Characterization of lesions via determination of vascular metrics using MRI data
Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and/or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.
1. A computer-implemented method of non-invasively characterizing a tumor in a region of interest (ROI) based on tumor hemodynamics determined using magnetic resonance imaging (MRI) data, the method comprising:
acquiring, by a computing system, MRI data of a ROI comprising:
a first MRI dataset corresponding to high spatial resolution dynamic contrast enhanced MRI (DCE-MRI) scans of the ROI;
a second MRI dataset corresponding to high temporal resolution DCE-MRI scans of the ROI; and
a third MRI dataset corresponding to diffusion-weighted MRI (DW-MRI) scans of the ROI;
determining, by the computing system, vasculature metrics using the first MRI dataset, the second MRI dataset, and the third MRI dataset;
determining, by the computing system, at least one of tumor-associated blood flow velocity or tumor interstitial pressure by using the vasculature metrics as inputs to a computational fluid dynamics model; and
generating, by the computing system, at least one tumor characteristic based on determining the at least one of tumor-associated blood flow velocity and tumor interstitial pressure.
2. The method of claim 1 , wherein the vasculature metrics include one or more values corresponding to tumor-associated vessel morphology and blood flow.
3. The method of claim 1 , wherein determining vasculature metrics comprises detecting, by the computing system, tumor-associated vessels using the first MRI dataset.
4. The method of claim 1 , wherein determining vasculature metrics comprises segmenting, by the computing device, the tumor from surrounding tissue.
5. The method of claim 4 , wherein segmenting the tumor comprises applying, by the computing system, fuzzy clustering to the first MRI dataset.
6. The method of claim 4 , wherein segmenting the vasculature comprises applying, by the computing system, a multi-scale filter to generate a probability histogram representing probabilities of voxels comprising blood vessels.
7. The method of claim 6 , wherein the multi-scale filter comprises a Hessian-based filter, and wherein the method further comprises setting, by the computing system, a threshold by applying the Hessian-based filter to a region of fixed size covering internal thoracic vessels and using a percentile of the probability histogram as the threshold.
8. The method of claim 6 , further comprising applying, by the computing system, a lowest-cost tracking function to detect at least one of (i) vessel segments likely to be in contact with the tumor, or (ii) paths connecting segmented vessels with the tumor.
9. The method of claim 1 , wherein determining vasculature metrics comprises generating, by the computing system, at least one of (i) one or more parametric maps for one or more image processing parameters through analysis of the second and third MRI datasets, or (ii) numerical meshes for the ROI from segmented masks obtained from the acquired MRI data.
10. The method of claim 1 , wherein determining vasculature metrics comprises applying, by the computing device, an adaptive vessel scaling function to integrate information from a segmented vascular mask and a plasma volume fraction (v p ) map, wherein applying the adaptive vessel scaling function comprises, by the computing system:
skeletonizing the segmented vascular mask to a centerline of the segmented vascular mask;
up-sampling the segmented vascular mask and the vp map to an isotropic resolution; and
at a location along the centerline, identifying a neighborhood in an up-sampled grid using vascular orientation and a step width of half of an up-sampled voxel size, wherein the neighborhood represents a region of the segmented vascular mask covering a section of vessel at the location.
11. The method of claim 1 , wherein determining vasculature metrics comprises generating, by the computing system, a contrast-agent pharmacokinetics model with respect to the tumor using the second and third MRI datasets, wherein generating the contrast-agent pharmacokinetics model comprises applying, by the computing system, a graphical analysis that uses linear regression to identify and analyze pharmacokinetics of the contrast agent.
12. The method of claim 1 , wherein determining vasculature metrics comprises generating, by the computing system, a parametric map of apparent diffusion coefficient contrast-agent pharmacokinetics with respect to the tumor using the second and third MRI datasets.
13. The method of claim 1 , wherein determining the at least one of tumor-associated blood flow velocity and tumor interstitial pressure comprises applying, by the computing system, a fluid dynamics function to quantify at least one of: (i) volumetric blood flow rate, or (ii) interstitial blood flow, or (iii) flux across vascular walls.
14. The method of claim 1 , wherein;
ii the first MRI dataset corresponds to fat-suppressed DCE-MRI scans with spatial resolutions less than two cubic millimeters, wherein the DCE-MRI scans of the first MRI dataset have temporal resolutions greater than 30 seconds; and
(ii) the second MRI dataset comprises ultrafast DCE-MRI data corresponding to ultrafast DCE-MRI scans with temporal resolutions less than five seconds, wherein the ultrafast DCE-MRI scans have spatial resolutions greater than five cubic millimeters (mm 3 ).
15. The method of claim 1 , further comprising outputting, via the computing system, the tumor characteristic, wherein the tumor characteristic comprises a characterization of tumor malignancy, aggressiveness or expected responsiveness of the tumor to a therapy.
16. A computer-implemented method of non-invasively characterizing a tumor by mapping pressure and flow fields in a region of interest (ROI) using quantitative magnetic resonance imaging (MRI) data, the method comprising:
acquiring, by a computing system, MRI data comprising dynamic contrast enhanced MRI (DCE-MRI) scans of the ROI and diffusion-weighted MRI (DW-MRI) scans of the ROI;
dividing, by the computing system, the ROI into a vascular space and an interstitial space by segmenting the MRI data, the vascular space comprising a blood vessel network, and the interstitial space comprising interstitial tissue;
assigning, by the computing system, corresponding material properties to the vascular space and the interstitial space via analysis of MRI data corresponding to both the DCE-MRI and DW-MRI scans;
applying, by the computing system, a computational fluid dynamics model to determine hemodynamics related metrics of a tumor in the ROI; and
generating, by the computing system, an indication of malignancy of the tumor based on the hemodynamics related metrics of the tumor.
17. The method of claim 16 , wherein applying the computational fluid dynamics model comprises determining, by the computing system, at least one of (i) interstitial fluid pressure, (ii) interstitial flow velocity, or (iii) blood flow within the blood vessel network along centerlines of the blood vessel network.
18. A computer-implemented method of non-invasively characterizing a tumor in a region of interest (ROI), the method comprising:
acquiring, by a computing system, a first magnetic resonance imaging (MRI) dataset corresponding to high spatial resolution scans of the ROI, and a second MRI dataset corresponding to high temporal resolution scans of the ROI, the scans of the first and second MRI datasets at least partially overlapping in time;
determining, by the computing system, at least one morphological vascular metric using the first MRI dataset;
determining, by the computing system, at least one functional vascular metric using the second MRI dataset; and
generating, by the computing system, at least one tumor characteristic based on both of the at least one morphological metric and the at least one functional metric.
19. The method of claim 18 , wherein:
i) the at least one morphological vascular metric comprises at least one of a vessel count or vessel tortuosity,
(ii) the at least one functional vascular metric comprises one or more of bolus arrival time, volume transfer coefficient (K trans ), or plasma volume fraction (v p ), and
(iii) the characterization of the tumor is based on a combination of the morphological vascular metric of vessel count and the functional vascular metric of bolus arrival time.
20. The method of claim 18 , wherein determining the at least one morphological vascular metric comprises segmenting, by the computing system, the tumor from surrounding tissue, wherein segmenting the tumor from surrounding tissue comprises applying, by the computing system, fuzzy clustering to data in the first MRI dataset.