference, 1992Co-Authors: Timothy F Cootes, C Taylor, D H Cooper, Jim GrahamAbstract:A method for building flexible shape models is presented in which a shape is represented by a set of labelled Points. The technique determines the statistics of the Points over a collection of example shapes. The mean positions of the Points give an average shape and a number of modes of variation are determined describing the main ways in which the example shapes tend to deform from the average. In this way allowed variation in shape can be included in the model. The method produces a compact flexible ‘Point Distribution Model’ with a small number of linearly independent parameters, which can be used during image search. We demonstrate the application of the Point Distribution Model in describing two classes of shapes.
Branka Vucetic - One of the best experts on this subject based on the ideXlab platform.
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non uniform linear antenna array design and optimization for millimeter wave communications
arXiv: Information Theory, 2016Co-Authors: Peng Wang, Y. Peng, Soung Chang Liew, Branka VuceticAbstract:In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeter wave (mmWave) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels. Our focus is on the maximization of the system effective multiplexing gain (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here the EMG is defined as the number of signal streams that are practically supported by the channel at a finite SNR. We first derive analytical expressions for the asymptotic channel eigenvalues with arbitrarily deployed NULAs when, asymptotically, the end-to-end distance is sufficiently large compared to the aperture sizes of the transmit/receive NULAs. Based on the derived expressions, we prove that, the asymptotically optimal NULA deployment that maximizes the achievable EMG should follow the groupwise Fekete-Point Distribution. Specifically, the antennas should be physically grouped into K separate uniform linear antenna arrays (ULAs) with the minimum feasible antenna spacing within each ULA, where K is the target EMG to be achieved; in addition, the centers of these K ULAs follow the Fekete-Point Distribution. We numerically verify the asymptotic optimality of such an NULA deployment and extend it to a groupwise projected arch type (PAT) NULA deployment, which provides a more practical option for mmWave LoS MIMO systems with realistic non-asymptotic configurations. Numerical examples are provided to demonstrate a significant capacity gain of the optimized NULAs over traditional ULAs.
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Non-Uniform Linear Antenna Array Design and Optimization for Millimeter-Wave Communications
IEEE Transactions on Wireless Communications, 2016Co-Authors: Pan Wang, Y. Peng, Siau-chuin Liew, Yong Li, Branka VuceticAbstract:In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeter-wave (mmWave) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels. Our focus is on the maximization of the system effective multiplexing gain (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here, the EMG is defined as the number of signal streams that are practically supported by the channel at a finite signal-to-noise ratio. We first derive analytical expressions for the asymptotic channel eigenvalues with arbitrarily deployed NULAs when, asymptotically, the end-to-end distance is sufficiently large compared with the aperture sizes of the transmit/receive NULAs. Based on the derived expressions, we prove that the asymptotically optimal NULA deployment that maximizes the achievable EMG should follow the groupwise Fekete-Point Distribution. Specifically, the antennas should be physically grouped into $K$ separate ULAs with the minimum feasible antenna spacing within each ULA, where $K$ is the target EMG to be achieved; in addition, the centers of these $K$ ULAs follow the Fekete-Point Distribution. We numerically verify the asymptotic optimality of such an NULA deployment and extend it to a groupwise projected arch-type NULA deployment, which provides a more practical option for mmWave LoS MIMO systems with realistic non-asymptotic configurations. Numerical examples are provided to demonstrate a significant capacity gain of the optimized NULAs over traditional ULAs.
Takahiro Nakamoto - One of the best experts on this subject based on the ideXlab platform.
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investigation of interfractional shape variations based on statistical Point Distribution model for prostate cancer radiation therapy
Medical Physics, 2017Co-Authors: Yusuke Shibayama, Norimasa Matsushita, Hidetaka Arimura, Taka Aki Hirose, Takahiro Nakamoto, Tomonari Sasaki, Saiji Ohga, Yoshiyuki Umezu, Yasuhiko NakamuraAbstract:Purpose The setup errors and organ motion errors pertaining to clinical target volume (CTV) have been considered as two major causes of uncertainties in the determination of the CTV-to-planning target volume (PTV) margins for prostate cancer radiation treatment planning. We based our study on the assumption that interfractional target shape variations are not negligible as another source of uncertainty for the determination of precise CTV-to-PTV margins. Thus, we investigated the interfractional shape variations of CTVs based on a Point Distribution model (PDM) for prostate cancer radiation therapy. Materials and Methods To quantify the shape variations of CTVs, the PDM was applied for the contours of 4 types of CTV regions (low-risk, intermediate- risk, high-risk CTVs, and prostate plus entire seminal vesicles), which were delineated by considering prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The standard deviations (SDs) of the interfractional random errors for shape variations were obtained from covariance matrices based on the PDMs, which were generated from vertices of triangulated CTV surfaces. The correspondences between CTV surface vertices were determined based on a thin plate spline robust Point matching algorithm. The systematic error for shape variations was defined as the average deviation between surfaces of an average CTV and planning CTVs, and the random error as the average deviation of CTV surface vertices for fractions from an average CTV surface. Results The means of the SDs of the systematic errors for the 4 types of CTVs ranged from 1.0 to 2.0 mm along the anterior direction, 1.2 to 2.6 mm along the posterior direction, 1.0 to 2.5 mm along the superior direction, 0.9 to 1.9 mm along the inferior direction, 0.9 to 2.6 mm along the right direction, and 1.0 to 3.0 mm along the left direction. Concerning the random errors, the means of the SDs ranged from 0.9 to 1.2 mm along the anterior direction, 1.0 to 1.4 mm along the posterior direction, 0.9 to 1.3 mm along the superior direction, 0.8 to 1.0 mm along the inferior direction, 0.8 to 0.9 mm along the right direction, and 0.8 to 1.0 mm along the left direction. Conclusions Since the shape variations were not negligible for intermediate and high risk CTVs, they should be taken into account for the determination of the CTV-to-PTV margins in radiation treatment planning of prostate cancer. This article is protected by copyright. All rights reserved.