Improved repeatability of dynamic contrast‐enhanced MRI using the complex MRI signal to derive arterial input functions: a test‐retest study in prostate cancer patients

Klawer, Edzo M.E.; Houdt, Petra J. van; Simonis, F.F.J.; Berg, Cornelis A.T. van den; Pos, Floris J.; Heijmink, Stijn W. T. P. J.; Isebaert, Sofie; Haustermans, Karin; Heide, Uulke A. van der

doi:10.1002/mrm.27646

Cited by 9 publications

(7 citation statements)

References 53 publications

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“…Instead using the combination of the magnitude and phase data, i.e. complex data, will result in more accurate arterial input functions [36,37]. However, not all institutes were able to save the phase data due to scanner software limitations, consequently a population or reference AIF will be used in the analysis of the patient data.…”

Section: Discussionmentioning

confidence: 99%

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer

Houdt

Kallehauge

Tanderup

et al. 2020

Radiotherapy and Oncology

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Background and purpose: A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. Materials and methods: Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE-)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. Results: The institutional protocols showed a range in bias of 0.88-0.98 for T2 (median %RC = 1%; % RDC = 12%), À0.007 to 0.029 Â 10 À3 mm 2 /s for the apparent diffusion coefficient (median %RC = 3%; % RDC = 18%), and 0.39-1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. Conclusion: We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE-MRI and T1 mapping with the variable flip angle method, this was more challenging.

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Section: Discussionmentioning

confidence: 99%

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer

Houdt

Kallehauge

Tanderup

et al. 2020

Radiotherapy and Oncology

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“…The final VIF was jointly estimated (by Y.B.) from the magnitude of image voxels within the VIF ROI ( 22 ). Baseline proton density and T1 maps were estimated at matching spatial resolution and coverage from sparsely sampled B 1 -corrected variable flip-angle T1 mapping (this step was performed by R.M.L., an MR scientist with 10 years of experience in brain DCE MRI) ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Bliesener¹,

Lebel²,

Acharya³

et al. 2021

Radiology

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D ynamic contrast-enhanced (DCE) MRI enables assessment of neurovascular parameters by monitoring enhancement patterns in tissue after injection of contrast agent. Such parameters could provide markers for tumor grading in patients with high-grade glioma (1,2) and for early brain tumor response to antiangiogenic therapy (3). Brain lesions commonly exhibit large regions of spatial heterogeneity or thinly enhancing rims around necrotic cores; in addition, multifocal metastases may be visible throughout the entire brain (4). Effective markers therefore require high-spatiotemporal-resolution whole-brain DCE MRI protocols, as well as accurate and reproducible tracer kinetic parameters (3,5,6).Many attempts have been made to achieve desired spatial resolution and coverage while preserving rapid sampling of the temporal evolution of contrast agent dynamics. Although direct estimation of tracer-kinetic parameters from undersampled MRI raw data exists (7,8), more commonly, constrained reconstruction algorithms reconstruct the image time series in an intermediate step (9-11) or simultaneously to tracer-kinetic estimation (12). This can be advantageous because it offers great flexibility in the estimation of nuisance parameters, such as the vascular input function (VIF), which is commonly done on the basis of image data.Extensive efforts by the Radiological Society of North America Quantitative Imaging Biomarkers Alliance DCE MRI task force and other groups have resulted in standardization and characterization of Nyquist-sampled DCE MRI (13). Although much research has been devoted to obtaining 20-to 50-fold undersampled parallel imaging Background: Advances in sub-Nyquist-sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility.Purpose: To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma. Materials and Methods:Two methods for sub-Nyquist-sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M 0 maps. Reproducibility was estimated in a retrospective study in participants with high-grade glioma, who underwent three consecutive standard-of-care examinations between December 2016 and April 2019. Coefficients of variation and reproducibility coefficients were reported for histogram statistics of the tracer kinetic parameters plasma volume fraction and volume transfer constant (K trans ) on five healthy tissue types. Results:The images from 13 participants (mean ag...

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“…The standard Tofts model was applied to estimate K trans using a deconvolution implementation . As input for the model we used patient‐specific arterial input functions derived from the magnitude data for the 1.5T system, whereas the arterial input functions were derived from the complex data for the data from the 3T systems . All quantitative maps were rigidly registered to the axial T 2 W image.…”

Section: Methodsmentioning

confidence: 99%

“…19 As input for the model we used patient-specific arterial input functions derived from the magnitude data for the 1.5T system, whereas the arterial input functions were derived from the complex data for the data from the 3T systems. 20 All quantitative maps were rigidly registered to the axial T 2 W image. When scanned with an endorectal coil, the T 2 W image with endorectal coil was deformable registered to the T 2 W image without endorectal coil.…”

Section: Imagingmentioning

confidence: 99%

Histopathological Features of MRI‐Invisible Regions of Prostate Cancer Lesions

Houdt

Ghobadi

Schoots

et al. 2019

Magnetic Resonance Imaging

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Background Previous studies have reported tumor volume underestimation with multiparametric (mp)MRI in prostate cancer diagnosis. Purpose To investigate why some parts of lesions are not visible on mpMRI by comparing their histopathology features to those of visible regions. Study Type Retrospective. Population Thirty‐four patients with biopsy‐proven prostate cancer scheduled for prostatectomy (median 68.7 years). Field Strength/Sequence T2‐weighted, diffusion‐weighted imaging, T2 mapping, and dynamic contrast‐enhanced MRI on two 3T systems and one 1.5T system. Assessment Two readers delineated suspicious lesions on mpMRI. A pathologist delineated the lesions on histopathology. A patient‐customized mold enabled the registration of histopathology and MRI. On histopathology we identified mpMRI visible and invisible lesions. Subsequently, within the visible lesions we identified regions that were visible and regions that were invisible on mpMRI. For each lesion and region the following characteristics were determined: size, location, International Society of Urological Pathology (ISUP) grade, and Gleason subpatterns (density [dense/intermediate], tumor morphology [homogeneous/heterogeneous], cribriform growth [yes/no]). Statistical Tests With generalized linear mixed‐effect modeling we investigated which features explain why a lesion or a region was invisible on MRI. We compared imaging values (T2, ADC, and Ktrans) for these features with one‐way analysis of variance (ANOVA). Results Small, anterior, and ISUP grade 1–2 lesions (n = 34) were missed more frequent than large, posterior, ISUP grade ≥ 3 lesions (n = 35). Invisible regions on mpMRI had lower tumor density, heterogeneous tumor morphology, and were located in the transition zone. Both T2 and ADC values were higher in "intermediate" compared with "dense" regions (P = 0.002 and < 0.001) and in regions with heterogeneous compared with homogeneous morphology (P < 0.001 and 0.03). Ktrans was not significantly different (P = 0.24 and 0.99). Data Conclusion Regions of prostate cancer lesions that are invisible on mpMRI have different histopathology features than visible regions. This may have implications for monitoring during active surveillance and focal treatment strategies. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1235–1246.

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Improved repeatability of dynamic contrast‐enhanced MRI using the complex MRI signal to derive arterial input functions: a test‐retest study in prostate cancer patients

Cited by 9 publications

References 53 publications

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Histopathological Features of MRI‐Invisible Regions of Prostate Cancer Lesions

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