Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion‐weighted imaging and dynamic contrast‐enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion‐weighted imaging and dynamic contrast‐enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101–e121.

Purpose Quantitative quality control procedures were sought to evaluate technical variability in multi-center measurements of the diffusion coefficient of water as a prerequisite to use of the biomarker apparent diffusion coefficient (ADC) in multi-center clinical trials. Materials and Methods A uniform data acquisition protocol was developed and shared with 18 participating test sites along with a temperature-controlled diffusion phantom delivered to each site. Usable diffusion weighted imaging data of ice water at 5 b-values were collected on 35 clinical MRI systems from 3 vendors at 2 field strengths (1.5 and 3T) and analyzed at a central processing site. Results Standard deviation of bore-center ADCs measured across 35 scanners was <2%; error range: −2% to +5% from literature value. Day-to-day repeatability of the measurements was within 4.5%. Intra-exam repeatability at the phantom center was within 1%. Excluding one outlier, inter-site reproducibility of ADC at magnet isocenter was within 3%, though variability increased for off-center measurements. Significant (>10%) vendor-specific and system-specific spatial non-uniformity ADC bias was detected for the off-center measurement that was consistent with gradient non-linearity. Conclusion Standardization of DWI protocol has improved reproducibility of ADC measurements and allowed identifying spatial ADC non-uniformity as a source of error in multi-site clinical studies.

Purpose Characterize system-specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials. Methods Diffusion weighted imaging (DWI) was performed on an ice-water phantom along the superior-inferior (SI) and right-left (RL) orientations spanning ±150 mm. The same scanning protocol was implemented on 14 MRI systems at seven imaging centers. The bias was estimated as a deviation of measured from known apparent diffusion coefficient (ADC) along individual DWI directions. The relative contributions of gradient nonlinearity, shim errors, imaging gradients and eddy currents were assessed independently. The observed bias errors were compared to numerical models. Results The measured systematic ADC errors scaled quadratically with offset from isocenter, and ranged between −55% (SI) and 25% (RL). Nonlinearity bias was dependent on system design and diffusion gradient direction. Consistent with numerical models, minor ADC errors (±5%) due to shim, imaging and eddy currents were mitigated by double echo DWI and image co-registration of individual gradient directions. Conclusion The analysis confirms gradient nonlinearity as a major source of spatial DW bias and variability in off-center ADC measurements across MRI platforms, with minor contributions from shim, imaging gradients and eddy currents. The developed protocol enables empiric description of systematic bias in multicenter quantitative DWI studies.

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Purpose Gradient nonlinearity of MRI systems leads to spatially-dependent b-values and consequently high non-uniformity errors (10–20%) in ADC measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. Methods All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. Results Spatial dependence of nonlinearity correction terms accounts for the bulk (75–95%) of ADC bias for FA = 0.3–0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. Conclusions The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients.