Yuxin Zhang

Purpose: To evaluate feasibility and reproducibility of liver diffusion‐weighted (DW) MRI using cardiac‐motion‐robust, blood‐suppressed, reduced‐distortion techniques.Methods: DW‐MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion‐robust (M1‐nulled, and M1‐optimized) DW gradient waveforms were each acquired with single‐shot echo‐planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion‐affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross‐correlation coefficient, with an undistorted T2‐weighted reference.Results: In the motion phantom, ADC mean and SD in motion‐affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion‐robust waveforms. M1‐optimized waveforms suppressed slow flow signal present with M1‐nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes (, reproducibility coefficients (RPC) = mm/s for monopolar‐msEPI), while M1‐optimized waveforms showed more reproducible ADC values (, mm/s for M1‐optimized‐msEPI). In phantom and healthy volunteer studies, motion‐robust acquisitions with msEPI showed significantly reduced image distortion () compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1‐optimized waveforms with msEPI.Conclusion: Synergistic effects of combined M1‐optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

Purpose: To design a b value and first‐order motion moment (M₁) optimized data acquisition for repeatable intravoxel incoherent motion (IVIM) quantification in the liver.Methods: Cramer‐Rao lower bound optimization was performed to determine optimal monopolar and optimal 2D samplings of the b‐M₁ space based on noise performance. Monte Carlo simulations were used to evaluate the bias and variability in estimates obtained using the proposed optimal samplings and conventional monopolar sampling. Diffusion MRI of the liver was performed in 10 volunteers using 3 IVIM acquisitions: conventional monopolar, optimized monopolar, and b‐M₁‐optimized gradient waveforms (designed based on the optimal 2D sampling). IVIM parameter maps of diffusion coefficient, perfusion fraction, and blood velocity SD were obtained using nonlinear least squares fitting. Noise performance (SDs), stability (outlier percentage), and test–retest or scan–rescan repeatability (intraclass correlation coefficients) were evaluated and compared across acquisitions.Results: Cramer‐Rao lower bound and Monte Carlo simulations demonstrated improved noise performance of the optimal 2D sampling in comparison to monopolar samplings. Evaluating the designed b‐M₁‐optimized waveforms in healthy volunteers, significant decreases (p < 0.05) in the SDs and outlier percentages were observed for measurements of diffusion coefficient, perfusion fraction, and blood velocity SD in comparison to measurements obtained using monopolar samplings. Good‐to‐excellent repeatability (intraclass correlation coefficients ≥ 0.77) was observed for all 3 parameters in both the right and left liver lobes using the b‐M₁‐optimized waveforms.Conclusions: 2D b‐M₁‐optimized data acquisition enables repeatable IVIM quantification with improved noise performance. 2D acquisitions may advance the establishment of IVIM quantitative biomarkers for liver diseases.

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton‐density fat fraction, and cardiacT₁. These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.

Purpose: To assess the effects of cardiovascular‐induced motion on conventional DWI of the pancreas and to evaluate motion‐robust DWI methods in a motion phantom and healthy volunteers.Methods: 3T DWI was acquired using standard monopolar and motion‐compensated gradient waveforms, including in an anatomically accurate pancreas phantom with controllable compressive motion and healthy volunteers (n = 8, 10). In volunteers, highly controlled single‐slice DWI using breath‐holding and cardiac gating and whole‐pancreas respiratory‐triggered DWI were acquired. For each acquisition, the ADC variability across volunteers, as well as ADC differences across parts of the pancreas were evaluated.Results: In motion phantom scans, conventional DWI led to biased ADC, whereas motion‐compensated waveforms produced consistent ADC. In the breath‐held, cardiac‐triggered study, conventional DWI led to heterogeneous DW signals and highly variable ADC across the pancreas, whereas motion‐compensated DWI avoided these artifacts. In the respiratory‐triggered study, conventional DWI produced heterogeneous ADC across the pancreas (head: 1756 ± 173 × 10⁻⁶ mm²/s; body: 1530 ± 338 × 10⁻⁶ mm²/s; tail: 1388 ± 267 × 10⁻⁶ mm²/s), with ADCs in the head significantly higher than in the tail (P < .05). Motion‐compensated ADC had lower variability across volunteers (head: 1277 ± 102 × 10⁻⁶ mm²/s; body: 1204 ± 169 × 10⁻⁶ mm²/s; tail: 1235 ± 178 × 10⁻⁶ mm²/s), with no significant difference (P ≥ .19) across the pancreas.Conclusion: Cardiovascular motion introduces artifacts and ADC bias in pancreas DWI, which are addressed by motion‐robust DWI.

Purpose: Chemical shift encoded (CSE)‐MRI enables quantification of proton‐density fat fraction (PDFF) as a biomarker of liver fat content. However, conventional 3D Cartesian CSE‐MRI methods require breath‐holding. A motion‐robust 2D Cartesian sequential method addresses this limitation but suffers from low SNR. In this work, a novel free breathing 2D Cartesian sequential CSE‐MRI method using a variable flip angle approach with centric phase encoding (VFA‐centric) is developed to achieve fat quantification with low bias, high SNR, and minimal blurring.Methods: Numerical simulation was performed for variable flip angle schedule design and preliminary evaluation of VFA‐centric method, along with several alternative flip angle designs. Phantom, adults (n = 8), and children (n = 27) were imaged at 3T. Multi‐echo images were acquired and PDFF maps were estimated. PDFF standard deviation was used as a surrogate for SNR.Results: In both simulation and phantom experiments, the VFA‐centric method enabled higher SNR imaging with minimal bias and blurring artifacts. High correlation (slope = 1.00, intercept = 0.04, = 0.998) was observed in vivo between the proposed VFA‐centric method obtained PDFF and reference PDFF (free breathing low‐flip angle 2D sequential acquisition). Further, the proposed VFA‐centric method (PDFF standard deviation = 1.5%) had a better SNR performance than the reference acquisition (PDFF standard deviation = 3.3%) with P < .001.Conclusions: The proposed free breathing 2D Cartesian sequential CSE‐MRI method with variable flip angle approach and centric‐ordered phase encoding achieved motion robustness, low bias, high SNR compared to previous 2D sequential methods, and low blurring in liver fat quantification.

Purpose: To develop motion‐robust, blood‐suppressed diffusion‐weighted imaging (DWI) of the liver with optimized diffusion encoding waveforms and evaluate the accuracy and reproducibility of quantitative apparent diffusion coefficient (ADC) measurements.Methods: A novel approach for the design of diffusion weighting waveforms, termed M1‐optimized diffusion imaging (MODI), is proposed. MODI includes an echo time‐optimized motion‐robust diffusion weighting gradient waveform design, with a small nonzero first‐moment motion sensitivity (M1) value to enable blood signal suppression. Experiments were performed in eight healthy volunteers and five patient volunteers. In each case, DW images and ADC maps were compared between acquisitions using standard monopolar waveforms, motion moment‐nulled (M1‐nulled and M1–M2‐nulled) waveforms, and the proposed MODI approach.Results: Healthy volunteer experiments using MODI showed no significant ADC bias in the left lobe relative to the right lobe (p < .05) demonstrating robustness to cardiac motion, and no significant ADC bias with respect to monopolar‐based ADC measured in the right lobe (p < .05), demonstrating blood signal suppression. In contrast, monopolar‐based ADC showed significant bias in the left lobe relative to the right lobe (p < .01) due to its sensitivity to motion, and both M1‐nulled and M1–M2‐nulled‐based ADC showed significant bias (p < .01) due to the lack of blood suppression. Preliminary patient results also suggest MODI may enable improved visualization and quantitative assessment of lesions throughout the entire liver.Conclusions: This novel method for diffusion gradient waveform design enables DWI of the liver with high robustness to motion and suppression of blood signals, overcoming the limitations of conventional monopolar waveforms and moment‐nulled waveforms, respectively.

Purpose: To present a stimulated‐echo based mapping (STEM) approach for simultaneous T₁, T₂, and ADC mapping.Methods: Diffusion‐weighted stimulated‐echo images with various combinations of mixing time (TM), TE, and b‐value were acquired to enable simultaneous mapping of T₁, T₂, and ADC. The proposed STEM method was performed by densely sampling the TM‐TE‐b space in a phantom and in brain and prostate of healthy volunteers. T₁, T₂, and ADC from STEM were compared to reference mapping methods. Additionally, protocol optimization was performed to enable rapid STEM acquisition within 2 min by sparsely sampling the TM‐TE‐b space. The T₁, T₂, and ADC measurements from rapid acquisitions were compared to the densely sampled STEM for evaluation. Finally, a patient with biopsy‐proven high‐risk prostate cancer was imaged to demonstrate the ability of STEM to differentiate cancer and healthy tissues.Results: Relative to the reference measurements, densely sampled STEM provided accurate quantitative T₁, T₂, and ADC mapping in phantoms (R² = 0.999, slope between 0.97–1.03), as well as in brain and prostate. Further, the T₁, T₂, and ADC measurements from the optimized rapid STEM acquisitions agreed closely with densely sampled STEM. Finally, STEM showed decreased T₂ and ADC in prostate cancer compared to healthy prostate tissue.Conclusion: STEM provides accurate simultaneous mapping of T₁, T₂, and ADC. This method may enable rapid and accurate multi‐parametric tissue characterization for clinical and research applications.