Diego Hernando

Purpose: To evaluate the feasibility of detecting prandial (i.e., meal) induced changes in microvascular flow in the liver, including changes in the spatial distribution of flow velocities, using intravoxel incoherent motion (IVIM) MRI. Methods: Thirteen ( n = 13) healthy volunteers underwent two liver MR exams: one preprandial after fasting and one postprandial after ingesting nutrition shakes. IVIM data were acquired four times: twice preprandial to evaluate repeatability using coefficients of variation ( CV ) and twice postprandial to evaluate meal‐induced changes relative to preprandial estimates. 4D flow MRI was acquired once pre‐ and postprandial to estimate portal venous flow rate, which was normalized by liver volume to evaluate correlations with IVIM. IVIM estimates of the diffusion coefficient ( D ), R ₂ ‐corrected perfusion signal fraction ( F _c ), blood velocity standard deviation ( V _b ), and spatial volumes associated with slow microvascular flow (Ω ₁ ), fast microvascular flow (Ω ₂ ), and major vessels (Ω ₃ ) were determined. Results: Significantly ( p ≤ 0.01) higher V _b and Ω ₂ volume and lower Ω ₁ volume were observed postprandial compared to preprandial. The magnitudes of the postprandial changes in V _b , Ω ₁ volume, and Ω ₂ volume (|median relative change| ≥ 10.2%, 48.4%, and 13.7%, respectively) were greater than the corresponding repeatability ( CV = 5.19%, 21.3%, and 8.13%, respectively). Significant ( p = 0.01) correlation between liver volume‐normalized portal venous flow rate and V _b was observed. Conclusion: The potential of IVIM to detect physiological (e.g., prandial‐induced) changes in microvascular flow in the liver was demonstrated. IVIM data may need to be acquired in a fasting state to avoid confounding prandial effects on IVIM‐derived microvascular flow estimates.

Background: Manual prescription of liver MRI volumes introduces inter‐operator variability and prolongs exam duration. Artificial intelligence (AI)‐based prescription has been demonstrated, but existing work has been limited to single‐vendor settings. Purpose: To develop and validate an AI model for automated liver MRI prescription using a large, consecutive, multicenter dataset encompassing multiple vendors, field strengths, localizer sequences, and liver pathologies. Study Type: Retrospective. Population: A total of 11,012 patients (5928 female) who underwent liver MRI exams across three vendors (GE HealthCare, Philips Healthcare, Siemens Healthineers) at three institutions, split into training/validation/test sets (70/10/20%). Liver disorders included cirrhosis, ascites, portal hypertension, and iron overload. Field Strength/Sequence: Three‐plane localizers acquired using single‐shot fast spin echo (68.3%) and gradient echo (31.7%) at 1.5 T (52.8%) and 3 T (47.2%). Assessment: A YOLOv8 object detection model was trained to detect the liver, torso, and arms on localizer images. Automated prescription accuracy was assessed using three‐dimensional intersection‐over‐union (IoU _3D ) for axial, coronal, and sagittal prescriptions. Subgroup analyses evaluated performance across technical and patient‐related factors. Statistical Tests: Mann–Whitney U tests with Bonferroni correction, Kruskal‐Wallis tests, Wilcoxon signed‐rank tests, Spearman correlation. Effect sizes were calculated using rank‐biserial correlation ( r _rb ) and epsilon‐squared ( ε ² ). p < 0.05 was considered statistically significant. Results: Median axial prescription IoU _3D was 0.963 (interquartile range: 0.946–0.976), with median IoU _3D above 0.940 for coronal and sagittal prescriptions. For axial prescriptions, margins less than 4 mm in each direction were required to achieve complete coverage of manual prescriptions in 90% of cases in the held‐out test set. Effects were negligible to small across vendors, field strengths, localizer sequences, and patient factors ( r _rb ≤ 0.16, ε ² ≤ 0.01). Data Conclusion: A single multivendor model achieved accurate automated liver MRI prescription without clinically meaningful performance differences across vendors, field strengths, localizer sequences, or patient factors. Evidence Level: 2. Technical Efficacy: Stage 1.

Background: Proton density fat fraction (PDFF) is typically measured using confounder‐corrected, multi‐echo, gradient‐recalled‐echo chemical‐shift‐encoded (CSE)‐MRI. Ferumoxytol, an iron‐based MRI contrast agent, increases liver and bone marrow (BM) R2*, a major confounder of PDFF. Purpose: To assess the impact of ferumoxytol‐induced R2* increase on BM and liver PDFF. Study Type: Retrospective. Population: Eleven healthy volunteers (6 female) imaged before and 1, 2, 4, 7, and 30 days after ferumoxytol; 25 patients (10 female) imaged before and 2–3 days after ferumoxytol. Field Strength/Sequence: 1.5‐T and 3.0‐T; 3D GRE‐CSE‐MRI. Assessment: Regions of interest were placed on liver and spinal BM to measure PDFF and R2* before and after ferumoxytol. Dual‐R2* simulations evaluated the effect of differential water and fat R2* relaxation on PDFF. Statistical Tests: Liver and BM PDFF and R2* were compared before and after ferumoxytol using regression analyses and F ‐tests. Changes from baseline were assessed with Wilcoxon signed‐rank tests; p < 0.05 was considered significant. Results: Among volunteers, BM R2* increased significantly after ferumoxytol, peaking at Day 1, 222 (84) s‐1 at 1.5‐T and 328 (106) s‐1 at 3.0‐T (baseline 72 [26] s‐1 and 182 [64] s‐1, respectively). BM PDFF increased significantly after ferumoxytol: 49.7 (11.3) % at 1.5‐T and 47.5 (12.9) % at 3.0‐T (baseline 41.1 [13.2] % and 38.4 [11.0] %, respectively). Post‐ferumoxytol liver R2* also increased, yet liver PDFF did not significantly change ( p range 0.579–0.983). Among patients, post‐ferumoxytol BM PDFF demonstrated a systematic positive bias relative to baseline, yet this effect was not observed in liver. Simulations confirmed that a differential increase in water relative to fat R2* results in PDFF bias when a single‐R2* model is fitted. Data Conclusion: BM PDFF demonstrates a systematic bias in the presence of ferumoxytol‐induced R2* increase and should be interpreted with caution. This bias was not observed in the liver. Evidence Level: 3. Technical Efficacy: Stage 1.

Background: Access to MRI is limited by lengthy exam times and inefficient utilization. Focused protocols can reduce exam times, but workflow variability and inefficient room turnaround contribute to conservative scheduling with long exam slots. Purpose: To develop and evaluate a high‐throughput clinical MRI suite architecture and workflow, using an AI‐prescribed free‐breathing chemical shift‐encoded (CSE) MRI exam to quantify liver proton density fat fraction (PDFF) in under 5 min of total MRI room time. Study Type: Prospective. Subjects: 24 healthy volunteers in two cohorts: 12 research staff (7 women/5 men; age 26.8 ± 5.8 years) and 12 community volunteers (6 women/6 men; age 41.3 ± 13.5 years). Field Strength/Sequence: 1.5 T; free‐breathing 2D multi‐echo gradient echo CSE‐MRI. Assessment: Each participant underwent three nonconsecutive CSE‐MRI exams in a continuously queued workflow to characterize timing and PDFF repeatability. Workflow intervals were recorded from timestamped video review and image metadata. Staff cohort exams included two CSE‐MRI acquisitions to assess within‐exam repeatability, while community cohort exams included one to simulate clinical practice. Three radiologists (8/13/14 years of experience) independently evaluated AI‐automated prescriptions for complete liver coverage and rated CSE‐MRI image quality (five‐point Likert scale). Statistical Tests: Student's t ‐tests; Gwet's AC2; repeatability coefficients (RCs) with bootstrap 95% confidence intervals; Bland–Altman analysis. p < 0.05 was significant. Results: Diagnostic image quality was achieved in all 72 exams (median PDFF Likert score 5/5, inter‐rater AC2 ≥ 0.86). Total MRI room times averaged 4:09 ± 0:14 min (staff) and 3:35 ± 0:34 min (community). Turnaround times averaged under 2 min, enabling throughput of 16.1 exams per hour in the community cohort. Automated prescription achieved complete liver coverage in all exams. PDFF RCs were 0.78% (staff within‐exam), 0.99% (staff between‐exam), and 1.21% (community between‐exam) absolute PDFF. Data Conclusion: The proposed high‐throughput MRI workflow achieved over 16 exams per hour with highly repeatable liver fat quantification, demonstrating a framework for improving MRI utilization and access. Evidence Level: 1. Stage of Technical Efficacy: 2.

Background: In chronic liver disease, fat and fibroinflammatory changes often coexist. However, their biomarkers, proton density fat fraction (PDFF) and T ₁ , are typically assessed separately. Their reproducibility under mutual confounding remains unclear. Purpose: To assess multicenter, multi‐vendor reproducibility of confounder‐corrected chemical shift‐encoded (CSE)‐MRI‐based PDFF mapping and MOLLI‐based T ₁ mapping using a combined PDFF‐T ₁ phantom. Study Type: Prospective phantom study. Phantom: Commercial PDFF‐T ₁ Phantom (Model 725) with varying PDFF (0%–30%) and T ₁ (200–1400 ms) values. Field Strength/Sequence: 1.5 T and 3 T multi‐echo, three‐dimensional spoiled‐gradient‐echo (SGRE) sequence for PDFF mapping, and MOLLI sequence (5(3)3 acquisition scheme) using two‐dimensional SGRE readouts for T ₁ mapping across four centers and vendors. Assessment: PDFF and T ₁ maps were acquired using standardized protocols. PDFF maps were reconstructed locally, while T ₁ maps were generated using a centralized algorithm. All maps were quantitatively analyzed by a single radiologist using standardized region‐of‐interest placement. Phantom temporal stability was assessed at one center across five sessions over 9 months (baseline, retest, 1 week, 6 and 9 months). Statistical Tests: Intraclass correlation coefficients (ICC), reproducibility coefficients (RDC), and linear regression analysis were used. A p value < 0.05 was considered statistically significant. Results: PDFF showed overall excellent reproducibility (ICC = 0.987, RDC = 3.7%), with increased variability at higher T ₁ values (RDC up to 7.9% at T ₁ = 1400 ms). T ₁ mapping showed good reproducibility in the absence of fat (RDC 16–161 ms at PDFF = 0%), but moderate to poor reproducibility in the presence of fat, with RDC increasing up to 1553 ms at PDFF 30%. Temporal stability was excellent ICC ≥ 0.998 for both PDFF and T ₁ , and RDC of 1.1%–1.3% for PDFF and 52–57 ms for T ₁ . Data Conclusion: This phantom study demonstrated high reproducibility of PDFF, whereas T ₁ reproducibility deteriorated at higher fat and T ₁ levels, underscoring the need for fat‐corrected T ₁ mapping for reliable assessment of fibroinflammatory changes. Evidence Level: N/A. Technical Efficacy: Stage 1.

Purpose: To calibrate the effect of hepatic steatosis on diffusion parameters, including and pure diffusion coefficient (), by Monte Carlo simulation and phantom studies. Methods: In the simulation study, four types of virtual liver models were constructed by incorporating size, nearest neighbor distance and regional anisotropy of fat droplets. By simulating magnetic field, proton movement and phase accrual, DWI signals were synthesized and analyzed to predict and . Correlation analyses were performed to investigate relationships between fat fraction (FF) and diffusion parameters predictions. Bland Altman analysis was conducted to evaluate the effects of virtual liver model, fat susceptibility and proton diffusivity on these predictions. In the phantom study, a fat‐water phantom was scanned to investigate the relationships between proton density fat fraction (PDFF) and diffusion parameters. Results: The simulation study demonstrated that and predictions were linearly and negatively correlated with FFs (), unaffected by virtual liver model and fat susceptibility (). Additionally, proton diffusivity posed positive effects on these predictions. Compared with in vivo calibration, simulations with the virtual liver model incorporating all criteria, fat susceptibility of 0.2 ppm and proton diffusivity of 0.96 μm ² /ms can accurately predict the diffusion parameters. The phantom study indicated that and measurements were linearly and negatively correlated with PDFF (). Conclusion: Hepatic steatosis decreases both diffusion parameters and should be calibrated in the DWI‐based assessment of liver fibrosis.

Intravoxel incoherent motion (IVIM) MRI allows for simultaneous assessment of tissue microcirculation (perfusion) and diffusion of water. In single‐center studies, IVIM has shown great potential for diagnosis, treatment outcome prediction, and treatment monitoring for many different diseases and organs. However, heterogeneity in data acquisition protocols, pre‐processing pipelines, and post‐processing routines yields differences in reported IVIM parameters, which has constrained large‐scale deployment of IVIM. Moreover, deploying IVIM protocols and analysis typically requires technical expertise, further challenging wider use, especially for clinicians. In this consensus paper, to accelerate the deployment of IVIM, we provide recommendations and harmonize protocols for brain, breast, kidney, liver, muscle, and pancreas IVIM studies. For this goal we organized multiple questionnaires and held a dedicated workshop. To ensure a level of standardized, reproducible results, without restricting innovation, we suggest a small subset of b ‐values to always be measured and analyzed separately, and to which more extensive b ‐value sampling can be added for advanced investigations. We further introduce detailed recommendations on acquisition protocols and analysis pipelines. To increase consistency, repeatability, and reproducibility, we highly recommend that these protocols and pipelines be deployed by scientists and clinicians for IVIM studies. For advanced users who desire different protocols or analysis approaches, we suggest adding results from our suggested protocols and analysis pipeline in the supplemental part of their paper to enable retrospective studies.

Purpose: To introduce and evaluate the feasibility of a novel RF‐phase modulated gradient echo (GRE) method for quantitative diffusion MRI, aimed at mitigating geometric distortion and enabling high‐resolution 3D quantitative diffusion/T2 mapping as a complementary alternative to conventional DWI. Theory and Methods: The proposed phase‐based diffusion (PBD) method employs RF phase modulation to encode both diffusion and T2 information into the GRE signal phase. A closed‐form analytical model enables joint apparent diffusion coefficient (ADC) and T2 mapping via iterative reconstruction. The method's feasibility was evaluated via Bloch equation simulations, phantom experiments, and preliminary in vivo imaging studies. Results: Monte Carlo simulations revealed that PBD provides more accurate median ADC estimates at low signal‐to‐noise ratios (SNRs) compared to conventional single‐shot echo‐planar imaging (SS‐EPI), although PBD exhibited greater variability. Phantom studies demonstrated good agreement for PBD‐derived ADC values (e.g., R ² = 0.99) with reference methods and strong correlation for PBD‐derived T2 values (e.g., R ² = 0.89), though the latter showed some systematic bias in phantoms. In vivo results from patients with benign or malignant prostate disease demonstrated the feasibility of the PBD method to provide high‐resolution ADC and T2 maps with minimal geometric distortions relative to conventional SS‐EPI. Conclusion: PBD provides ADC and T2 maps with improved geometric fidelity in phantoms and in vivo, and offers robust median ADC estimates from noisy data based on simulations. This combination of spatial precision and noise characteristics makes PBD promising for applications such as high‐resolution DWI for prostate MRI.

Purpose: To develop and validate a vendor‐agnostic, motion‐insensitive proton‐density fat‐fraction (PDFF) quantification method. Methods: Flip‐angle‐modulated (FAM) 2D chemical‐shift‐encoded (CSE) MRI for PDFF quantification was implemented in both the vendor‐agnostic platform Pulseq (“Pulseq‐FAM”) and one vendor‐specific platform (“GE‐specific FAM”). These implementations were distributed to four sites with twelve MR systems of three vendors (Siemens/GE/Philips) and field strengths (0.55T/1.5T/3T). A sequentially‐shipped 16‐vial phantom (PDFF = 0%–30%/T1 _water = 200–1400 ms) underwent confounder‐corrected PDFF mapping with commercial 3D‐CSE methods and GE‐specific FAM as available on each system, and Pulseq‐FAM on every system. To assess bias, phantom PDFF measurements were compared to reference. Between‐system variance was evaluated with linear mixed‐effects modeling. Different volunteers were also imaged at each site to assess free‐breathing PDFF mapping feasibility. A prospective single‐site volunteer study was also conducted. Adult patients and children were imaged with breath‐held 3D‐CSE and free‐breathing GE‐specific and Pulseq‐FAM. Radiologists evaluated images for overall quality and motion artifacts. To assess bias, Pulseq‐FAM PDFF measurements were compared to 3D‐CSE and GE‐specific FAM. Test–retest repeatability was assessed by re‐imaging after repositioning. Between‐field‐strength reproducibility was assessed at 1.5T and 3.0T. Results: In the multi‐center study, Pulseq‐FAM showed reduced T1‐bias and between‐system variability versus 3D‐CSE in phantom PDFF measurements, and free‐breathing feasibility in volunteers. In the single‐site volunteer study ( N = 57), Pulseq‐FAM improved image quality and motion artifacts versus 3D‐CSE ( p < 0.01). Pulseq‐FAM showed excellent agreement with 3D‐CSE (95% limits‐of‐agreement (LoA) = 3.4% PDFF) and GE‐specific FAM (LoA = 2.0%). Pulseq‐FAM showed excellent repeatability (repeatability coefficient (RC) = 1.6% PDFF) and between‐field‐strength reproducibility (reproducibility coefficient (RDC) = 2.4%) versus 3D‐CSE (RC = 2.7%/RDC = 3.4%; differences p < 0.05). Conclusion: Pulseq‐FAM enables accurate, reproducible, vendor‐agnostic, and motion‐insensitive PDFF quantification in adults and children.

Purpose: Evaluate the feasibility of using perfused explanted human livers for validating intravoxel incoherent motion (IVIM). Methods: Eight ( n = 8) explanted livers from deceased donors were obtained. The portal vein and hepatic artery of each explanted liver were connected to a perfusion system. IVIM data were acquired at four total volumetric flow rates (0, 0.6, 0.9, and 1.2 L/min). For each IVIM dataset, diffusion coefficient ( D ), relaxation‐corrected perfusion signal fraction ( F _c ), blood velocity SD ( V _b ), and/or pseudo‐diffusion coefficient ( D* ) were estimated. Linear mixed‐effects modeling was performed to determine if the effect of applied flow, flow rate, and fibrosis stage on the estimated IVIM parameters was significant ( F ‐tests), while correcting for temporal effect(s). Liver fibrosis stages were obtained from clinical histology. Results: D was independent of applied flow and total volumetric flow rate ( p ≥ 0.30). F _c was approximately zero when no flow was applied and a positive, non‐negligible value that was independent of flow rate with applied flow ( p < 0.001 and ≥ 0.58 for applied flow and flow rate effects, respectively). V _b and D* were dependent on applied flow and flow rate ( p ≤ 0.04 with flow rate effect size ≥ 0.69 mm/s per L/min and 25.7 × 10 ⁻³ mm ² /s per L/min for V _b and D* , respectively). Significantly lower ( p ≤ 0.01) F _c , V _b , and D* estimates were observed for livers with moderate‐to‐advanced fibrosis (stages F2‐4) compared to no‐to‐mild fibrosis (stages F0‐1) (effect sizes = −0.74%, −0.60 mm/s, and −19.8 × 10 ⁻³ mm ² /s, respectively). Conclusion: Perfused explanted human livers from deceased donors may serve as biologically accurate systems for validation of quantitative IVIM techniques.