Comparison of voxel-wise and histogram analyses of glioma ADC maps for prediction of early therapeutic change

Noninvasive imaging methods are sought to objectively predict early response to therapy for high-grade glioma tumors. Quantitative metrics derived from diffusion-weighted imaging, such as apparent diffusion coefficient (ADC), have previously shown promise when used in combination with voxel-based analysis reflecting regional changes. The functional diffusion mapping (fDM) metric is hypothesized to be associated with volume of tumor exhibiting an increasing ADC owing to effective therapeutic action. In this work, the reference fDM-predicted survival (from previous study) for 3 weeks from treatment initiation (midtreatment) is compared to multiple histogram-based metrics using Kaplan-Meier estimator for 80 glioma patients stratified to responders and nonresponders based on the population median value for the given metric. The ADC histogram metric reflecting reduction in midtreatment volume of solid tumor (ADC 8% population-median with respect to pretreatment is found to have the same predictive power as the reference fDM of increasing midtreatment ADC volume above 4%. This study establishes the level of correlation between fDM increase and low-ADC tumor volume shrinkage for prediction of early response to radiation therapy in patients with glioma malignancies

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition

Purpose: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. // Materials and Methods: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016–October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. // Results: The mean lesion ADC for b0 b1500 had the lowest wSD in most zones (0.18–0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90–10.24x10-4 mm2/s). // Conclusion: ADC maps generated from b0 b1500 have better repeatability in normal TZ, normal PZ, and index lesions

Toward Uniform Implementation Of Parametric Map Digital Imaging And Communication In Medicine Standard In Multisite Quantitative Diffusion Imaging Studies

This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation

Retrospective Correction of ADC for Gradient Nonlinearity Errors in Multicenter Breast DWI Trials: ACRIN6698 Multiplatform Feasibility Study.

The presented analysis of multisite, multiplatform clinical oncology trial data sought to enhance quantitative utility of the apparent diffusion coefficient (ADC) metric, derived from diffusion-weighted magnetic resonance imaging, by reducing technical interplatform variability owing to systematic gradient nonlinearity (GNL). This study tested the feasibility and effectiveness of a retrospective GNL correction (GNC) implementation for quantitative quality control phantom data, as well as in a representative subset of 60 subjects from the ACRIN 6698 breast cancer therapy response trial who were scanned on 6 different gradient systems. The GNL ADC correction based on a previously developed formalism was applied to trace-DWI using system-specific gradient-channel fields derived from vendor-provided spherical harmonic tables. For quantitative DWI phantom images acquired in typical breast imaging positions, the GNC improved interplatform accuracy from a median of 6% down to 0.5% and reproducibility of 11% down to 2.5%. Across studied trial subjects, GNC increased low ADC (<1 µm2/ms) tumor volume by 16% and histogram percentiles by 5%–8%, uniformly shifting percentile-dependent ADC thresholds by ∼0.06 µm2/ms. This feasibility study lays the grounds for retrospective GNC implementation in multiplatform clinical imaging trials to improve accuracy and reproducibility of ADC metrics used for breast cancer treatment response prediction

Apparent Diffusion Coefficient Measurement of Myelofibrosis in Mouse Tibia

The goal of this Co-Clinical Imaging Research Program (CIRP) pre-clinical imaging protocol (PIP) is to provide detailed description of key steps used to achieve a stated level of technical repeatability (precision) embodied in “Claims”, for MRI measurement of apparent diffusion coefficient (ADC) in tibia bone marrow of myelofibrosis mouse models. This pre-clinical imaging procedure document will be referred to as a “profile” and adheres to a PIP MRI template provided in: https://www.protocols.io/workspaces/pre-clinical-imaging-protocols. This profile details procedures for ADC measurement from diffusion weighted imaging (DWI) acquisition and image processing in MF mouse tibia to achieve stated performance claims. Tibia bone marrow composition in MF mouse models has gradation going from proximal to distil ends of the tibia, therefore separate claims are made for volume of interest (VOI) analysis of ADC maps for each of three distinct sections along the length of the tibia (see Figure 1): Section 1 (proximal)º VOI (~4-5mm3) within 9mm of proximal end of tibia Section 2 (transition)º VOI (~0.4-0.5mm3) from 10 to 12mm of proximal end of tibia Section 3 (distil)º VOI (~0.1-0.2mm3) from 13 to 14mm of proximal end of tibia Claim 1:A measured change in the mean ADC in Section 1 VOI of MF mouse model tibia that exceeds ±0.037µm2/ms indicates a true biological change has occurred in the tibia bone marrow with 95% confidence. Claim 2:A measured change in the mean ADC in Section 2 VOI of MF mouse model tibia that exceeds ±0.087µm2/ms indicates a true biological change has occurred in the tibia bone marrow with 95% confidence Claim 3:A measured change in the mean ADC in Section 3 VOI of MF mouse model tibia that exceeds ±0.030µm2/ms indicates a true biological change has occurred in the tibia bone marrow with 95% confidencehttp://deepblue.lib.umich.edu/bitstream/2027.42/175837/2/UMich_ADC_SOP-Profile_20230214.pdfPublished versionDescription of UMich_ADC_SOP-Profile_20230214.pdf : Published versio

Correction of Gradient Nonlinearity Bias in Quantitative Diffusion Parameters of Renal Tissue with Intravoxel Incoherent Motion

Spatially nonuniform diffusion weighting bias as a result of gradient nonlinearity (GNL) causes substantial errors in apparent diffusion coefficient (ADC) maps for anatomical regions imaged distant from the magnet isocenter. Our previously described approach effectively removed spatial ADC bias from 3 orthogonal diffusion-weighted imaging (DWI) measurements for monoexponential media of arbitrary anisotropy. This work evaluates correction feasibility and performance for quantitative diffusion parameters of the 2-component intravoxel incoherent motion (IVIM) model for well-perfused and nearly isotropic renal tissue. Sagittal kidney DWI scans of a volunteer were performed on a clinical 3T magnetic resonance imaging scanner near isocenter and offset superiorly. Spatially nonuniform diffusion weighting caused by GNL resulted both in shifting and broadening of perfusion-suppressed ADC histograms for off-center DWI relative to unbiased measurements close to the isocenter. Direction-average diffusion weighting bias correctors were computed based on the known gradient design provided by the vendor. The computed bias maps were empirically confirmed by coronal DWI measurements for an isotropic gel-flood phantom. Both phantom and renal tissue ADC bias for off-center measurements was effectively removed by applying precomputed 3D correction maps. Comparable ADC accuracy was achieved for corrections of both b maps and DWI intensities in the presence of IVIM perfusion. No significant bias impact was observed for the IVIM perfusion fraction. INTRODUCTION Recent multicenter oncology trials have evaluated quantitative diffusion-weighted imaging (DWI) as a radiological marker of tumor malignancy and response to therapy (1-3). The underlying physical principle for this technology is that oncogenic processes and therapeutic interventions induce regional changes in cellularity of the imaged tissue that can be detected and quantified by mean (isotropic) diffusivity (4, 5). In clinical applications outside of the brain, tissues with low fractional anisotropy are typically assessed by combining 3 orthogonal DWI acquisitions as a function of diffusion gradient weighting, quantified by a b-value to provide a mean diffusivity measure of the tissue. The optimal number of acquired b-values depends on the diffusion model utilized to appropriately characterize tissue diffusivity (6-8). The default measure of mean diffusivity in current clinical trials is the apparent diffusion coefficient (ADC), which assumes monoexponential signal decay with increasing b-values (4