PurposeValidation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).ApproachA digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300 μm/60 s) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300 μm/1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.ResultsThe observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.ConclusionAn in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.
Glioblastoma multiforme is the most common and deadly form of primary brain cancer. Even with aggressive treatment consisting of surgical resection, chemotherapy, and external beam radiation therapy, response rates remain poor. In an attempt to improve outcomes, investigators have developed nanoliposomes loaded with 186Re, which are capable of delivering a large dose (< 1000 Gy) of highly localized β- radiation to the tumor, with minimal exposure to healthy brain tissue. Additionally, 186Re also emits gamma radiation (137 keV) so that it’s spatio-temporal distribution can be tracked through single photon emission computed tomography. Planning the delivery of these particles is challenging, especially in cases where the tumor borders the ventricles or previous resection cavities. To address this issue, we are developing a finite element model of convection enhanced delivery for nanoliposome carriers of radiotherapeutics. The model is patient specific, informed by each individual’s own diffusion-weighted and contrast-enhanced magnetic resonance imaging data. The model is then calibrated to single photon emission computed tomography data, acquired at multiple time points mid- and post-infusion, and validation is performed by comparing model predictions to imaging measurements obtained at future time points. After initial calibration to a one SPECT image, the model is capable of recapitulating the distribution volume of RNL with a DICE coefficient of 0.88 and a PCC of 0.80. We also demonstrate evidence of restricted flow due to large nanoparticle size in comparison to interstitial pore size.
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