Digital Reference Object Research Articles

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods.

To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of the field inhomogeneity on estimated parameters. The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy of and increase to about 33% and 51% without correction for field. We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.

Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.

Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images

BackgroundMagnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established. New methodWe use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of ‘PVS’ orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements’ accuracy. ResultsPVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha= 0.5, beta= 0.5 and c= 500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. ‘PVS’ orientation did not affect measurements in data with isotropic voxels. Comparison with existent methodsDoes not apply. ConclusionsThe in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Digital reference objects for evaluating algorithm performance in MR image formation

PurposeDigital Reference Objects (DROs) are mathematical phantoms that can serve as a basis for evaluating MR image quality (IQ) in an objective way. Their main purpose is to facilitate the establishment of fully automated and perfectly reproducible IQ metrics to objectively compare different algorithms in MR image formation in a standardized manner. They also allow to re-build parts of standard phantoms. MethodsWe sample DROs directly in k-space, using analytical formulas for the continuous Fourier transform of primitive shapes. We demonstrate this DRO approach by applying a state-of-the-art CNN-based denoising algorithm that is robust to varying noise levels to noisy images of the resolution section of the well-known ACR phantom for IQ assessment, reconstructed from both measured and simulated k-space data. ResultsApplying the CNN-based denoising algorithm to the measured and simulated version of the ACR phantom resolution section produced virtually identical results, as confirmed by visual and quantitative comparison. ConclusionsDROs can help guide technology selection during the development of new algorithms in MR image formation, e.g., via deep learning. This could be an important step towards reproducible MR image formation.

Open source code for the generation of digital reference objects for dynamic contrast-enhanced MRI analysis software validation.

Dynamic contrast-enhanced MR images can be analyzed through the application of a wide range of kinetic models. This process is prone to variability and a lack of standardization that can affect the measured metrics. There is a need for customized digital reference objects (DROs) for the validation of DCE-MRI software packages that undertake kinetic model analysis. DROs are currently available only for a small subset of the kinetic models commonly applied to DCE-MRI data. This work aimed to address this gap. Code was written in the MATLAB programming environment to generate customizable DROs. This modular code allows the insertion of a plug-in to describe the kinetic model to be tested. We input our generated DROs into three commercial and open-source analysis packages and assessed the agreement of kinetic model parameter values output with the 'ground-truth' values used in the DRO generation. For the five kinetic models tested, the concordance correlation coefficient values were >98%, indicating excellent agreement of the results with 'ground-truth'. Testing our DROs on three independent software packages produced concordant results, strongly suggesting our DRO generation code is correct. This implies that our DROs can be used to validate other third party software for the kinetic model analysis of DCE-MRI data. This work extends published work of others to allow customized generation of test objects for any applied kinetic model and allows the incorporation of B1 mapping into the DRO for application at higher field strengths.

Abstract 5621: Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes

Abstract High grade glioma (HGG) represents a group of devastating diseases with dismal prognosis. Surgical resection of the contrast enhancing (CE) region of HGG remains the mainstay of treatment, but recurrence inevitably arises from the unresected non-contrast enhancing (NE) region, surgically inaccessible due to cancer cell invasion into healthy brain tissue. Due to its critical role in recurrence, understanding of the NE region is central to the improvement of clinical outcomes. We reveal the biological characteristics of this region through image localized multi-regional sampling. We linked microenvironmental characteristics measured by multi-parametric MRI to genomic mutations and transcriptional phenotypes using mixed effect modeling which allowed us to control for individualized patient effects. We first confirmed that T2 is a significant indicator of IDH mutation status in the NE region, being the first description of such a relationship in a HGG cohort. We found the combination of EGFR amplification and CDKN2A homozygous loss was associated with a significantly lower mean diffusivity (MD) compared to double wild type tumors in the NE region, indicating the presence of greater cellular packing and proliferation in EGFR amplification/CDKN2A loss regions. Finally, using single cell pathway based tumor classifications, we showed that nK2, a DSC-MRI metric representing cell size heterogeneity, correlated positively with neuronal signature and negatively with glycolytic/plurimetabolic signature within the NE tumor, indicating that glycolytic/plurimetobolic tumors possessed a high amount of cell size heterogeneity compared to neuronal samples. This hypothesis was supported using digital reference object (DRO) modeling which confirmed that cell size and heterogeneity drove the differential nK2 signal between neuronal and glycolytic/plurimetabolic samples. We identified immune cell infiltrate as one possible mechanism of increased cell size heterogeneity using transcriptomic signature analysis which found more immune cell signatures within glycolytic/plurimetobolic tumors compared to neuronal. Collectively this study demonstrates the central role of multi-parametric MRI as a non-invasive measure of tumor biology and a tool for understanding the clinically critical NE region which can then inform new therapies targeting this region of HGG recurrence. Citation Format: Matthew Flick, Taylor Weiskittel, Kevin Meng-Lin, Fulvio D'Angelo, Francesca Caruso, Shannon Ensign, Mylan Blomquist, Luija Wang, Christopher Sereduk, Gustavo De Leon, Ashley Nespodzany, Javier Urcuyo, Ashlynn Gonzalez, Lee Curtin, Kyle Singleton, Aliya Anil, Natenael Simmineh, Erika Lewis, Teresa Noviello, Reyna Patel, Panwen Wang, Junwen Wang, Jennifer Eschbacher, Andrea Hawkins-Daarud, Pamela Jackson, Kris Smith, Peter Nakaji, Bernard Bendok, Richard Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Matthew Neal, Kliment Donev, Maciej Mrugala, Alyx Porter, Scott Beeman, Yuxiang Zhou, Leslie Baxter, Christopher Plaisier, Jing Li, Hu Li, Anna Lasorella, Chad Quarles, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan Tran, Leland Hu. Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5621.

Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation

This work proposes to develop and evaluate a deep learning framework that jointly optimizes k-t sampling patterns and reconstruction for head and neck dynamic contrast-enhanced (DCE) MRI aiming to reduce bias and uncertainty of pharmacokinetic (PK) parameter estimation. 2D Cartesian phase encoding k-space subsampling patterns for a 3D spoiled gradient recalled echo (SPGR) sequence along a time course of DCE MRI were jointly optimized in a deep learning-based dynamic MRI reconstruction network by a loss function concerning both reconstruction image quality and PK parameter estimation accuracy. During training, temporal k-space data sharing scheme was optimized as well. The proposed method was trained and tested by multi-coil complex digital reference objects of DCE images (mcDROs). The PK parameters estimated by the proposed method were compared with two published iterative DCE MRI reconstruction schemes using normalized root mean squared errors (NRMSEs) and Bland-Altman analysis at temporal resolutions of [Formula: see text] = 2s, 3s, 4s, and 5s, which correspond to undersampling rates of R = 50, 34, 25, and 20. The proposed method achieved low PK parameter NRMSEs at all four temporal resolutions compared with the benchmark methods on testing mcDROs. The Bland-Altman plots demonstrated that the proposed method reduced PK parameter estimation bias and uncertainty in tumor regions at temporal resolution of 2s. The proposed method also showed robustness to contrast arrival timing variations across patients. This work provides a potential way to increase PK parameter estimation accuracy and precision, and thus facilitate the clinical translation of DCE MRI.

Assessment of perivascular space filtering methods using a three-dimensional computational model

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all “vesselness” filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study.

This study aimed to measure standardized uptake value (SUV) variations across different PET/computed tomography (CT) scanners to harmonize quantification across systems. We acquired images using the National Electrical Manufacturers Association International Electrotechnical Commission phantom from three PET/CT scanners operated using routine imaging protocols at each site. The SUVs of lesions were assessed in the presence of reference values by a digital reference object (DRO) and recommendations by the European Association of Nuclear Medicine (EANM/EARL) to measure inter-site variations. For harmonization, Gaussian filters with tuned full width at half maximum (FWHM) values were applied to images to minimize differences in SUVs between reference and images. Inter-site variation of SUVs was evaluated in both pre- and postharmonization situations. Test-retest analysis was also carried out to evaluate repeatability. SUVs from different scanners became significantly more consistent, and inter-site differences decreased for SUV mean , SUV max and SUV peak from 17.3, 20.7, and 15.5% to 4.8, 4.7, and 2.7%, respectively, by harmonization ( P values <0.05 for all). The values for contrast-to-noise ratio in the smallest lesion of the phantom verified preservation of image quality following harmonization (>2.8%). Harmonization significantly lowered variations in SUV measurements across different PET/CT scanners, improving reproducibility while preserving image quality.

Abstract 2459: Automated VIF methods improve DCE-MRI parameterization repeatability in GBM

Abstract Introduction: Analysis of Dynamic Contrast Enhanced (DCE) MRI is widely used for measuring perfusion kinetics of cancerous tissue, though its implementation and results may vary between sites. Here we study the repeatability of quantitative DCE-MRI parameterization in a publicly available Glioblastoma (GBM) double-baseline dataset and set forth percent repeatability coefficients (%RC) which constitute a level of change at which a detectable biological change in biology may be differentiated from experimental noise. Methods: Double-baseline imaging data was previously acquired for The Cancer Imaging Archive, in the Quantitative Imaging Network (QIN) GBM Treatment Response (TR) dataset. This data includes dual baseline acquisitions of DCE-MRI MRI data performed within 5 days, for 29 patients. Each acquisition is analyzed using two vascular input function (VIF) methods: a manually segmented VIF from the superior sagittal sinus, and an automatically derived VIF determined from the top 10% of rapidly enhancing voxels. Each dynamic scan is analyzed the the Extended Tofts-Kety model (eTM; Ktrans, ve, vp) pharmacokinetic (PK) model. Bland-Altman analysis is then performed on the log-transformed PK parameters to determine the percent repeatability coefficients (%RC) of the data (95% confidence), as well as bias between first and second DCE scans. This analysis was repeated for two separate observers, writing their own analysis code, both of which are validated on a digital reference object to ensure consistency between analysis methods with zero noise. Results: For bothVIF determination methods, the 25-75th percentiles of the change in PK parameters was less than +/- 25%, the standard set forth by QIBA as a detectable biological change. For most parameters, VIF methods, and model combinations, the automatic VIF method has smallest %RC, indicating that automatic VIF determination may enhance the repeatability of the measurements between timepoints. The %RC was lowest for vp when using an automatically determined VIF. Using the automatically determined VIF and eTM, the %RCs of Ktrans, ve, and vp were measured as 72%, 82%, and 53%, respectively. Using the eTM and manually segmented VIF, the %RCs of Ktrans, ve, and vp were measured as 83%, 88%, and 66%. Conclusions: Using the QIN-GBM-TR dataset, the authors have determined that the %RC of Ktrans, ve, and vp to be 72%, 82%, and 53%, respectively (eTM, auto-VIF). Because these relative changes are large, we recommend that all sites abide by QIBA guidelines to enhance DCE parameterization reproducibility. We also recommend usage of an automated AIF determination method, as opposed to manual segmentation of large blood vessels. Citation Format: Ryan T. Woodall, Prativa Sahoo, Yujie Cui, Bihong Chen, Mark Shiroishi, Cristina Lavini, Paul Frankel, Margarita Gutova, Christine Brown, Jennifer Munson, Russell Rockne. Automated VIF methods improve DCE-MRI parameterization repeatability in GBM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2459.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI.

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with Ktrans values ranging from 0.01 to 0.8 min−1 and fixed Ve = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both Ktrans and Ve. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

An Anthropomorphic Digital Reference Object (DRO) for Simulation and Analysis of Breast DCE MRI Techniques.

Advances in accelerated magnetic resonance imaging (MRI) continue to push the bounds on achievable spatial and temporal resolution while maintaining a clinically acceptable image quality. Validation tools, including numerical simulations, are needed to characterize the repeatability and reproducibility of such methods for use in quantitative imaging applications. We describe the development of a simulation framework for analyzing and optimizing accelerated MRI acquisition and reconstruction techniques used in dynamic contrast enhanced (DCE) breast imaging. The simulation framework, in the form of a digital reference object (DRO), consists of four modules that control different aspects of the simulation, including the appearance and physiological behavior of the breast tissue as well as the MRI acquisition settings, to produce simulated k-space data for a DCE breast exam. The DRO design and functionality are described along with simulation examples provided to show potential applications of the DRO. The included simulation results demonstrate the ability of the DRO to simulate a variety of effects including the creation of simulated lesions, tissue enhancement modeled by the generalized kinetic model, T1-relaxation, fat signal precession and saturation, acquisition SNR, and changes in temporal resolution.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BackgroundDiffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.PurposeTo compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.Study TypeProspective.PopulationThirty‐three patients prospectively imaged prior to prostatectomy.Field Strength/Sequence3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence.AssessmentDatasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).Statistical TestLevene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.ResultsThe DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72–0.76, 0.76–0.81, and 0.76–0.80 respectively) as compared to bi‐exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53–0.80, 0.51–0.81, and 0.52–0.80 respectively). Post‐processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.Data ConclusionWe found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post‐processing decisions on DWI data can affect sensitivity and specificity when applied to radiological–pathological studies in prostate cancer.Level of Evidence1Technical EfficacyStage 3

Image intensity histograms as imaging biomarkers: application to immune-related colitis

Purpose. To investigate image intensity histograms as a potential source of useful imaging biomarkers in both a clinical example of detecting immune-related colitis (irColitis) in 18F-FDG PET/CT images of immunotherapy patients and an idealized case of classifying digital reference objects (DRO). Methods. Retrospective analysis of bowel 18F-FDG uptake in N = 40 patients receiving immune checkpoint inhibitors was conducted. A CNN trained to segment the bowel was used to generate the histogram of bowel 18F-FDG uptake, and percentiles of the histogram were considered as potential metrics for detecting inflammation associated with irColitis. A model of the colon was also considered using cylindrical DRO. Classification of DRO with different intensity distributions was undertaken under varying geometry and noise settings. Results. The most predictive biomarker of irColitis was the 95th percentile of the bowel SUV histogram (SUV95%). Patients later diagnosed with irColitis had a significantly higher increase in SUV95% from baseline to first on-treatment PET than patients who did not experience irColitis (p = 0.02). An increase in SUV95%> + 40% separated pre-irColitis change from normal variability with a sensitivity of 75% and specificity of 88%. Furthermore, histogram percentiles were ideal metrics for classifying ‘hot center’ and ‘cold center’ DRO, and were robust to varying DRO geometry and noise, and to the presence of spoiler volumes unrelated to the detection task. Conclusions. The 95th percentile of the bowel SUV histogram was the optimal metric for detecting irColitis on 18F-FDG PET/CT. Image intensity histograms are a promising source of imaging biomarkers for clinical tasks.

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation.

Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation. Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

Motion correction of free-breathing magnetic resonance renography using model-driven registration

IntroductionModel-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR).Materials and methodsMDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data).ResultsDRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses.DiscussionMDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.

Sparse precontrast T1 mapping for high-resolution whole-brain DCE-MRI.

PurposeTo develop and evaluate an efficient precontrast T1 mapping technique suitable for quantitative high‐resolution whole‐brain dynamic contrast‐enhanced–magnetic resonance imaging (DCE‐MRI).MethodsVariable flip angle (VFA) T1 mapping was considered that provides 1 × 1 × 2 mm3 resolution to match a recent high‐resolution whole‐brain DCE‐MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T1 and M0 maps were estimated using model‐based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise‐mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high‐grade gliomas.ResultsT1‐mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T1 bias was <25 ms in white matter (WM) and <11 ms in brain tumor (BT). T1 standard deviation (SD) was <119.5 ms in WM (coefficient of variation [COV] ~11.0%) and <253.2 ms in BT (COV ~12.7%). In the noisy DRO, T1 bias was <50 ms in WM and <30 ms in BT. For R ≤ 10, T1 SD was <107.1 ms in WM (COV ~9.9%) and <240.9 ms in BT (COV ~12.1%). In the healthy subject, T1 bias was <30 ms for R ≤ 16. At R = 4, T1 SD was 171.4 ms (COV ~13.0%). In the prospective brain tumor study, T1 values were consistent with literature values in WM and BT.ConclusionHigh‐resolution whole‐brain VFA T1 mapping is feasible with sparse sampling, supporting its use for quantitative DCE‐MRI.

A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.

Validación de un algoritmo de obtención de biomarcadores de imágenes dinámicas de perfusión en tomografía computarizada

A partir del análisis de las imágenes dinámicas de perfusión de contraste en tomografía computarizada, se pueden obtener parámetros que miden características fisiológicas relacionadas con la permeabilidad de los tejidos. Puesto que no existen elementos patrón con los que realizar calibraciones de los conjuntos de herramientas de adquisición y procesado, es necesario hacer una validación de los resultados obtenidos con el algoritmo aplicado a imágenes sintéticas. En este texto se estudia el desempeño en condiciones ideales y en presencia de ruido de un algoritmo de ajuste no lineal para la obtención de parámetros del modelo de Tofts extendido para imágenes de perfusión dinámica. En las pruebas se utiliza como fuente de datos unas imágenes sintéticas formadas a partir de valores conocidos de los parámetros del modelo.