BACKGROUND: The diffusion and perfusion parameters derived from intravoxel incoherent motion (IVIM) imaging provide promising biomarkers for noninvasively quantifying and managing various diseases. Nevertheless, due to the distribution gap between simulated and real datasets, the out-of-distribution (OOD) problem occurred in supervised learning-based methods degrades their performance and hinders their real applications.
OBJECTIVE: To address the OOD problem in supervised methods and to further improve the accuracy and stability of IVIM parameter estimation, this work proposes a novel learning framework called IterANN, based on mean deviation prior (MDP) between training and estimated IVIM parameters on the test set.
METHODS: Specifically, MDP indicates that the mean of the estimated IVIM parameters always locates between the mean of IVIM parameters in the test and train sets. In IterANN, we adopt a very simple artificial neural network (ANN) architecture of two hidden layers with 12 neurons per hidden layer, an input layer containing the signals acquired at multiple b-values and an output layer composed of three IVIM parameters ( D $D$ , F $F$ and D S t a r $DStar$ ). Inspired by MDP, the distribution of IVIM parameters in the training set (simulated data) is iteratively updated so that their mean gradually approaches the predicted values of the real data. This aims to achieve a strong correlation between the simulated data and the real data. To validate the effectiveness of IterANN, we compare it with several methods on both simulation and real acquisition datasets, including 21 healthy and 3 tumor subjects, in terms of residual errors of IVIM parameters or DW signals, the coefficients of variation (CV) of IVIM parameters, and the parameter contrast-to-noise ratio (PCNR) between normal and tumor tissues.
RESULTS: On two simulation datasets, the proposed IterANN achieves the lowest residual error in IVIM parameters, especially in the case of low signal-to-noise ratio (SNR = 10), the residual error of D $D$ , F $F$ and D S t a r $DStar$ is decreased by 15.82 % / 14.92 % , 81.19 % / 74.04 % , 50.77 % / 1.549 % $15.82\\%/14.92\\%, 81.19\\%/74.04\\%, 50.77\\%/1.549\\%$ (Gaussian distribution /realistic distribution) respectively comparing to the suboptimal method. On real dataset, the IterANN achieves the highest PCNR when comparing the normal and tumor regions. Additionally, the proposed IterANN demonstrated better stability, with its CV being significantly lower than that of other methods in the vast majority of cases ( p < 0.01 $p<0.01$ , paired-sample Student\'s t-test).
CONCLUSIONS: The superior performance of IterANN demonstrates that updating the distribution of the train set based on MDP can effectively solve the OOD problem, which allows us not only to improve the accuracy and stability of the estimated IVIM parameters, but also to increase the potential of IVIM in disease diagnosis.

Intravoxel incoherent motion (IVIM) MRI provides insight into tissue diffusion and perfusion. Here, estimates of perfusion fraction ( f ), pseudo-diffusion coefficient ( D * ), and diffusion coefficient ( D ) obtained via different fitting methods are compared to ascertain (1) the optimal analysis strategy for muscles of the lumbar spine and (2) repeatability of IVIM parameters in skeletal muscle at rest. Diffusion-weighted images were acquired in the lumbar spine at rest in 15 healthy participants. Data were fit to the bi-exponential IVIM model to estimate f , D * and D using three variably segmented approaches based on non-linear least squares fitting, and a Bayesian fitting method. Assuming that perfusion and diffusion are temporally stable in skeletal muscle at rest, and spatially uniform within a spinal segment, the optimal analysis strategy was determined as the approach with the lowest temporal or spatial variation and smallest residual between measured and fit data. Inter-session repeatability of IVIM parameters was evaluated in a subset of 11 people. Finally, simulated IVIM signal at varying signal to noise ratio were evaluated to understand precision and bias. Experimental results showed that IVIM parameter values differed depending on the fitting method. A three-step non-linear least squares fitting approach, where D , f , and D * were estimated sequentially, generally yielded the lowest spatial and temporal variation. Solving all parameters simultaneously yielded the lowest residual between measured and fit data, however there was substantial spatial and temporal variability. Results obtained by Bayesian fitting had high spatial and temporal variability in addition to a large residual between measured and fit data. Simulations showed that all fitting methods can fit the IVIM data at signal to noise ratios >35, and that D * was the most challenging to accurately obtain. Overall, this study motivates use of a three-step non-linear least squares fitting strategy to quantify IVIM parameters in skeletal muscle.

We discuss two potential non-invasive MRI methods to study phenomena related to subarachnoid cerebrospinal fluid (CSF) motion and perivascular fluid transport, and their association with sleep and aging. We apply diffusion-based intravoxel incoherent motion (IVIM) imaging to evaluate pseudodiffusion coefficient, D*, or CSF movement across large spaces like the subarachnoid space (SAS). We also performed perfusion-based multi-echo, Hadamard encoded arterial spin labeling (ASL) to evaluate whole brain cortical cerebral blood flow (CBF) and trans-endothelial exchange (Tex) of water from the vasculature into the perivascular space and parenchyma. Both methods were used in young adults (N = 9, 6 F, 23 ± 3 years old) in the setting of sleep and sleep deprivation. To study aging, 10 older adults (6 F, 67 ± 3 years old) were imaged after a night of normal sleep and compared with the young adults. D* in SAS was significantly (p < 0.05) reduced with sleep deprivation (0.016 ± 0.001 mm2/s) compared to normal sleep (0.018 ± 0.001 mm2/s) and marginally reduced with aging (0.017 ± 0.001 mm2/s, p = 0.029). Cortical CBF and Tex were unchanged with sleep deprivation but significantly lower in older adults (37 ± 3 ml/100 g/min, 578 ± 61 ms) than in young adults (42 ± 2 ml/100 g/min, 696 ± 62 ms). IVIM was sensitive to sleep physiology and aging, and multi-echo, multi-delay ASL was sensitive to aging.

OBJECTIVE: To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer.
METHODS: This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed.
RESULTS: Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05).
CONCLUSIONS: IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.

OBJECTIVE: To investigate the correlation between DCE-MRI, R2*, IVIM, and clinicopathological features of rectal cancer.
METHODS: This was a prospective study, enrolling 42 patients with rectal cancer, 20 of whom underwent rectal mesorectal excision. Dynamic contrast-enhanced magnetic resonance imaging scanning was performed preoperatively in all patients, and additional preoperative scanning of R2* imaging and intravoxel incoherent motion was performed in those who underwent surgery. Artificially delineate the ROI around the tumor. Functional magnetic resonance index parameters Ktrans, Ve, R2*, D, D*, and f were estimated by computer software to analyze postoperative pathological reports of patients undergoing total mesenteric resection. Correlation and significance analyses of imaging metrics and pathologic features were performed by GraphPad Prism 9 to assess statistical significance.
RESULTS: DEC-MRI, R2*, and IVIM have certain application values in the distance from the lower margin of the tumor to the anorectal ring, imaging T stage and N stage, tumor markers CEA and CA199, immunohistochemical indexes Ki-76 and P53, lymph node cancer metastasis, and rectal fascia status (P < 0.05).
CONCLUSIONS: DEC-MRI, R2*, and IVIM provide reliable quantitative parameters for preoperative clinicopathological evaluation of patients with rectal cancer.

UNASSIGNED: Most functional magnetic resonance research has primarily examined alterations in the affected kidney, often neglecting the contralateral kidney. Our study aims to investigate whether imaging parameters accurately depict changes in both the renal cortex and medulla in a unilateral ureteral obstruction rat model, thereby showcasing the utility of intravoxel incoherent motion (IVIM) in evaluating contralateral renal changes.
UNASSIGNED: Six rats underwent MR scans and were subsequently sacrificed for baseline histological examination. Following the induction of left ureteral obstruction, 48 rats were scanned, and the histopathological examinations were conducted on days 3, 7, 10, 14, 21, 28, 35, and 42. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudodiffusion (D*), and perfusion fraction (f) values were measured using IVIM.
UNASSIGNED: On the 10th day of obstruction, both cortical and medullary ADC values differed significantly between the UUO10 group and the sham group (p < 0.01). The cortical D values showed statistically significant differences between UUO3 group and sham group (p < 0.01) but not among UUO groups at other time point. Additionally, the cortical and medullary f values were statistically significant between the UUO21 group and the sham group (p < 0.01). Especially, the cortical f values exhibited significant differences between the UUO21 group and the UUO groups with shorter obstruction time (at time point of 3, 7, 10, 14 day) (p < 0.01).
UNASSIGNED: Significant hemodynamic alterations were observed in the contralateral kidney following renal obstruction. IVIM accurately captures changes in the unobstructed kidney. Particularly, the cortical f value exhibits the highest potential for assessing contralateral renal modifications.

Background: The purpose of this study was to investigate the dependence of Intravoxel Incoherent Motion (IVIM) parameters measured in the human calf on B0. Methods: Diffusion-weighted image data of eight healthy volunteers were acquired using five b-values (0-600 s/mm2) at rest and after muscle activation at 0.55 and 7 T. The musculus gastrocnemius mediale (GM, activated) was assessed. The perfusion fraction f and diffusion coefficient D were determined using segmented fits. The dependence on field strength was assessed using Student\'s t-test for paired samples and the Wilcoxon signed-rank test. A biophysical model built on the three non-exchanging compartments of muscle, venous blood, and arterial blood was used to interpret the data using literature relaxation times. Results: The measured perfusion fraction of the GM was significantly lower at 7 T, both for the baseline measurement and after muscle activation. For 0.55 and 7 T, the mean f values were 7.59% and 3.63% at rest, and 14.03% and 6.92% after activation, respectively. The biophysical model estimations for the mean proton-density-weighted perfusion fraction were 3.37% and 6.50% for the non-activated and activated states, respectively. Conclusions: B0 may have a significant effect on the measured IVIM parameters. The blood relaxation times suggest that 7 T IVIM may be arterial-weighted whereas 0.55 T IVIM may exhibit an approximately equal weighting of arterial and venous blood.

OBJECTIVE: Diffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from microcirculation effects, and combinations of the two. However, interpretation of these features and adaptation of more specific models remains an ongoing challenge. One input to this process is a whole organ distillation of corticomedullary contrast of diffusion metrics, as has been explored for other renal biomarkers.
METHODS: In this work, we probe the spatial dependence of diffusion MRI metrics with concentrically layered segmentation in 11 healthy kidneys at 3 T. The metrics include those from DTI, IVIM, a combined approach titled \"REnal Flow and Microstructure AnisotroPy (REFMAP)\", and a multiply encoded model titled \"FC-IVIM\" providing estimates of fluid velocity and branching length.
RESULTS: Fractional anisotropy decreased from the inner kidney to the outer kidney with the strongest layer correlation in both parenchyma (including cortex and medulla) and medulla with Spearman correlation coefficients and p-values (r, p) of (0.42, <0.001) and (0.37, <0.001), respectively. Also, dynamic parameters derived from the three models significantly decreased with a high correlation from the inner to the outer parenchyma or medulla with (r, p) ranges of (0.46-0.55, <0.001).
CONCLUSIONS: These spatial trends might find implications for indirect assessments of kidney physiology and microstructure using diffusion MRI.

OBJECTIVE: To determine whether intravoxel incoherent motion (IVIM) describes the blood perfusion in muscles better, assuming pseudo diffusion (Bihan Model 1) or ballistic motion (Bihan Model 2).
METHODS: IVIM parameters were measured in 18 healthy subjects with three different diffusion gradient time profiles (bipolar with two diffusion times and one with velocity compensation) and 17 b-values (0-600 s/mm2) at rest and after muscle activation. The diffusion coefficient, perfusion fraction, and pseudo-diffusion coefficient were estimated with a segmented fit in the gastrocnemius medialis (GM) and tibialis anterior (TA) muscles.
RESULTS: Velocity-compensated gradients resulted in a decreased perfusion fraction (6.9% ± 1.4% vs. 4.4% ± 1.3% in the GM after activation) and pseudo-diffusion coefficient (0.069 ± 0.046 mm2/s vs. 0.014 ± 0.006 in the GM after activation) compared to the bipolar gradients with the longer diffusion encoding time. Increased diffusion coefficients, perfusion fractions, and pseudo-diffusion coefficients were observed in the GM after activation for all gradient profiles. However, the increase was significantly smaller for the velocity-compensated gradients. A diffusion time dependence was found for the pseudo-diffusion coefficient in the activated muscle.
CONCLUSIONS: Velocity-compensated diffusion gradients significantly suppress the IVIM effect in the calf muscle, indicating that the ballistic limit is mostly reached, which is supported by the time dependence of the pseudo-diffusion coefficient.

OBJECTIVE: To investigate the value of imaging parameters derived from T1 relaxation times in the rotating frame (T1ρ or T1rho), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in assessment of liver fibrosis in rats and propose an optimal diagnostic model based on multiparametric MRI.
METHODS: Thirty rats were divided into one control group and four fibrosis experimental groups (n = 6 for each group). Liver fibrosis was induced by administering thioacetamide (TAA) for 2, 4, 6, and 8 weeks. T1ρ, mean kurtosis (MK), mean diffusivity (MD), perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were measured and compared among different fibrosis stages. An optimal diagnostic model was established and the diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis.
RESULTS: The mean AUC values, sensitivity, and specificity of T1ρ and MD derived from DKI across all liver fibrosis stages were comparable but much higher than those of other imaging parameters (0.954, 92.46, 91.85 for T1ρ; 0.949, 92.52, 91.24 for MD). The model combining T1ρ and MD exhibited better diagnostic performance with higher AUC values than any individual method for staging liver fibrosis (≥ F1: 1.000 (0.884-1.000); ≥ F2: 0.935 (0.782-0.992); ≥ F3: 0.982 (0.852-1.000); F4: 0.986 (0.859-1.000)).
CONCLUSIONS: Among the evaluated imaging parameters, T1ρ and MD were superior for differentiating varying liver fibrosis stages. The model combining T1ρ and MD was promising to be a credible diagnostic biomarker to detect and accurately stage liver fibrosis.