The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform
Nils Daniel Forkert - One of the best experts on this subject based on the ideXlab platform.
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Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA
IEEE Transactions on Biomedical Engineering, 2020Co-Authors: Renzo Phellan, Michael Helle, Magdalena Sokolska, Rolf H. Jäger, Thomas Lindner, Clarissa L. Yasuda, Alexandre X. Falcão, Nils Daniel ForkertAbstract:Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and Blood Flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses Blood Flow data to improve its accuracy, is extended to estimate Blood Flow Parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated Blood Flow Parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined Blood Flow estimation is more realistic compared to the raw hemodynamic Parameters. Conclusion: The proposed method can provide accurate segmentations and Blood Flow Parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.
Renzo Phellan - One of the best experts on this subject based on the ideXlab platform.
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Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA
IEEE Transactions on Biomedical Engineering, 2020Co-Authors: Renzo Phellan, Michael Helle, Magdalena Sokolska, Rolf H. Jäger, Thomas Lindner, Clarissa L. Yasuda, Alexandre X. Falcão, Nils Daniel ForkertAbstract:Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and Blood Flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses Blood Flow data to improve its accuracy, is extended to estimate Blood Flow Parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated Blood Flow Parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined Blood Flow estimation is more realistic compared to the raw hemodynamic Parameters. Conclusion: The proposed method can provide accurate segmentations and Blood Flow Parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.
Liu Zhendong - One of the best experts on this subject based on the ideXlab platform.
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effects of amlodipine plus amiloride hydrochlorothiazide versus amlodipine plus telmisartan on carotid atherosclerosis in hypertensive patients
Heart, 2011Co-Authors: Sun Shangwen, Lu Fanghong, Zhao Yingxin, Wang Shujian, Liu ZhendongAbstract:Objective To investigate the effects of amlodipine plus amiloride/hydrochlorothiazide versus amlodipine plus telmisartan on carotid atherosclerosis in hypertensive patients. Methods The patients with essential hypertension were randomly divided into amlodipine plus amiloride/HCTZ (Group A, n=207) or into amlodipine plus telmisartan (Group B, n=211). Carotid arterial mean intimal-medial thickness (MIMT) and carotid inner diameters and Blood Flow Parameter and carotid plaques were measured with high resolution ultrasound for two groups. For groups A and B, all above indices and Blood pressure were measured again after 12 months and 24 months of treatment respectively. Results There was no difference in reduction of Blood pressure and improvement of PSV and EDV between group A and B (p>0.05), there effect was more remarkable with treatment prolonged, but amlodipine plus telmisartan had better effect of improvement RI of CCA; The MIMT and Crouse scores of carotid after treatment were reduced significantly and inner diameter was significantly enlarged between two groups, the effects was more remarkable with treatment prolonged, Compared with group A, group B had a significantly better effect on normalising carotid IMT and decreasing of Crouse scores (p Conclusion The combinations therapy of amlodipine plus amiloride/HCTZ and amlodipine plus telmisartan produced a similarly and statistically significant BP reduction, but amlodipine plus telmisartan has better effect on regression of abnormal function and structure of large arteries, which may delay the progression of atherosclerosis.
Magdalena Sokolska - One of the best experts on this subject based on the ideXlab platform.
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Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA
IEEE Transactions on Biomedical Engineering, 2020Co-Authors: Renzo Phellan, Michael Helle, Magdalena Sokolska, Rolf H. Jäger, Thomas Lindner, Clarissa L. Yasuda, Alexandre X. Falcão, Nils Daniel ForkertAbstract:Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and Blood Flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses Blood Flow data to improve its accuracy, is extended to estimate Blood Flow Parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated Blood Flow Parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined Blood Flow estimation is more realistic compared to the raw hemodynamic Parameters. Conclusion: The proposed method can provide accurate segmentations and Blood Flow Parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.
Rolf H. Jäger - One of the best experts on this subject based on the ideXlab platform.
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Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA
IEEE Transactions on Biomedical Engineering, 2020Co-Authors: Renzo Phellan, Michael Helle, Magdalena Sokolska, Rolf H. Jäger, Thomas Lindner, Clarissa L. Yasuda, Alexandre X. Falcão, Nils Daniel ForkertAbstract:Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and Blood Flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses Blood Flow data to improve its accuracy, is extended to estimate Blood Flow Parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated Blood Flow Parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined Blood Flow estimation is more realistic compared to the raw hemodynamic Parameters. Conclusion: The proposed method can provide accurate segmentations and Blood Flow Parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.