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Project: MRI-based biomarkers for the prediction of kidney functions

MRI-based biomarkers for the prediction of kidney functions



Project coordinator / PI: Prof. Jarle Rørvik  Image  

Background

The term ”biomarker” generally refers to a measurable indicator of some biological state or condition – being increasingly important in personalized medical diagnosis, treatment planning, and follow-up. The concept of imaging biomarkers has recently been introduced as an equivalent concept in quantitative imaging, denoting a biologic feature, or biomarker detectable in an image (1). The present project addresses the methodological and computational machinery that is crucial for introducing non-invasive imaging biomarkers into clinical research and practice. The mathematical modelling and numerical methods are rather generic in nature, and could be linked to a wide range of potential applications.

The focus of this kidney sub-project is robust estimation of imaging-derived parameters related to flow, perfusion, filtration, and leakage. The main goal is quantification of kidney perfusion (renal plasma flow, RPF) and filtration (glomerular filtration rate, GFR) from multiparametric MRI, incorporating dynamic contrast enhanced imaging (DCE-MRI). The interdisciplinary “Kidney project” has been a core project within the MedViz research network for many years e.g. (2,3,4,5,6). It has obtained financial support from Helse-Vest, University of Bergen, and the EU COST B21 (Renal MRI Working group, with two Workshops being organised in Bergen).

(1) Smith, J. J., Sorensen, A. G. & Thrall, J. H. Biomarkers in imaging: realizing radiology’s future. Radiology 227, 633–638 (2003). http://www.ncbi.nlm.nih.gov/pubmed/12663828

(2) Zöllner FG, Sance R, Rogelj P, Ledesma-Carbayo MJ, Rørvik J, Santos A, Lundervold A. Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses. Comput Med Imaging Graph. 2009;33(3):171-81. http://www.ncbi.nlm.nih.gov/pubmed/19135861

(3) Zöllner, F. G. et al. Assessment of kidney volumes from MRI: acquisition and segmentation techniques. Am J Roentgenol 199, 1060–1069 (2012). http://www.ncbi.nlm.nih.gov/pubmed/23096180

(4) Hodneland, E., Lundervold, A., Rørvik, J. & Munthe-Kaas, A. Z. Normalized gradient fields for nonlinear motion correction of DCE-MRI time series. Comput Med Imaging Graph 38, 202–210 (2014). http://www.ncbi.nlm.nih.gov/pubmed/24440179

(5) Hodneland, E. et al. Segmentation-driven image registration - application to 4D DCE-MRI recordings of the moving kidneys. IEEE Trans Image Process 23, 2392–2404 (2014). http://www.ncbi.nlm.nih.gov/pubmed/24710831

(6) Eikefjord, E. et al. Use of 3D DCE-MRI for the estimation of renal perfusion and glomerular filtration rate (GFR) - an intra-subject comparison of FLASH and KWIC with a comprehensive framework for evaluation. To appear in American Journal of Roentgenology.

Challenges and research questions

Image registration (motion correction) - affine transformation / elastic modelling
Image segmentation - in time and space (renal cortex, medulla, and pelvis)
Pharmacokinetic modelling - multi-compartment modeling, regularization, arterial input function (AIF), deconvolution
Parameter estimation - GFR, RPF, where these “imaging biomarkers” are embedded in noise and movement artefacts (inverse problem)

Below are links to DCE-MRI kidney examinations (in Gnu-zipped NIFTI format) from 10 healthy volunteers, each examined at two imaging sessions seven days apart.

Protocol: MRI-examinations were performed on a 32 channel 1.5 T whole-body scanner (Siemens Magnetom Avanto) using a standard six-channel body matrix coil and table-mounted six-channel spine matrix coil for signal reception.
Coronal-oblique DCE-MRI data were continuously acquired using a spoiled gradient recalled 3D FLASH pulse-sequence:
TE=0.8 ms, TR=2.36 ms, FA= 20°, parallel imaging factor 3. Volumes (30 slices) were acquired every 2.3 s for ~ 6 minutes (74 volumes in total); voxel-size = 2.2 x 2.2 x 3 mm^3; acquisition matrix = 192 x 192. A bolus injection of 0.025 mmol/kg of GdDOTA was administered at 3 mL/s in an antecubital vein using an automated power injector, followed by a 20 mL saline flush. Breathing instructions were given by a CD-player. Five seconds after injection of contrast agent the subjects were instructed to hold their breath for 26 seconds for motion-free first pass perfusion. Subsequent instructions on 15 s breath holds and 25 s free breathing were given during the continuous scan.

SubjectIohexol-GFR (ml/min)eGFR (ml/min)DCE-MRI examination #1DCE-MRI examination #2
1107 138 SG2_FF01_1_flash3d_bh.nii.gz SG2_FF01_2_flash3d_bh.nii.gz
298 109 SG2_FF02_1_flash3d_bh.nii.gz SG2_FF02_2_flash3d_bh.nii.gz
390 108 SG2_FF03_1_flash3d_bh.nii.gz SG2_FF03_2_flash3d_bh.nii.gz
493 107 SG2_FF04_1_flash3d_bh.nii.gz SG2_FF04_2_flash3d_bh.nii.gz
594 83 SG2_FF05_1_flash3d_bh.nii.gz SG2_FF05_2_flash3d_bh.nii.gz
6103 107 SG2_FF06_1_flash3d_bh.nii.gz SG2_FF06_2_flash3d_bh.nii.gz
7112 125 SG2_FF07_1_flash3d_bh.nii.gz SG2_FF07_2_flash3d_bh.nii.gz
8119 142 SG2_FF08_1_flash3d_bh.nii.gz SG2_FF08_2_flash3d_bh.nii.gz
996 110 SG2_FF09_1_flash3d_bh.nii.gz SG2_FF09_2_flash3d_bh.nii.gz
10112 133 SG2_FF10_1_flash3d_bh.nii.gz SG2_FF10_2_flash3d_bh.nii.gz

Download all 10x2 DCE-MRI datasets as a single Kidney.zip file (1.7 GB)



( Let us know and be acknowledged if you use any of these data for publication - jarle.rorvik at helse-bergen.no / arvid.lundervold at biomed.uib.no )

Getting started

The 4D DCE-MRI data can be inspected using the freely available MIPAV (Medical Image Processing, Analysis, and Visualization) tool developed at NIH Centre for Information Technology.

Import data

MATLAB:

Download 'my_load_nifti.m', 'my_strlen.m', 'my_load_nifti_hdr.m', and 'my_save_nifti.m', slightly modified from corresponding files in the the matlab-tree of Freesurfer. The 4D dataset 'SG2_FF01_1_flash3d_bh.nii.gz' can then be read into MATLAB workspace by
>> D = my_load_nifti('SG2_FF01_1_flash3d_bh.nii.gz');
>> size(D.vol)
ans =
192 192 30 74

Data analysis

 

- MedViz kidney project
- BMED360 Lecture 6 on DCE-MRI
- BMED360 Lab 5 on DCE-MRI
- Discussion forum
- Ferl GZ. DATforDCEMRI: An R Package for Deconvolution Analysis and Visualization of
DCE-MRI Data. Journal of Statistical Software 2011;44:1-18. PDF
- Ferl GZ, Xu L, Friesenhahn M, Bernstein LJ, Barboriak DP, Port RE. An automated method
for nonparametric kinetic analysis of clinical DCE-MRI data: application to glioblastoma treated
with bevacizumab. Magn Reson Med 2010;63:1366-1375. PubMed

Reports on results