Construction of a protocol for measuring blood flow by two-dimensional phase-contrast MRA

Bakker, Chris J.G.; Hoogeveen, Romhild M.; Viergever, Max A.

doi:10.1002/(sici)1522-2586(199901)9:1<119::aid-jmri16>3.0.co;2-f

Cited by 60 publications

(70 citation statements)

References 33 publications

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“…Flow measurements made in vivo are only approximations based on results of phantom studies (12). Therefore, the overall error of phasecontrast measurements is difficult to estimate.…”

Section: Errors and Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Cardiovascular Flow Measurement with Phase-Contrast MR Imaging: Basic Facts and Implementation

Lotz

Meier²,

Leppert³

et al. 2002

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573

465

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Phase-contrast magnetic resonance (MR) imaging is a well-known but undervalued method of obtaining quantitative information on blood flow. Applications of this technique in cardiovascular MR imaging are expanding. According to the sequences available, phase-contrast measurement can be performed in a breath hold or during normal respiration. Prospective as well as retrospective gating techniques can be used. Common errors in phase-contrast imaging include mismatched encoding velocity, deviation of the imaging plane, inadequate temporal resolution, inadequate spatial resolution, accelerated flow and spatial misregistration, and phase offset errors. Flow measurements are most precise if the imaging plane is perpendicular to the vessel of interest and flow encoding is set to through-plane flow. The sequence should be repeated at least once, with a high encoding velocity used initially. If peak velocity has to be estimated, flow measurement is repeated with an adapted encoding velocity. The overall error of a phase-contrast flow measurement comprises errors during prescription as well as errors that occur during image analysis of the flow data. With phase-contrast imaging, the overall error in flow measurement can be reduced to less than 10%, an acceptable level of error for routine clinical use.

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“…Flow measurements made in vivo are only approximations based on results of phantom studies (12). Therefore, the overall error of phasecontrast measurements is difficult to estimate.…”

Section: Errors and Limitationsmentioning

confidence: 99%

“…Accelerated flow (eg, turbulence or stenotic jets) causes the precision of the flow mea surement to decline (12), although this error might be corrected with a short echo time (34).…”

Section: Accelerated Flow and Spatial Misregistrationmentioning

confidence: 99%

Cardiovascular Flow Measurement with Phase-Contrast MR Imaging: Basic Facts and Implementation

Lotz

Meier²,

Leppert³

et al. 2002

RadioGraphics

573

465

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“…One group of methods proposes estimating the velocity offset error in the vessel by examining the velocity offsets in somewhat distant stationary tissue (e.g., chest wall, liver, etc.) and spatially fitting the data using linear or higherorder interpolation (9)(10)(11). A drawback of this technique is that there may be insufficient stationary tissue to accurately determine the offset error in the vessel.…”

Section: Introductionmentioning

confidence: 99%

Baseline Correction of Phase Contrast Images Improves Quantification of Blood Flow in the Great Vessels

Chernobelsky¹,

Shubayev²,

Comeau³

et al. 2007

J. of Cardiovascular Magnetic Resonance

148

128

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“…(1)(2)(3), the output of the PC MRI-like data generator is a set of images (or matrices) differing for slice thickness, in-plane pixel size and number of time frames per cardiac cycle.…”

Section: Pc Mri-like Data Generatormentioning

confidence: 99%

“…In vitro validation is limited by costs, time consumption, proper experimental setup to be built, intrinsic source of noise and/or artifacts such as eddy currents, concomitant gradients, displacement, misregistration, partial volume effects, intra-voxel phase dispersion and aliasing [1][2][3]26]. As for theoretical models [5,35], they remain extremely simplistic descriptors of cardiovascular flows.…”

mentioning

confidence: 99%

Synthetic dataset generation for the analysis and the evaluation of image-based hemodynamics of the human aorta

Morbiducci

Ponzini

Rizzo

et al. 2011

Med Biol Eng Comput

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Here, we consider the issue of generating a suitable controlled environment for the evaluation of phase contrast (PC) MRI measurements. The computational framework, tailored to build synthetic datasets, is based on a two-step approach, i.e., define and implement (1) an accurate CFD model and (2) an image generator able to mime the overall outcomes of a PC MRI acquisition starting from datasets retrieved by the computational model. About 20 different datasets were built by changing relevant image parameters (pixel size, slice thickness, time frames per cardiac cycle). Focusing our attention on the thoracic aorta, synthetic images were processed in order to: (1) verify to which extent the fluid dynamics into the aortic arch is influenced by the image parameters; (2) establish the effect of spatial and temporal interpolation. Our study demonstrates that the integral scale of the aortic bulk flow could be described satisfactorily even when using images which are nowadays acquirable with MRI scanners. However, attention must be paid to near-wall velocities that can be affected by large inaccuracy. In detail, in bulk flow regions error values are well bounded (below 5% for most of the analyzed resolutions), while errors greater than 100% are systematically present at the vessel's wall. Moreover, also the data interpolation process can be responsible for large inaccuracies in new data generation, due to the inherent complexity of the flow field in some connected regions.

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Construction of a protocol for measuring blood flow by two-dimensional phase-contrast MRA

Cited by 60 publications

References 33 publications

Cardiovascular Flow Measurement with Phase-Contrast MR Imaging: Basic Facts and Implementation

Cardiovascular Flow Measurement with Phase-Contrast MR Imaging: Basic Facts and Implementation

Baseline Correction of Phase Contrast Images Improves Quantification of Blood Flow in the Great Vessels

Synthetic dataset generation for the analysis and the evaluation of image-based hemodynamics of the human aorta

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