Oscar Jalnefjord

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.

Background: Skeletal muscle blood oxygen level dependent (BOLD) MRI is a technique for assessing vascular function in peripheral limbs. In patients, however, an increased frequency of atypical response patterns has been observed, warranting investigation into its underlying causes. Purpose: To explore the dynamics of cuff‐induced ischemia and reactive hyperemia with BOLD MRI, focusing on the occurrence, quantification, and interpretation of frequent atypical cuff‐induced response patterns. Study Type: Secondary assessment of prospectively collected datasets. Population: Seventeen peripheral artery disease (PAD) patients (age: 64–80 years, ankle‐brachial index (ABI) range: 0.4–0.8), 8 age‐matched controls (age: 64–82 years, ABI range: 1–1.2), and 14 young controls (age: 22–39 years). Field Strength/Sequence: ‐weighted multi‐echo gradient‐echo sequence with 11 equidistant echo‐times at 3T. Assessment: A transverse slice of the calf was imaged repeatedly during an upper‐thigh cuff compression cycle consisting of 1‐min baseline, 5‐min arterial occlusion (cuff inflated) and 5‐min reactive hyperemia (cuff deflated). ‐mapping with ROI‐wise analysis of the soleus and gastrocnemius muscles produced ‐time curves and previously established metrics, including the hyperemic upslope () and time‐to‐peak (TTP) were assessed. The time‐curves were surveyed to identify the frequency and type of deviations from expected. ‐time curves of soleus were further analyzed by fitting a parameterized function to derive additional metrics including depth of the undershoot on cuff release and deviation from a monotonic T2* decrease. Ankle‐brachial index (ABI) served as a clinical reference for comparisons. Statistical Tests: Non‐parametric 2‐tailed Wilcoxon rank‐sum tests to assess differences between patients and age‐matched controls. A p value < 0.05 was considered significant. Results: Atypical cuff‐induced response patterns in PAD patients included a negative cuff‐induced transient (42%, soleus) and non‐monotonic declines in during cuffing (58%, soleus). While these contributed to increased variabilities in patients, (−0.63 ms/s) and TTP (62.8 s) between patients and age‐matched controls. Data Conclusion: HS _up and TTP provided means to quantify calf muscle responses to cuffing. Specific cases of non‐monotonous decrease during cuffing suggest the detection of venous valve leakages in inter‐muscular veins. Insufficient cuff pressure produced curves with absent ischemic plateau and weak hyperemic responses, the recognition of which is important to prevent physiological misinterpretations of curves and derived metrics. Evidence Level: 1. Technical Efficacy: 3.

Purpose: The intravoxel incoherent motion (IVIM) model is commonly used to separate the effects of motion related to diffusion and blood microcirculation (perfusion) on the MR signal. Depending on the encoding time ( T ), it is possible to probe the different temporal regimes of blood motion, which resemble a ballistic flow at short T and a pseudo‐diffusion at long T . The purpose of this work was to derive an encoding‐time‐dependent analytical model for flow‐compensated IVIM and to estimate the corresponding microvascular IVIM parameters in healthy brain. Theory and Methods: An encoding‐time‐dependent analytical IVIM model was derived for flow‐compensated/non‐flow‐compensated (FC/NC) double diffusion encoding (DDE) from the Langevin equation and validated using simulations. Eleven healthy participants were scanned to estimate microvascular IVIM parameters (blood velocity ν and blood correlation time τ) in healthy brain using the proposed model, with T = 50–100 ms. Results: The IVIM parameters were estimated to be τ = 123.1 ± 50 ms, ν = 1.51 ± 0.76 mm/s, perfusion fraction f = 4.75 ± 1.94%, and tissue diffusion coefficient D = 0.91 ± 0.32 μm ² /ms in the healthy human brain, although simulations indicate a positive bias for τ . For very short/long T , the proposed model approaches established models for the ballistic/diffusive regimes. Pseudocode for the derivation of the analytical model is presented to facilitate a transfer to other gradient waveforms or pulse sequences. Conclusion: An encoding‐time‐dependent analytical IVIM model is presented for FC/NC DDE. In vivo results and simulations indicate that IVIM experiments with encoding times typical for clinical MRI scanners probe an intermediate to ballistic blood flow regime in the brain.

Diffusion MRI models accounting for varying diffusion times and high b‐values, such as VERDICT, hold potential for non‐invasively characterizing tumor tissue types, potentially enabling improved tumor grading, and treatment evaluation. Furthermore, cluster analysis can aid in identifying multidimensional patterns in the diffusion MRI (dMRI) data that are not apparent when analyzing individual parameters in isolation. The aim of this study was to evaluate how well cluster analysis of VERDICT parameters can be used for intratumor tissue characterization compared to ADC in a mouse model of human small intestine neuroendocrine tumor (GOT1), and to validate the method by histological analysis. Mice implanted with GOT1 were irradiated and subsequently imaged using a dMRI protocol designed for estimation of VERDICT parameters and ADC values. Histological analysis using hematoxylin and eosin (H&E), Masson's trichrome, and Ki67 staining identified three distinct tumor tissue types: necrotic, fibrotic, and viable tumor tissue. ROIs were drawn on regions of high and low ADC, which spatially matched with necrosis or fibrosis, and viable tumor tissue, respectively. Among the VERDICT parameters, the cell radius index (R) was most effective in distinguishing between necrotic and fibrotic tissue, whereas the intracellular fraction (f_IC) was the most effective in differentiating viable from non‐viable tissue. A Gaussian mixture model (GMM) of three clusters, representing each tumor tissue type, was fitted to R and f_IC of all tumor voxel data. VERDICT cluster maps corresponded well with the histology classification maps overall. Fibrotic tissue corresponded best with the cluster of low f_IC and low R, necrotic tissue with the cluster of low f_IC and high R, and viable tumor tissue with the cluster of high f_IC and intermediate R. In conclusion, GMM cluster analysis of VERDICT MRI data shows potential in differentiating necrotic, fibrotic, and viable tumor tissue in irradiated GOT1 tumors.

Dual-polarity readout is a simple and robust way to mitigate Nyquist ghosting in diffusion-weighted echo-planar imaging but imposes doubled scan time. We here propose how dual-polarity readout can be implemented with little or no increase in scan time by exploiting an observed b-value dependence and signal averaging. The b-value dependence was confirmed in healthy volunteers with distinct ghosting at low b-values but of negligible magnitude at b = 1000 s/mm². The usefulness of the suggested strategy was exemplified with a scan using tensor-valued diffusion encoding for estimation of parameter maps of mean diffusivity, and anisotropic and isotropic mean kurtosis, showing that ghosting propagated into all three parameter maps unless dual-polarity readout was applied. Results thus imply that extending the use of dual-polarity readout to low non-zero b-values provides effective ghost elimination and can be used without increased scan time for any diffusion MRI scan containing signal averaging at low b-values.

Objectives: Signal drift has been put forward as one of the fundamental confounding factors in diffusion MRI (dMRI) of the brain. This study characterizes signal drift in dMRI of the brain, evaluates correction methods, and exemplifies its impact on parameter estimation for three intravoxel incoherent motion (IVIM) protocols. Materials and methods: dMRI of the brain was acquired in ten healthy subjects using protocols designed to enable retrospective characterization and correction of signal drift. All scans were acquired twice for repeatability analysis. Three temporal polynomial correction methods were evaluated: (1) global, (2) voxelwise, and (3) spatiotemporal. Effects of acquisition order were simulated using estimated drift fields. Results: Signal drift was around 2% per 5 min in the brain as a whole, but reached above 5% per 5 min in the frontal regions. Only correction methods taking spatially varying signal drift into account could achieve effective corrections. Altered acquisition order introduced both systematic changes and differences in repeatability in the presence of signal drift. Discussion: Signal drift in dMRI of the brain was found to be spatially varying, calling for correction methods taking this into account. Without proper corrections, choice of protocol can affect dMRI parameter estimates and their repeatability.

Purpose: T₁ mapping is a widely used quantitative MRI technique, but its tissue‐specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well‐established inversion‐recovery T₁ mapping technique, using acquisition details from a seminal T₁ mapping paper on a standardized phantom and in human brains.Methods: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T₁ mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open‐source platforms. Intersubmission and intrasubmission comparisons were performed.Results: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org.Conclusion: The T₁ intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T₁ measures across independent research groups, bringing us one step closer to a practical clinical baseline of T₁ variations in vivo.

Purpose: Joint analysis of flow‐compensated (FC) and non‐flow‐compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain.Methods: Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non‐linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b‐values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning‐based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b‐values 0–200 s/mm² and corresponding flow weighting factors 0–2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis.Results: Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning‐based algorithm for IVIM parameters and , and for the Bayesian algorithm only for , relative to the other methods.Conclusion: A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning‐based algorithms appear promising.