The Experts below are selected from a list of 25293 Experts worldwide ranked by ideXlab platform
Tao Qian - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (5) - A New Supervised Learning Approach: Statistical Adaptive Fourier Decomposition (SAFD).
Communications in Computer and Information Science, 2019Co-Authors: Chunyu Tan, Liming Zhang, Tao QianAbstract:This paper proposes a new type of supervised learning approach - statistical adaptive Fourier Decomposition (SAFD). SAFD uses the orthogonal rational systems, or Takenaka-Malmquist (TM) systems, to build up a learning model for the training set, based on which predictions of unknown data can be made. The approach focuses on the classification of signals or time series. AFD is a newly developed signal analysis method, which can adaptively decompose different signals into different TM systems that introduces the Fourier type but non-linear and non-negative time-frequency representation. SAFD fully integrates the learning process with the adaptability character of AFD, in which a small number of learned atoms are adequate to capture structures and features of the signals for classification. There are three advantages in SAFD. First, the features are automatically detected and extracted in the learning process. Secondly, all parameters are selected automatically by the algorithm. Finally, the learned features are mathematically represented and the characteristics can be further studied based on the induced instantaneous frequencies. The efficiency of the proposed method is verified by electrocardiography (ECG) signal classification. The experiments show promising results over other feature based learning approaches.
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A Novel Feature Representation for Single-Channel Heartbeat Classification based on Adaptive Fourier Decomposition
arXiv: Signal Processing, 2019Co-Authors: Chunyu Tan, Liming Zhang, Tao QianAbstract:This paper proposes a novel approach for heartbeat classification from single-lead electrocardiogram (ECG) signals based on the novel adaptive Fourier Decomposition (AFD). AFD is a recently developed signal processing tool that provides useful morphological features, referred to as AFD-derived instantaneous frequency (IF) features, that are different from those provided by traditional tools. A support vector machine (SVM) classifier is trained with the AFD-derived IF features, ECG landmark features, and RR interval features. To evaluate the performance of the trained classifier, the Association for the Advancement of Medical Instrumentation (AAMI) standard is applied to the publicly available benchmark databases, including MIT-BIH arrhythmia database and MIT-BIH supraventricular arrhythmia database, to classify heartbeats from single-lead ECG. The overall performance in terms of sensitivities and positive predictive values is comparable to the state-of-the-art automatic heartbeat classification algorithms based on two-leads ECG.
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An enhancement algorithm for cyclic adaptive Fourier Decomposition
Applied and Computational Harmonic Analysis, 2019Co-Authors: Tao Qian, Jianzhong Wang, Weixiong MaiAbstract:Abstract One important problem in the theory of Hardy space is to find the best rational approximation of a given order to a function in the Hardy space H 2 on the unit disk. It is equivalent to finding the best Blaschke form with free poles. The cyclic adaptive Fourier Decomposition method is based on the grid search technique. Its approximative precision is limited by the grid spacing. This paper proposes two enhanced methods of the cyclic adaptive Fourier Decomposition. The proposed algorithms utilize the gradient descent optimization to tune the best pole-tuple on the mesh grids, reaching higher precision. Their performances are confirmed by several examples.
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Fast basis search for adaptive Fourier Decomposition
EURASIP Journal on Advances in Signal Processing, 2018Co-Authors: Ze Wang, Feng Wan, Chi Man Wong, Tao QianAbstract:The adaptive Fourier Decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each Decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.
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An Enhancement Algorithm of Cyclic Adaptive Fourier Decomposition.
arXiv: Complex Variables, 2018Co-Authors: Tao Qian, Jianzhong WangAbstract:The paper investigates the complex gradient descent method (CGD) for the best rational approximation of a given order to a function in the Hardy space on the unit disk. It is equivalent to finding the best Blaschke form with free poles. The adaptive Fourier Decomposition (AFD) and the cyclic AFD methods in literature are based on the grid search technique. The precision of these methods is limited by the grid spacing. The proposed method employs a fast search algorithm to find the initial for CGD, then finds the target poles by gradient descent optimization. Hence, it can reach higher precision with less computation cost. Its validity and effectiveness are confirmed by several examples.
Lothar R. Schad - One of the best experts on this subject based on the ideXlab platform.
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Fourier Decomposition pulmonary mri using a variable flip angle balanced steady state free precession technique
Magnetic Resonance in Medicine, 2015Co-Authors: Dominique M.r. Corteville, Åsmund Kjørstad, Thomas Henzler, Frank G. Zöllner, Lothar R. SchadAbstract:Purpose Fourier Decomposition (FD) is a noninvasive method for assessing ventilation and perfusion-related information in the lungs. However, the technique has a low signal-to-noise ratio (SNR) in the lung parenchyma. We present an approach to increase the SNR in both morphological and functional images. Methods The data used to create functional FD images are usually acquired using a standard balanced steady-state free precession (bSSFP) sequence. In the standard sequence, the possible range of the flip angle is restricted due to specific absorption rate (SAR) limitations. Thus, using a variable flip angle approach as an optimization is possible. This was validated using measurements from a phantom and six healthy volunteers. Results The SNR in both the morphological and functional FD images was increased by 32%, while the SAR restrictions were kept unchanged. Furthermore, due to the higher SNR, the effective resolution of the functional images was increased visibly. The variable flip angle approach did not introduce any new transient artifacts, and blurring artifacts were minimized. Conclusion Both a gain in SNR and an effective resolution gain in functional lung images can be obtained using the FD method in conjunction with a variable flip angle optimized bSSFP sequence. Magn Reson Med 73:1999–2004, 2015. © 2014 Wiley Periodicals, Inc.
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Non-invasive quantitative pulmonary V/Q imaging using Fourier Decomposition MRI at 1.5T.
Zeitschrift fur medizinische Physik, 2015Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Lothar R. SchadAbstract:Abstract Objectives Techniques for quantitative pulmonary perfusion and ventilation using the Fourier Decomposition method were recently demonstrated. We combine these two techniques and show that ventilation-perfusion (V/Q) imaging is possible using only a single MR acquisition of less than thirty seconds. Methods The Fourier Decomposition method is used in combination with two quantification techniques, which extract baselines from within the images themselves and thus allows quantification. For the perfusion, a region assumed to consist of 100% blood is utilized, while for the ventilation the zero-frequency component is used. V/Q-imaging is then done by dividing the quantified ventilation map with the quantified perfusion map. The techniques were used on ten healthy volunteers and fifteen patients diagnosed with lung cancer. Results A mean V/Q-ratio of 1.15±0.22 was found for the healthy volunteers and a mean V/Q-ratio of 1.93±0.83 for the non-afflicted lung in the patients. Mean V/Q-ratio in the afflicted (tumor-bearing) lung was found to be 1.61±1.06. Functional defects were clearly visible in many of the patient images, but 5 of 15 patient images had to be excluded due to artifacts or low SNR, indicating a lack of robustness. Conclusion Non-invasive, quantitative V/Q-imaging is possible using Fourier Decomposition MRI. The method requires only a single acquisition of less than 30 seconds, but robustness in patients remains an issue.
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Quantitative lung ventilation using Fourier Decomposition MRI; comparison and initial study
Magnetic Resonance Materials in Physics Biology and Medicine, 2014Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Erlend Hodneland, Lothar R. SchadAbstract:Objective The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation and perfusion-related information in the lungs, but the lack of quantifiable values is a drawback. We demonstrate a novel technique for quantification of the FD ventilation maps, compare it to two published methods, and show results from both healthy volunteers and patients diagnosed with lung cancer. Materials and methods We quantified the standard FD ventilation images by utilizing additional information, i.e., the zero-frequency component image, which is also obtained from the Fourier analysis. This image acts as a baseline for the changes recorded in the FD ventilation image and can therefore be used to calculate the ventilation. Using this technique, we compared the ventilation values from ten healthy volunteers and ten patients to two previously published methods for quantitative ventilation assessment. Results All methods showed good overall agreement (mean difference between the methods was 14–38 ml/min). The mean minute ventilation for the FD method was calculated to be 693 ml/min for a 2D slice, which is in the expected range. Conclusion The zero-frequency component image can be used as a baseline to quantify the FD ventilation maps. Our initial study showed good agreement with published methods in healthy volunteers, but less so in patients with lung cancer.
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Quantitative lung ventilation using Fourier Decomposition MRI; comparison and initial study
Magma (New York N.Y.), 2014Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Erlend Hodneland, Lothar R. SchadAbstract:Objective The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation and perfusion-related information in the lungs, but the lack of quantifiable values is a drawback. We demonstrate a novel technique for quantification of the FD ventilation maps, compare it to two published methods, and show results from both healthy volunteers and patients diagnosed with lung cancer.
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Quantitative lung perfusion evaluation using Fourier Decomposition perfusion MRI.
Magnetic resonance in medicine, 2013Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, André Fischer, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Lothar R. SchadAbstract:Purpose To quantitatively evaluate lung perfusion using Fourier Decomposition perfusion MRI. The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation- and perfusion-related information in the lungs, where the perfusion maps in particular have shown promise for clinical use. However, the perfusion maps are nonquantitative and dimensionless, making follow-ups and direct comparisons between patients difficult. We present an approach to obtain physically meaningful and quantifiable perfusion maps using the FD method. Methods The standard FD perfusion images are quantified by comparing the partially blood-filled pixels in the lung parenchyma with the fully blood-filled pixels in the aorta. The percentage of blood in a pixel is then combined with the temporal information, yielding quantitative blood flow values. The values of 10 healthy volunteers are compared with SEEPAGE measurements which have shown high consistency with dynamic contrast enhanced-MRI. Results All pulmonary blood flow (PBF) values are within the expected range. The two methods are in good agreement (mean difference = 0.2 mL/min/100 mL, mean absolute difference = 11 mL/min/100 mL, mean PBF-FD = 150 mL/min/100 mL, mean PBF-SEEPAGE = 151 mL/min/100 mL). The Bland-Altman plot shows a good spread of values, indicating no systematic bias between the methods. Conclusion Quantitative lung perfusion can be obtained using the Fourier Decomposition method combined with a small amount of postprocessing. Magn Reson Med 72:558–562, 2014. © 2013 Wiley Periodicals, Inc.
Liming Zhang - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (5) - A New Supervised Learning Approach: Statistical Adaptive Fourier Decomposition (SAFD).
Communications in Computer and Information Science, 2019Co-Authors: Chunyu Tan, Liming Zhang, Tao QianAbstract:This paper proposes a new type of supervised learning approach - statistical adaptive Fourier Decomposition (SAFD). SAFD uses the orthogonal rational systems, or Takenaka-Malmquist (TM) systems, to build up a learning model for the training set, based on which predictions of unknown data can be made. The approach focuses on the classification of signals or time series. AFD is a newly developed signal analysis method, which can adaptively decompose different signals into different TM systems that introduces the Fourier type but non-linear and non-negative time-frequency representation. SAFD fully integrates the learning process with the adaptability character of AFD, in which a small number of learned atoms are adequate to capture structures and features of the signals for classification. There are three advantages in SAFD. First, the features are automatically detected and extracted in the learning process. Secondly, all parameters are selected automatically by the algorithm. Finally, the learned features are mathematically represented and the characteristics can be further studied based on the induced instantaneous frequencies. The efficiency of the proposed method is verified by electrocardiography (ECG) signal classification. The experiments show promising results over other feature based learning approaches.
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A Novel Feature Representation for Single-Channel Heartbeat Classification based on Adaptive Fourier Decomposition
arXiv: Signal Processing, 2019Co-Authors: Chunyu Tan, Liming Zhang, Tao QianAbstract:This paper proposes a novel approach for heartbeat classification from single-lead electrocardiogram (ECG) signals based on the novel adaptive Fourier Decomposition (AFD). AFD is a recently developed signal processing tool that provides useful morphological features, referred to as AFD-derived instantaneous frequency (IF) features, that are different from those provided by traditional tools. A support vector machine (SVM) classifier is trained with the AFD-derived IF features, ECG landmark features, and RR interval features. To evaluate the performance of the trained classifier, the Association for the Advancement of Medical Instrumentation (AAMI) standard is applied to the publicly available benchmark databases, including MIT-BIH arrhythmia database and MIT-BIH supraventricular arrhythmia database, to classify heartbeats from single-lead ECG. The overall performance in terms of sensitivities and positive predictive values is comparable to the state-of-the-art automatic heartbeat classification algorithms based on two-leads ECG.
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a novel blaschke unwinding adaptive Fourier Decomposition based signal compression algorithm with application on ecg signals
IEEE Journal of Biomedical and Health Informatics, 2019Co-Authors: Liming Zhang, Hautieng WuAbstract:This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier Decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal Decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each Decomposition step, and achieves a faster convergence rate with higher fidelity. The proposed compression algorithm is applied to the electrocardiogram signal. To assess the performance of the proposed compression algorithm, in addition to the generic assessment criteria, we consider the less discussed criteria related to the clinical needs—for the heart rate variability analysis purpose, how accurate the R-peak information is preserved is evaluated. The experiments are conducted on the MIT-BIH arrhythmia benchmark database. The results show that the proposed algorithm performs better than other state-of-the-art approaches. Meanwhile, it also well preserves the R-peak information.
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A novel ECG data compression method based on adaptive Fourier Decomposition
2017 International Conference on Robotics and Machine Vision, 2017Co-Authors: Chunyu Tan, Liming ZhangAbstract:This paper presents a novel electrocardiogram (ECG) compression method based on adaptive Fourier Decomposition (AFD). AFD is a newly developed signal Decomposition approach, which can decompose a signal with fast convergence, and hence reconstruct ECG signals with high fidelity. Unlike most of the high performance algorithms, our method does not make use of any preprocessing operation before compression. Huffman coding is employed for further compression. Validated with 48 ECG recordings of MIT-BIH arrhythmia database, the proposed method achieves the compression ratio (CR) of 35.53 and the percentage root mean square difference (PRD) of 1.47% on average with N = 8 Decomposition times and a robust PRD-CR relationship. The results demonstrate that the proposed method has a good performance compared with the state-of-the-art ECG compressors.
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The Decomposition and compression of HRTF based on adaptive Fourier Decomposition
4th International Conference on Smart and Sustainable City (ICSSC 2017), 2017Co-Authors: Yong Fang, Qinghua Huang, Liming ZhangAbstract:Head-Related Transfer Function (HRTFS) is the key to many applications in spatial audio. Its large amount of data makes it difficult to make real-time implementation. Reducing HRTF data is necessary and important. In this paper, we apply a new developed signal Decomposition theory, named Adaptive Fourier Decomposition (AFD), to decompose and compress HRTF data, comparing with traditional Fourier's convergence property and PCA's compression property. Simulation results show that the proposed AFD-based Decomposition and compression method enables evident performance improvement for HRTF.
Dominique M.r. Corteville - One of the best experts on this subject based on the ideXlab platform.
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Fourier Decomposition pulmonary mri using a variable flip angle balanced steady state free precession technique
Magnetic Resonance in Medicine, 2015Co-Authors: Dominique M.r. Corteville, Åsmund Kjørstad, Thomas Henzler, Frank G. Zöllner, Lothar R. SchadAbstract:Purpose Fourier Decomposition (FD) is a noninvasive method for assessing ventilation and perfusion-related information in the lungs. However, the technique has a low signal-to-noise ratio (SNR) in the lung parenchyma. We present an approach to increase the SNR in both morphological and functional images. Methods The data used to create functional FD images are usually acquired using a standard balanced steady-state free precession (bSSFP) sequence. In the standard sequence, the possible range of the flip angle is restricted due to specific absorption rate (SAR) limitations. Thus, using a variable flip angle approach as an optimization is possible. This was validated using measurements from a phantom and six healthy volunteers. Results The SNR in both the morphological and functional FD images was increased by 32%, while the SAR restrictions were kept unchanged. Furthermore, due to the higher SNR, the effective resolution of the functional images was increased visibly. The variable flip angle approach did not introduce any new transient artifacts, and blurring artifacts were minimized. Conclusion Both a gain in SNR and an effective resolution gain in functional lung images can be obtained using the FD method in conjunction with a variable flip angle optimized bSSFP sequence. Magn Reson Med 73:1999–2004, 2015. © 2014 Wiley Periodicals, Inc.
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Non-invasive quantitative pulmonary V/Q imaging using Fourier Decomposition MRI at 1.5T.
Zeitschrift fur medizinische Physik, 2015Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Lothar R. SchadAbstract:Abstract Objectives Techniques for quantitative pulmonary perfusion and ventilation using the Fourier Decomposition method were recently demonstrated. We combine these two techniques and show that ventilation-perfusion (V/Q) imaging is possible using only a single MR acquisition of less than thirty seconds. Methods The Fourier Decomposition method is used in combination with two quantification techniques, which extract baselines from within the images themselves and thus allows quantification. For the perfusion, a region assumed to consist of 100% blood is utilized, while for the ventilation the zero-frequency component is used. V/Q-imaging is then done by dividing the quantified ventilation map with the quantified perfusion map. The techniques were used on ten healthy volunteers and fifteen patients diagnosed with lung cancer. Results A mean V/Q-ratio of 1.15±0.22 was found for the healthy volunteers and a mean V/Q-ratio of 1.93±0.83 for the non-afflicted lung in the patients. Mean V/Q-ratio in the afflicted (tumor-bearing) lung was found to be 1.61±1.06. Functional defects were clearly visible in many of the patient images, but 5 of 15 patient images had to be excluded due to artifacts or low SNR, indicating a lack of robustness. Conclusion Non-invasive, quantitative V/Q-imaging is possible using Fourier Decomposition MRI. The method requires only a single acquisition of less than 30 seconds, but robustness in patients remains an issue.
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Quantitative lung ventilation using Fourier Decomposition MRI; comparison and initial study
Magnetic Resonance Materials in Physics Biology and Medicine, 2014Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Erlend Hodneland, Lothar R. SchadAbstract:Objective The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation and perfusion-related information in the lungs, but the lack of quantifiable values is a drawback. We demonstrate a novel technique for quantification of the FD ventilation maps, compare it to two published methods, and show results from both healthy volunteers and patients diagnosed with lung cancer. Materials and methods We quantified the standard FD ventilation images by utilizing additional information, i.e., the zero-frequency component image, which is also obtained from the Fourier analysis. This image acts as a baseline for the changes recorded in the FD ventilation image and can therefore be used to calculate the ventilation. Using this technique, we compared the ventilation values from ten healthy volunteers and ten patients to two previously published methods for quantitative ventilation assessment. Results All methods showed good overall agreement (mean difference between the methods was 14–38 ml/min). The mean minute ventilation for the FD method was calculated to be 693 ml/min for a 2D slice, which is in the expected range. Conclusion The zero-frequency component image can be used as a baseline to quantify the FD ventilation maps. Our initial study showed good agreement with published methods in healthy volunteers, but less so in patients with lung cancer.
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Quantitative lung ventilation using Fourier Decomposition MRI; comparison and initial study
Magma (New York N.Y.), 2014Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Erlend Hodneland, Lothar R. SchadAbstract:Objective The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation and perfusion-related information in the lungs, but the lack of quantifiable values is a drawback. We demonstrate a novel technique for quantification of the FD ventilation maps, compare it to two published methods, and show results from both healthy volunteers and patients diagnosed with lung cancer.
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Quantitative lung perfusion evaluation using Fourier Decomposition perfusion MRI.
Magnetic resonance in medicine, 2013Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, André Fischer, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Lothar R. SchadAbstract:Purpose To quantitatively evaluate lung perfusion using Fourier Decomposition perfusion MRI. The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation- and perfusion-related information in the lungs, where the perfusion maps in particular have shown promise for clinical use. However, the perfusion maps are nonquantitative and dimensionless, making follow-ups and direct comparisons between patients difficult. We present an approach to obtain physically meaningful and quantifiable perfusion maps using the FD method. Methods The standard FD perfusion images are quantified by comparing the partially blood-filled pixels in the lung parenchyma with the fully blood-filled pixels in the aorta. The percentage of blood in a pixel is then combined with the temporal information, yielding quantitative blood flow values. The values of 10 healthy volunteers are compared with SEEPAGE measurements which have shown high consistency with dynamic contrast enhanced-MRI. Results All pulmonary blood flow (PBF) values are within the expected range. The two methods are in good agreement (mean difference = 0.2 mL/min/100 mL, mean absolute difference = 11 mL/min/100 mL, mean PBF-FD = 150 mL/min/100 mL, mean PBF-SEEPAGE = 151 mL/min/100 mL). The Bland-Altman plot shows a good spread of values, indicating no systematic bias between the methods. Conclusion Quantitative lung perfusion can be obtained using the Fourier Decomposition method combined with a small amount of postprocessing. Magn Reson Med 72:558–562, 2014. © 2013 Wiley Periodicals, Inc.
Åsmund Kjørstad - One of the best experts on this subject based on the ideXlab platform.
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Fourier Decomposition pulmonary mri using a variable flip angle balanced steady state free precession technique
Magnetic Resonance in Medicine, 2015Co-Authors: Dominique M.r. Corteville, Åsmund Kjørstad, Thomas Henzler, Frank G. Zöllner, Lothar R. SchadAbstract:Purpose Fourier Decomposition (FD) is a noninvasive method for assessing ventilation and perfusion-related information in the lungs. However, the technique has a low signal-to-noise ratio (SNR) in the lung parenchyma. We present an approach to increase the SNR in both morphological and functional images. Methods The data used to create functional FD images are usually acquired using a standard balanced steady-state free precession (bSSFP) sequence. In the standard sequence, the possible range of the flip angle is restricted due to specific absorption rate (SAR) limitations. Thus, using a variable flip angle approach as an optimization is possible. This was validated using measurements from a phantom and six healthy volunteers. Results The SNR in both the morphological and functional FD images was increased by 32%, while the SAR restrictions were kept unchanged. Furthermore, due to the higher SNR, the effective resolution of the functional images was increased visibly. The variable flip angle approach did not introduce any new transient artifacts, and blurring artifacts were minimized. Conclusion Both a gain in SNR and an effective resolution gain in functional lung images can be obtained using the FD method in conjunction with a variable flip angle optimized bSSFP sequence. Magn Reson Med 73:1999–2004, 2015. © 2014 Wiley Periodicals, Inc.
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Non-invasive quantitative pulmonary V/Q imaging using Fourier Decomposition MRI at 1.5T.
Zeitschrift fur medizinische Physik, 2015Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Lothar R. SchadAbstract:Abstract Objectives Techniques for quantitative pulmonary perfusion and ventilation using the Fourier Decomposition method were recently demonstrated. We combine these two techniques and show that ventilation-perfusion (V/Q) imaging is possible using only a single MR acquisition of less than thirty seconds. Methods The Fourier Decomposition method is used in combination with two quantification techniques, which extract baselines from within the images themselves and thus allows quantification. For the perfusion, a region assumed to consist of 100% blood is utilized, while for the ventilation the zero-frequency component is used. V/Q-imaging is then done by dividing the quantified ventilation map with the quantified perfusion map. The techniques were used on ten healthy volunteers and fifteen patients diagnosed with lung cancer. Results A mean V/Q-ratio of 1.15±0.22 was found for the healthy volunteers and a mean V/Q-ratio of 1.93±0.83 for the non-afflicted lung in the patients. Mean V/Q-ratio in the afflicted (tumor-bearing) lung was found to be 1.61±1.06. Functional defects were clearly visible in many of the patient images, but 5 of 15 patient images had to be excluded due to artifacts or low SNR, indicating a lack of robustness. Conclusion Non-invasive, quantitative V/Q-imaging is possible using Fourier Decomposition MRI. The method requires only a single acquisition of less than 30 seconds, but robustness in patients remains an issue.
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Quantitative lung ventilation using Fourier Decomposition MRI; comparison and initial study
Magnetic Resonance Materials in Physics Biology and Medicine, 2014Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Erlend Hodneland, Lothar R. SchadAbstract:Objective The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation and perfusion-related information in the lungs, but the lack of quantifiable values is a drawback. We demonstrate a novel technique for quantification of the FD ventilation maps, compare it to two published methods, and show results from both healthy volunteers and patients diagnosed with lung cancer. Materials and methods We quantified the standard FD ventilation images by utilizing additional information, i.e., the zero-frequency component image, which is also obtained from the Fourier analysis. This image acts as a baseline for the changes recorded in the FD ventilation image and can therefore be used to calculate the ventilation. Using this technique, we compared the ventilation values from ten healthy volunteers and ten patients to two previously published methods for quantitative ventilation assessment. Results All methods showed good overall agreement (mean difference between the methods was 14–38 ml/min). The mean minute ventilation for the FD method was calculated to be 693 ml/min for a 2D slice, which is in the expected range. Conclusion The zero-frequency component image can be used as a baseline to quantify the FD ventilation maps. Our initial study showed good agreement with published methods in healthy volunteers, but less so in patients with lung cancer.
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Quantitative lung ventilation using Fourier Decomposition MRI; comparison and initial study
Magma (New York N.Y.), 2014Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Erlend Hodneland, Lothar R. SchadAbstract:Objective The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation and perfusion-related information in the lungs, but the lack of quantifiable values is a drawback. We demonstrate a novel technique for quantification of the FD ventilation maps, compare it to two published methods, and show results from both healthy volunteers and patients diagnosed with lung cancer.
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Quantitative lung perfusion evaluation using Fourier Decomposition perfusion MRI.
Magnetic resonance in medicine, 2013Co-Authors: Åsmund Kjørstad, Dominique M.r. Corteville, André Fischer, Thomas Henzler, Gerald Schmid-bindert, Frank G. Zöllner, Lothar R. SchadAbstract:Purpose To quantitatively evaluate lung perfusion using Fourier Decomposition perfusion MRI. The Fourier Decomposition (FD) method is a noninvasive method for assessing ventilation- and perfusion-related information in the lungs, where the perfusion maps in particular have shown promise for clinical use. However, the perfusion maps are nonquantitative and dimensionless, making follow-ups and direct comparisons between patients difficult. We present an approach to obtain physically meaningful and quantifiable perfusion maps using the FD method. Methods The standard FD perfusion images are quantified by comparing the partially blood-filled pixels in the lung parenchyma with the fully blood-filled pixels in the aorta. The percentage of blood in a pixel is then combined with the temporal information, yielding quantitative blood flow values. The values of 10 healthy volunteers are compared with SEEPAGE measurements which have shown high consistency with dynamic contrast enhanced-MRI. Results All pulmonary blood flow (PBF) values are within the expected range. The two methods are in good agreement (mean difference = 0.2 mL/min/100 mL, mean absolute difference = 11 mL/min/100 mL, mean PBF-FD = 150 mL/min/100 mL, mean PBF-SEEPAGE = 151 mL/min/100 mL). The Bland-Altman plot shows a good spread of values, indicating no systematic bias between the methods. Conclusion Quantitative lung perfusion can be obtained using the Fourier Decomposition method combined with a small amount of postprocessing. Magn Reson Med 72:558–562, 2014. © 2013 Wiley Periodicals, Inc.