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This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis. A diffusion-weighted imaging (DWI) protocol (b-value: 0-200-400-600-800-1000 s mm -2 ) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function ( ar 2 + br + c ). Moreover, for each Δ-profile, the maximum minus minimum of the fitting function ( δ max ) was calculated. The parameters a , b , c , and δ max showed some significant variations between scanner systems by different manufacturers or with different static magnetic field strengths, as well as between different acquisition/estimation setups. Unsupervised clustering analysis showed two evident clusters with significantly different values of parameter a ( p < 0.0001), which can be grouped by acquisition protocol/Δ-profile direction but not scanner system. The results of ∆-profiles confirm an appreciable inter-scanner variability in ADC measurement and corroborate the importance of guarantying the reliability of ADC estimations in clinical or research studies, considering for each scanner system the specific acquisition sequence in terms of acquisition plan orientation and diffusion weighting gradient direction., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Purpose: The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation., Methods: Water phantom acquisitions were carried out on 39 scanners. DWI acquisitions (b-value = 0-200-400-600-800-1000 s/mm 2 ) with different acquisition plans (axial, coronal, sagittal) and phase encoding directions (anterior/posterior and right/left, for the axial acquisition plan), for 3 orthogonal diffusion weighting gradient directions, were performed. For each acquisition setup, ADC values were measured in-center and off-center (6 different positions), resulting in an entire dataset of 84 × 39 = 3276 ADC values. Spatial uniformity of ADC maps was assessed by means of the percentage difference between off-center and in-center ADC values (Δ)., Results: No significant dependence of in-center ADC values on acquisition plan/phase encoding direction was found. Ward unsupervised clustering analysis showed 3 distinct clusters of scanners and an association between Δ-values and manufacturer/model, whereas no association between Δ-values and maximum gradient strength, slew rate or static magnetic field strength was revealed. Several acquisition setups showed significant differences among groups, indicating the introduction of different biases in ADC estimation., Conclusions: Unsupervised clustering analysis of DWI data, obtained from several scanners using an extensive acquisition protocol, allows to reveal an association between measured ADC values and manufacturer/model of scanner, as well as to identify suboptimal DWI acquisition setups for accurate ADC estimation., (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Purpose: To propose an MRI quality assurance procedure that can be used for routine controls and multi-centre comparison of different MR-scanners for quantitative diffusion-weighted imaging (DWI)., Materials and Methods: 44 MR-scanners with different field strengths (1 T, 1.5 T and 3 T) were included in the study. DWI acquisitions (b-value range 0-1000 s/mm 2 ), with three different orthogonal diffusion gradient directions, were performed for each MR-scanner. All DWI acquisitions were performed by using a standard spherical plastic doped water phantom. Phantom solution ADC value and its dependence with temperature was measured using a DOSY sequence on a 600 MHz NMR spectrometer. Apparent diffusion coefficient (ADC) along each diffusion gradient direction and mean ADC were estimated, both at magnet isocentre and in six different position 50 mm away from isocentre, along positive and negative AP, RL and HF directions., Results: A good agreement was found between the nominal and measured mean ADC at isocentre: more than 90% of mean ADC measurements were within 5% from the nominal value, and the highest deviation was 11.3%. Away from isocentre, the effect of the diffusion gradient direction on ADC estimation was larger than 5% in 47% of included scanners and a spatial non uniformity larger than 5% was reported in 13% of centres., Conclusion: ADC accuracy and spatial uniformity can vary appreciably depending on MR scanner model, sequence implementation (i.e. gradient diffusion direction) and hardware characteristics. The DWI quality assurance protocol proposed in this study can be employed in order to assess the accuracy and spatial uniformity of estimated ADC values, in single- as well as multi-centre studies., (Copyright © 2018. Published by Elsevier Ltd.)

Purpose: To propose a magnetic resonance imaging (MRI) quality assurance procedure that can be used for multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging (DWI)., Materials and Methods: Twenty-six centers (35 MR scanners with field strengths: 1T, 1.5T, and 3T) were enrolled in the study. Two different DWI acquisition series (b-value ranges 0-1000 and 0-3000 s/mm(2) , respectively) were performed for each MR scanner. All DWI acquisitions were performed by using a cylindrical doped water phantom. Mean apparent diffusion coefficient (ADC) values as well as ADC values along each of the three main orthogonal directions of the diffusion gradients (x, y, and z) were calculated. Short-term repeatability of ADC measurement was evaluated for 26 MR scanners., Results: A good agreement was found between the nominal and measured mean ADC over all the centers. More than 80% of mean ADC measurements were within 5% from the nominal value, and the highest deviation and overall standard deviation were 9.3% and 3.5%, respectively. Short-term repeatability of ADC measurement was found <2.5% for all MR scanners., Conclusion: A specific and widely accepted protocol for quality controls in DWI is still lacking. The DWI quality assurance protocol proposed in this study can be applied in order to assess the reliability of DWI-derived indices before tackling single- as well as multicenter studies., (© 2015 Wiley Periodicals, Inc.)

Objectives: To prospectively verify, in vivo, Le Bihan's model of signal decay in magnetic resonance/diffusion-weighted imaging (intravoxel incoherent motion) in healthy liver parenchyma., Methods: Informed consent and institutional board approval were obtained. To measure both underfasting and postprandial conditions, apparent, slow, and fast diffusion (D*) coefficients and perfusion fraction of liver parenchyma, 40 healthy volunteers (19 women and 21 men) underwent a 3.0-T magnetic resonance imaging examination, including portal venous flow measurements by a 2-dimensional phase-contrast sequence, and multi-b diffusion-weighted imaging acquired before and 30 minutes after a 600-Kcal meal. Parameters were measured by fitting procedure with regions of interest drawn on the right liver lobe. Paired-sample t test was performed to search for any statistically significant difference between preprandial and postprandial values of each parameter and of portal flow. Pearson correlation coefficients were calculated to evaluate the relationship between portal flow increase and diffusion-weighted imaging parameter changes in postprandial conditions. Interobserver agreement for measurement of the intravoxel incoherent motion parameters was determined, both for preprandial and postprandial values., Results: Mean increase in postprandial portal flow was 98% (P < 0.0009). The t test did not show any statistically significant difference between the preprandial and postprandial values for apparent, slow diffusion coefficients and perfusion fraction (P ≥ 0.05), whereas a statistically significant postprandial increase (P < 0.01) of D* was detected. Correlation with portal venous flow increase at Pearson test was statistically significant for D* (P = 0.04) and nonsignificant for the other parameters. All the parameters showed wide variability, with a higher percent coefficient of variation for D*. Interobserver agreement was always greater than 0.70., Conclusions: This study verifies Le Bihan's theory, confirming that in the liver, D* is influenced by perfusional changes related to portal venous flow.

Purpose: To evaluate the dependence on the b-values adopted of apparent diffusion coefficient (ADC), perfusion fraction (PF), slow and fast diffusion coefficient (Dslow, Dfast), corrected diffusion coefficient (D) and kurtosis (K), in healthy peripheral (HP) and peripheral cancerous (PCa) prostate tissues., Materials and Methods: Patients who underwent multiparametric prostate MR examination were retrospectively evaluated for possible inclusion. ADC, PF, Dslow, Dfast, D, and K were estimated both in HP and PCa tissues, using three different ranges of b-values: 0-2300, 0-1800, 0-800 s/mm2 (group A, B and C, respectively). Analysis of variance (ANOVA) and receiver operating characteristic (ROC) analysis were performed, to establish differences among groups and to evaluate sensitivity and specificity of every parameter in distinguishing HP and PCa tissues when calculated with different b-values., Results: In all, 57 patients were included. ANOVA showed significant differences of all parameters between group A-B vs. C, both in HP and PCa tissues. In ROC analysis K showed the best area under the curve (AUC) when calculated in groups A and B (0.87 and 0.86), while it was comparable with the ADC one in group C (both 0.82)., Conclusion: A significant dependence on the adopted b-values of DWI parameters is shown. The best performance in distinguishing HP from PCa tissues was obtained by K, calculated using a high b-value sequence., (Copyright © 2013 Wiley Periodicals, Inc.)