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Title: SU-D-207A-02: Possible Characterization of the Brain Tumor Vascular Environment by a Novel Strategy of Quantitative Analysis in Dynamic Contrast Enhanced MR Imaging: A Combination of Both Patlak and Logan Analyses

Abstract

Purpose: The majority of quantitative analyses involving dynamic contrast enhanced (DCE) MRI have been performed to obtain kinetic parameters such as Ktrans and ve. Such analyses are generally performed assuming a “reversible” tissue compartment, where the tracer is assumed to be rapidly equilibrated between the plasma and tissue compartments. However, some tumor vascular environments may be more suited for a “non-reversible” tissue compartment, where, as with FDG PET imaging, the tracer is continuously deposited into the tissue compartment (or the return back to the plasma compartment is very slow in the imaging time scale). Therefore, Patlak and Logan analyses, which represent tools for the “non-reversible” and “reversible” modeling, respectively, were performed to better characterize the brain tumor vascular environment. Methods: A voxel-by-voxel analysis was performed to generate both Patlak and Logan plots in two brain tumor patients, one with grade III astrocytoma and the other with grade IV astrocytoma or glioblastoma. The slopes of plots and the r-square were then obtained by linear fitting and compared for each voxel. Results: The 2-dimensional scatter plots of Logan (Y-axis) vs. Patlak slopes (X-axis) clearly showed increased Logan slopes for glioblastoma (Figure 3A). The scatter plots of goodness-of-fit (Figure 3B) also suggested glioblastoma,more » relative to grade III astrocytoma, might consist of more voxels that are kinetically Logan-like (i.e. rapidly equilibrated extravascular space and active vascular environment). Therefore, the enhanced Logan-like behavior (and the Logan slope) in glioblastoma may imply an increased fraction of active vascular environment, while the enhanced Patlak-like behavior implies the vascular environment permitting a relatively slower washout of the tracer. Conclusion: Although further verification is required, the combination of Patlak and Logan analyses in DCE MRI may be useful in characterizing the tumor vascular environment, and thus, may have implications in tumor grading and monitoring response to anti-vascular therapy.« less

Authors:
; ; ; ;  [1]
  1. Beaumont Health System, Royal Oak, MI (United States)
Publication Date:
OSTI Identifier:
22624393
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ANIMAL TISSUES; ASTROCYTOMAS; BIOMEDICAL RADIOGRAPHY; BRAIN; NMR IMAGING; POSITRON COMPUTED TOMOGRAPHY; THERAPY

Citation Formats

Yee, S, Chinnaiyan, P, Wloch, J, Pirkola, M, and Yan, D. SU-D-207A-02: Possible Characterization of the Brain Tumor Vascular Environment by a Novel Strategy of Quantitative Analysis in Dynamic Contrast Enhanced MR Imaging: A Combination of Both Patlak and Logan Analyses. United States: N. p., 2016. Web. doi:10.1118/1.4955649.
Yee, S, Chinnaiyan, P, Wloch, J, Pirkola, M, & Yan, D. SU-D-207A-02: Possible Characterization of the Brain Tumor Vascular Environment by a Novel Strategy of Quantitative Analysis in Dynamic Contrast Enhanced MR Imaging: A Combination of Both Patlak and Logan Analyses. United States. doi:10.1118/1.4955649.
Yee, S, Chinnaiyan, P, Wloch, J, Pirkola, M, and Yan, D. Wed . "SU-D-207A-02: Possible Characterization of the Brain Tumor Vascular Environment by a Novel Strategy of Quantitative Analysis in Dynamic Contrast Enhanced MR Imaging: A Combination of Both Patlak and Logan Analyses". United States. doi:10.1118/1.4955649.
@article{osti_22624393,
title = {SU-D-207A-02: Possible Characterization of the Brain Tumor Vascular Environment by a Novel Strategy of Quantitative Analysis in Dynamic Contrast Enhanced MR Imaging: A Combination of Both Patlak and Logan Analyses},
author = {Yee, S and Chinnaiyan, P and Wloch, J and Pirkola, M and Yan, D},
abstractNote = {Purpose: The majority of quantitative analyses involving dynamic contrast enhanced (DCE) MRI have been performed to obtain kinetic parameters such as Ktrans and ve. Such analyses are generally performed assuming a “reversible” tissue compartment, where the tracer is assumed to be rapidly equilibrated between the plasma and tissue compartments. However, some tumor vascular environments may be more suited for a “non-reversible” tissue compartment, where, as with FDG PET imaging, the tracer is continuously deposited into the tissue compartment (or the return back to the plasma compartment is very slow in the imaging time scale). Therefore, Patlak and Logan analyses, which represent tools for the “non-reversible” and “reversible” modeling, respectively, were performed to better characterize the brain tumor vascular environment. Methods: A voxel-by-voxel analysis was performed to generate both Patlak and Logan plots in two brain tumor patients, one with grade III astrocytoma and the other with grade IV astrocytoma or glioblastoma. The slopes of plots and the r-square were then obtained by linear fitting and compared for each voxel. Results: The 2-dimensional scatter plots of Logan (Y-axis) vs. Patlak slopes (X-axis) clearly showed increased Logan slopes for glioblastoma (Figure 3A). The scatter plots of goodness-of-fit (Figure 3B) also suggested glioblastoma, relative to grade III astrocytoma, might consist of more voxels that are kinetically Logan-like (i.e. rapidly equilibrated extravascular space and active vascular environment). Therefore, the enhanced Logan-like behavior (and the Logan slope) in glioblastoma may imply an increased fraction of active vascular environment, while the enhanced Patlak-like behavior implies the vascular environment permitting a relatively slower washout of the tracer. Conclusion: Although further verification is required, the combination of Patlak and Logan analyses in DCE MRI may be useful in characterizing the tumor vascular environment, and thus, may have implications in tumor grading and monitoring response to anti-vascular therapy.},
doi = {10.1118/1.4955649},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: To implement the Gradient Echo Plural Contrast Imaging(GEPCI) technique in MRI-simulation for radiation therapy and assess the feasibility of using GEPCI images with advanced inhomogeneity correction in MRI-guided radiotherapy for brain treatment. Methods: An optimized multigradient-echo GRE sequence (TR=50ms;TE1=4ms;delta-TE=4ms;flip angle=300,11 Echoes) was developed to generate both structural (T1w and T2*w) and functional MRIs (field and susceptibility maps) from a single acquisition. One healthy subject (Subject1) and one post-surgical brain cancer patient (Subject2) were scanned on a Philips Ingenia 1.5T MRI used for radiation therapy simulation. Another healthy subject (Subject3) was scanned on a 0.35T MRI-guided radiotherapy (MR-IGRT) system (ViewRay).more » A voxel spread function (VSF) was used to correct the B0 inhomogeneities caused by surgical cavities and edema for Subject2. GEPCI images and standard radiotherapy planning MRIs for this patient were compared focusing the delineation of radiotherapy target region. Results: GEPCI brain images were successfully derived from all three subjects with scan times of <7 minutes. The images derived for Subjects1&2 demonstrated that GEPCI can be applied and combined into radiotherapy MRI simulation. Despite low field, T1-weighted and R2* images were successfully reconstructed for Subject3 and were satisfactory for contour and target delineation. The R2* distribution of grey matter (center=12,FWHM=4.5) and white matter (center=14.6, FWHM=2) demonstrated the feasibility for tissue segmentation and quantification. The voxel spread function(VSF) corrected surgical site related inhomogeneities for Subject2. R2* and quantitative susceptibility map(QSM) images for Subject2 can be used to quantitatively assess the brain structure response to radiation over the treatment course. Conclusion: We implemented the GEPCI technique in MRI-simulation and in MR-IGRT system for radiation therapy. The images demonstrated that it is feasible to adopt this technique in radiotherapy for structural delineation. The preliminary data also enable the opportunity for quantitative assessment of radiation response of the target region and normal tissue.« less
  • Purpose: To develop a computerized pharmacokinetic model-free Gross Tumor Volume (GTV) segmentation method based on dynamic contrastenhanced MRI (DCE-MRI) data that can improve physician GTV contouring efficiency. Methods: 12 patients with biopsy-proven early stage breast cancer with post-contrast enhanced DCE-MRI images were analyzed in this study. A fuzzy c-means (FCM) clustering-based method was applied to segment 3D GTV from pre-operative DCE-MRI data. A region of interest (ROI) is selected by a clinician/physicist, and the normalized signal evolution curves were calculated by dividing the signal intensity enhancement value at each voxel by the pre-contrast signal intensity value at the corresponding voxel.more » Three semi-quantitative metrics were analyzed based on normalized signal evolution curves: initial Area Under signal evolution Curve (iAUC), Immediate Enhancement Ratio (IER), and Variance of Enhancement Slope (VES). The FCM algorithm wass applied to partition ROI voxels into GTV voxels and non-GTV voxels by using three analyzed metrics. The partition map for the smaller cluster is then generated and binarized with an automatically calculated threshold. To reduce spurious structures resulting from background, a labeling operation was performed to keep the largest three-dimensional connected component as the identified target. Basic morphological operations including hole-filling and spur removal were useutilized to improve the target smoothness. Each segmented GTV was compared to that drawn by experienced radiation oncologists. An agreement index was proposed to quantify the overlap between the GTVs identified using two approaches and a thershold value of 0.4 is regarded as acceptable. Results: The GTVs identified by the proposed method were overlapped with the ones drawn by radiation oncologists in all cases, and in 10 out of 12 cases, the agreement indices were above the threshold of 0.4. Conclusion: The proposed automatic segmentation method was shown to be promising and might be used to improve physician contouring efficiency. J Horton receives grant from NIH and Varian Medical Systems; F-F Yin receives grant from Varian Medical Systems.« less
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