Karis, John P.

AbstractPurposePerfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual‐echo 3D spiral technique with a single‐bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast‐enhanced (DCE) data and overcome the limitations of current EPI‐based techniques.MethodsA 3D spiral‐based technique with dual‐echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through‐plane SENSE acceleration for improved speed and image quality, in‐plane variable‐density undersampling combined with a sliding‐window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single‐echo EPI and a dual‐echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single‐echo EPI collected during the second bolus injection.ResultsVolunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single‐echo and dual‐echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single‐echo dual‐dose EPI, with the additional DCE information.ConclusionA 3D dual‐echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single‐bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC‐EPI techniques for brain perfusion MRI.

Abstract INTRODUCTION Differentiation of glioblastoma recurrence from post treatment radiation effects (PTRE) would improve patient management. Dynamic susceptibility contrast (DSC) MRI can be used to generate fractional tumor burden (FTB) maps, via application of regional cerebral blood volume (RCBV) thresholds, to spatially differentiate recurrence from PTRE. FTB maps derived from a DSC-MRI protocol that includes a contrast agent preload in addition to the bolus injection and a moderate-flip angle (MFA) been histologically validated. Recently, a single-dose, low-flip angle (LFA) protocol was shown to provide RCBV measurements consistent with the MFA protocol and has been recommended as a consensus protocol. This study aims to identify the RCBV thresholds for the single-dose, LFA protocol that generates FTB maps, concordant with those obtained from the reference double-dose, MFA protocol. METHODS Two sets of DSC-MRI data were collected sequentially from 52 patients using a single-dose, low-flip angle (30o) protocol and a double-dose, moderate flip angle (60o) protocol using TR = 1.5s and TE = 30ms. Standardized RCBV maps (sRCBV) were obtained and co-registered with the post-contrast T1-weighted images using the FDA-approved clinical software, IB Neuro™ and IB Delta Suite™, respectively. The ground truth MFA-based FTB maps were computed using multiple sRCBV thresholds (1.0 and 1.75). An ROC analysis was conducted to identify the optimal, voxel-wise LFA sRCBV thresholds to produce FTB maps that best match the reference maps. RESULTS/ CONCLUSIONS The ROC analysis identified a lower LFA threshold of 1.0 to accurately distinguish PTRE from tumor recurrence and an upper threshold of 1.55 to differentiate aggressive tumor regions. The single-dose, LFA protocol eliminates variations in the contrast agent incubation time between the preload and bolus injection. This study provides motivation for the use of the single-dose LFA protocol in the management of patients with glioblastoma and is a reliable method for distinguishing tumor from PTRE.

PurposeThe purpose of this study was to develop a spiral‐based combined spin‐ and gradient‐echo (spiral‐SAGE) method for simultaneous dynamic contrast‐enhanced (DCE‐MRI) and dynamic susceptibility contrast MRI (DSC‐MRI).MethodsUsing this sequence, we obtained gradient‐echo TEs of 1.69 and 26 ms, a SE TE of 87.72 ms, with a TR of 1663 ms. Using an iterative SENSE reconstruction followed by deblurring, spiral‐induced image artifacts were minimized. Healthy volunteer images are shown to demonstrate image quality using the optimized reconstruction, as well as for comparison with EPI‐based SAGE. A bioreactor phantom was used to compare dynamic‐contrast time courses with both spiral‐SAGE and EPI‐SAGE. A proof‐of‐concept cohort of patients with brain tumors shows the range of hemodynamic maps available using spiral‐SAGE.ResultsComparison of spiral‐SAGE images with conventional EPI‐SAGE images illustrates substantial reductions of image distortion and artifactual image intensity variations. Bioreactor phantom data show similar dynamic contrast time courses between standard EPI‐SAGE and spiral‐SAGE for the second and third echoes, whereas first‐echo data show improvements in quantifying T1 changes with shorter echo times. In a cohort of patients with brain tumors, spiral‐SAGE–based perfusion and permeability maps are shown with comparison with the standard single‐echo EPI perfusion map.ConclusionSpiral‐SAGE provides a substantial improvement for the assessment of perfusion and permeability by mitigating artifacts typically encountered with EPI and by providing a shorter echo time for improved characterization of permeability. Spiral‐SAGE enables quantification of perfusion, permeability, and vessel architectural parameters, as demonstrated in brain tumors.

Abstract Dynamic susceptibility contrast (DSC) MRI measures of brain tumor cerebral blood volume (CBV) are able to predict grade, overall survival and response to treatment. Wide-spread acceptance of DSC-MRI has been challenged by the need to balance contrast agent dose and CBV accuracy. The goal of this study was to identify and validate single-dose, BTIP compliant, DSC-MRI protocols. Using a validated, patient-based DRO, we evaluated CBV accuracy across a range of acquisition parameters (field strength, TR, TE, flip angle, multi-echo acquisitions, dosing protocols) and post-processing steps. To validate the optimal protocols, we next collected DSC-MRI data following ASFNR’s recommended “double – dose” approach, where a single-dose preload (to minimize T1 effects) is given prior to a second bolus injection (for DSC-MRI data acquisition). The single-dose DSC-MRI data was collected during the preload bolus injection. Consistency of the derived CBV data, visual agreement and data characteristics (e.g. CNR) was statistically evaluated. When using a single-dose and routine single-echo pulse sequence, the DRO analysis found that a low flip angle (LFA = 30o) and 30ms TE provided the highest CBV accuracy (concordance correlation coefficient (CCC) = 0.92) and precision (coefficient of variation (CV) = 8.2%)). For comparison, the maximum accuracy found with the DRO utilizes a double-dose injection protocol and yielded a CCC of 0.98 and CV of 6.8%. Single-dose, multi-echo acquisitions provided higher accuracy than the LFA data and matched that found with the double-dose approach. In patients (data collection ongoing), the agreement between single-dose LFA (n > 40) or multi-echo (n > 40) based CBV values and the reference double-dose approach was very high (CCC > 0.94) and were statistically equivalent. Optimized single-dose DSC-MRI protocols provide highly accurate CBV data, use lower doses of contrast agent, and simplify scan procedures, indicating their potential for robust use in clinical practice and trials.

PurposeFluid‐attenuated inversion recovery (FLAIR) nulls the CSF signal and is widely used in neuro MRI exams. A 3D scan can provide high SNR, contiguous coverage, and reduced sensitivity to through‐plane CSF flow. In this work, a 3D spiral FLAIR technique is proposed to improve the image quality of conventional 3D Cartesian FLAIR.MethodsThe 3D spiral FLAIR sequence incorporated a spiral‐in/out readout to preserve higher scan efficiency and eliminate off resonance‐induced artifacts observed with a commonly implemented spiral‐out readout, a compensation approach to minimize phase errors due to the concomitant fields accompanying the spiral gradient, and an adapted variable flip angle scheme to preserve scan efficiency and maintain a long and stable echo train. 3D Cartesian and spiral FLAIR (~6 min each) were acquired on a 3 Tesla scanner from 6 subjects (age range: 31‐64 years; mean: 39.5). Two neuroradiologists rated the images in a blinded fashion on a 5‐point scale. The noise performance was assessed quantitatively.ResultsCompared to 3D Cartesian FLAIR, 3D spiral FLAIR exhibits greater reduction of artifacts from CSF, especially anterior to the brain stem (rated better in 4 cases), artifacts attributed to blood/flow in the deep brain (better or much better in all 6 cases), and superior overall image quality (much better in 5 cases) despite residual susceptibility artifacts near the nasal cavity. Quantitative assessment demonstrates ~1.5× higher average SNR than Cartesian data.Conclusion3D spiral FLAIR achieves higher SNR, reduced CSF, and blood/flow artifacts, providing an alternative to 3D Cartesian FLAIR for neurological exams.

Abstract Purpose: Investigate the utility of patient-specific spatial predictions of tumor cell density from a bio-mathematical model. Introduction: Glioblastomas (GBMs) are the most malignant of all primary brain tumors. While it is known there is always a non-detectable portion of the tumor, current techniques of monitoring GBM progression, imaging and initial histological assessment, are not able to reliably estimate the tumor invasion past the enhancing region on T2-Weighted (T2W) imaging. Over the last two decades, a large effort has been made to create a simple patient-specific mathematical model of gliomas. The resulting model, referred to as the Proliferation-Invasion (PI) model, is based on two key parameters, the net growth rate, ρ, and the dispersal coefficient, D. In this model, the ratio of D/ρ is related to degree of invasion and the product D*ρ, is related to the speed of growth. The intuitive understanding provided by this model has been able to provide patient-specific understanding of disease kinetics enabling prediction of outcomes following surgical resection, radiation and the development of a prognostic response metric. Previous literature utilizing this model has been based on the assumption that what is seen on the pretreatment T1-Weighted contrast-enhanced (T1Gd) and T2W, images correspond to an 80% and 16% tumor cell density threshold respectively. This assumption allows for an estimate of D/ρ from a single time point of imaging. While these values were based on extensive experience, for ethical and technical reasons, they have never been rigorously investigated histologically. Recent technological advances have made it possible for surgeons to use an MRI to guide the acquisition of tissue making it possible to know with a good degree of accuracy where on the MR image the histological specimen comes from. Methods: Model Calibration : To estimate D/ρ for each patient, we assume abnormalities on the T1Gd and T2W images correspond to an 80% and 16% tumor cell density threshold respectively. We then utilize a Bayesian calibration approach based on adaptive grid refinement while holding the velocity constant to find the most likely value of D/ρ to match the observed radial measurements. Three-Dimensional Density Maps : Given a gray/white segmentation and an estimate for D/ρ, we can build a tumor cell density prediction in the patient's anatomy using the Eikonal equations and the modified Fast Marching Method (FMM) algorithm presented by Konukoglu et al. Patient Cohort : Eighteen patients were recruited with clinically suspected GBM undergoing preoperative stereotactic MRI for surgical resection with IRB approval Barrow Neurological Institute and Mayo Clinic in Arizona. Surgical Biopsy : Pre-operative conventional MRI, including T1Gd and T2W, was utilized to guide stereotactic biopsies. An average of 5–6 tissue specimens were acquired from each tumor by using stereotactic surgical localization, following the smallest possible diameter craniotomies to minimize brain shift. Histological Analysis : 4 μm tissue sections were stained with hematoxylin and eosin (H&E) for neuropathology review. H&E slides were reviewed by a neuropathologist. The percent tumor nuclei in the field was estimated. Results and Conclusion: Our analysis showed that the intuitive ordering of diffuse to nodular tumor profiles from the PI model is consistent with the histologic data. Further, utilizing the histologic data, limitations to a universal threshold cutoff of the T2/FLAIR region are specified and a better threshold cutoff value is suggested. We hope that this paper will lead to more studies of this kind resulting in more accurate ways to predict the spatial distribution of GBM tumor cells from medical imaging. Such advances could revolutionize surgical procedures and radiation therapy and may enable additional insights into tumor kinetic differences meaningful for treatment. Citation Format: Andrea Hawkins-Daarud, Lauren DeGirolamo, Joshua Jacobs, Kamala Clark-Swanson, Jennifer M. Eschbacher, Kris A. Smith, Peter Nakaji, Leslie C. Baxter, John P. Karis, Teresa Wu, J. Ross Mitchell, Jing Li, Leland Hu, Kristin R. Swanson. Histologic evidence for a bio-mathematical model of glioblastoma invasion. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A08.

AbstractBackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Abstract Purpose: This study uses spatially accurate image-guided tissue analysis to evaluate the correlation between Perfusion MRI (pMRI) and tissue microvascular density parameters as markers for tumor recurrence and treatment response in high-grade glioma (HGG). Methods: Following Institutional Review Board approval, we recruited previously treated (including chemo-radiation therapy) HGG patients undergoing surgical re-resection of new enhancing lesions on surveillance MRI. Preoperatively, we acquired pMRI and contrast-enhanced stereotactic T1-Weighted (T1W) MRI data sets. Intraoperatively, we recorded stereotactic locations of multiple biopsies based on T1W data sets that guided surgery. Following surgery, we coregistered pMRI and T1W data sets to calculate localized relative cerebral blood volume (rCBV) for each stereotactic specimen location. For each specimen, we performed 1) standard H&E staining to diagnose tumor recurrence or post-treatment radiation effect (PTRE); and 2) immunohistochemical analysis with CD34 to highlight tissue vessels. We analyzed CD-34 stained slides with Axiovision Automeasure 3.4 (Zeiss, Germany) to calculate both total microvessel number (MVN) and total microvessel area (MVA), each normalized to total slide specimen area (μm2). We calculated Pearson correlations to establish relationships between a) rCBV and MVN; and b) rCBV and MVA. We also performed t-test to compare MVN, MVA, and rCBV values between tumor and PTRE samples. A neuroradiologist performed all coregistration and pMRI calculations, and a neuropathologist analyzed all tissue specimens, without knowledge of corresponding data. A biostatistician performed all statistical comparisons. Results: In this preliminary study, we included 16 tissue specimens (from 8 subjects), each diagnosed as either tumor (n=7) or PTRE (n=9). We successfully calculated localized rCBV and determined both MVA and MVN for each specimen. The rCBV values showed highest correlation with total vessel area (MVA) (r=0.65, p=0.007) and slightly less correlation with vessel number (MVN) (r=0.52, p=0.04). Tumor showed significantly higher values than PTRE for all parameters: rCBV (1.87 + 0.82 versus 0.78 + 0.2, p=.002); MVA (0.22 + 0.03 versus 0.04 + 0.007, p=0.0001); and MVN (0.0068 + 0.001 versus. 0.0016 + 0.0006, p=0.0004). Conclusion: These preliminary results show the promise of Perfusion MRI to non-invasively estimate tissue microvessel density and distinguish tumor recurrence from treatment effects. The current study is ongoing to confirm results in a larger patient population. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3748.