Quantification of blood volume flow rates from dynamic angiography data

Patent metadata

Field	Value
Publication number	US-9501620-B2
Application number	US-201313815854-A
Country	US
Kind code	B2
Filing date	Mar 14, 2013
Priority date	Mar 20, 2012
Publication date	Nov 22, 2016
Grant date	Nov 22, 2016

Abstract

Systems and methods are disclosed for quantifying absolute blood volume flow rates by fitting a kinetic model incorporating blood volume, bolus dispersion and signal attenuation to dynamic angiographic data. A self-calibration method is described for both 2D and 3D data sets to convert the relative blood volume parameter into absolute units. The parameter values are then used to simulate the signal arising from a very short bolus, in the absence of signal attenuation, which can be readily encompassed within a vessel mask of interest. The volume flow rate can then be determined by calculating the blood volume within the vessel mask and dividing by the simulated bolus duration. This method is exemplified using non-contrast magnetic resonance imaging data from a flow phantom and the cerebral arteries of healthy volunteers and a patient with Moya-Moya disease acquired using a 2D vessel-encoded pseudo-continuous arterial spin labeling pulse sequence. This allows flow quantification in downstream vessels from each brain-feeding artery separately. The systems and methods can be of use in patients with a variety of cerebrovascular diseases, such as the assessment of collateral flow in patients with steno-occlusive disease or the evaluation of arteriovenous malformations.

First claim

What is claimed:

1. A method for quantification of blood volume flow rates within a vessel mask including blood vessels of interest within a subject, from dynamic angiography data, comprising the steps of:
A. Acquiring dynamic angiographic data which covers the blood vessels of interest in a subject;
B. Fitting a kinetic model to the dynamic angiographic data in each voxel or a subset of voxels deemed to contain the major blood vessels to derive parameter maps relating to relative blood volume, blood arrival time and dispersion of a blood bolus;
C. Determining a calibration factor using the dynamic angiographic data and using the calibration factor to convert relative blood volume in each voxel or subset of voxels to an absolute blood volume;
D. Using the derived parameter maps to simulate over a number of time points an expected signal that would arise from a bolus of labeled blood in the absence of signal decay;
E. Summing the simulated signal within the vessel mask of interest and converting the summed signal into an estimate of absolute blood volume within the vessel mask using the calibration factor at each simulated time point; and
F. Determining an absolute blood volume flow rate within the vessel mask of interest from the blood volume of step E and the simulated expected signal that would arise from a bolus of labeled blood at each simulated time point.

2. The method of claim 1 , wherein the dynamic angiographic data is acquired by providing a medical imaging device, positioning the subject in association with the medical imaging device, and using the medical imaging device to acquire the data.

3. The method of claim 1 , further including use of the parameter maps to simulate blood flow at any desired time points in the absence of signal decay/attenuation for unbiased data visualization.

4. The method of claim 3 , wherein the desired time points include blood arrival to the vessel mask.

5. The method of claim 1 , when applied to artery-specific data, further including determination of the relative blood volume flow rates from each feeding artery in well-mixed vessel segments.

6. The method of claim 1 , further including the step of assessment of time points during which the simulated bolus data lies entirely within a vessel mask of interest to obtain estimates of the blood volume flow rate.

7. A system comprising
at least one computing device;
at least one application executable in the at least one computing device,
the at least one application comprising logic that:
A. acquires dynamic angiographic data which covers the blood vessels of interest in a subject;
B. fits a kinetic model to the dynamic angiographic data in each voxel or a subset of voxels deemed to contain the major blood vessels to derive parameter maps relating to relative blood volume, blood arrival time and dispersion of a blood bolus;
C. determines a calibration factor and using the calibration factor to convert relative blood volume in each voxel or subset of voxels to an absolute blood volume;
D. uses the derived parameter maps to simulate over a number of time points an expected signal that would arise from a bolus of labeled blood in the absence of signal decay;
E. sums the simulated signal within a vessel mask of interest and converting the summed signal into an estimate of absolute blood volume within the vessel mask using the calibration factor at each simulated time point; and
F. determines an absolute blood volume flow rate within the vessel mask of interest from the blood volume of step E and the simulated expected signal that would arise from a bolus of labeled blood at each simulated time point.

8. The method of claim 7 , wherein the logic acquires the dynamic angiographic data from a medical imaging device, wherein the subject is positioned in association with the medical imaging device, and the medical imaging device is used to acquire the data.

9. The system of claim 7 , wherein the logic uses the parameter maps to simulate blood flow at any desired time points in the absence of signal decay/attenuation for unbiased data visualization.

10. The system of claim 9 , wherein the desired time points include blood arrival to the vessel mask.

11. The system of claim 7 , wherein the logic is applied to artery-specific data and further determines the relative blood volume flow rates from each feeding artery in well-mixed vessel segments.

12. The method of claim 7 , wherein the logic assesses time points during which the simulated bolus data lies entirely within a vessel mask of interest to obtain estimates of the blood volume flow rate.

13. The method of claim 1 , wherein the filling of the kinetic model derives parameter maps relating to blood volume, blood arrival time and dispersion of a blood bolus within the vessel mask.

14. The system of claim 7 , wherein the logic that fits the kinetic model derives parameter maps relating to blood volume, blood arrival time and dispersion of a blood bolus within the vessel mask.

Assignees

• Isis Innovation
• Univ Oxford Innovation Ltd

Inventors

• Okell Thomas
• Chappell Michael
• Jezzard Peter

Classifications

• A61B6/5217

  extracting a diagnostic or physiological parameter from medical diagnostic data · CPC title

• A61B6/504 Primary

  for diagnosis of blood vessels, e.g. by angiography · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• A61B6/507

  for determination of haemodynamic parameters, e.g. perfusion CT · CPC title

• G16H50/50 Primary

  for simulation or modelling of medical disorders · CPC title