FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

Improved reconstruction of crossing fibers in the mouse optic pathways with orientation distribution function fingerprinting.

PURPOSE: The accuracy of diffusion MRI tractography reconstruction decreases in the white matter regions with crossing fibers. The optic pathways in rodents provide a challenging structure to test new diffusion tractography approaches because of the small crossing volume within the optic chiasm and the unbalanced 9:1 proportion between the contra- and ipsilateral neural projections from the retina to the lateral geniculate nucleus, respectively. METHODS: Common approaches based on Orientation Distribution Function (ODF) peak finding or statistical inference were compared qualitatively and quantitatively to ODF Fingerprinting (ODF-FP) for reconstruction of crossing fibers within the optic chiasm using in vivo diffusion MRI ( n = 18 $$ n=18 $$ healthy C57BL/6 mice). Manganese-Enhanced MRI (MEMRI) was obtained after intravitreal injection of manganese chloride and used as a reference standard for the optic pathway anatomy. RESULTS: ODF-FP outperformed by over 100% all the tested methods in terms of the ratios between the contra- and ipsilateral segments of the reconstructed optic pathways as well as the spatial overlap between tractography and MEMRI. CONCLUSION: In this challenging model system, ODF-Fingerprinting reduced uncertainty of diffusion tractography for complex structural formations of fiber bundles.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging.

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems.

Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40and80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1-4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.

Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model.

PURPOSE: Orientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier. METHODS: A randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images. RESULTS: ODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature. CONCLUSION: The modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.

Three-Dimensional Printed Anatomic Models Derived From Magnetic Resonance Imaging Data: Current State and Image Acquisition Recommendations for Appropriate Clinical Scenarios.

Three-dimensional (3D) printing technologies have been increasingly utilized in medicine over the past several years and can greatly facilitate surgical planning thereby improving patient outcomes. Although still much less utilized compared to computed tomography (CT), magnetic resonance imaging (MRI) is gaining traction in medical 3D printing. The purpose of this study was two-fold: 1) to determine the prevalence in the existing literature of using MRI to create 3D printed anatomic models for surgical planning and 2) to provide image acquisition recommendations for appropriate clinical scenarios where MRI is the most suitable imaging modality. The workflow for creating 3D printed anatomic models from medical imaging data is complex and involves image segmentation of the regions of interest and conversion of that data into 3D surface meshes, which are compatible with printing technologies. CT is most commonly used to create 3D printed anatomic models due to the high image quality and relative ease of performing image segmentation from CT data. As compared to CT datasets, 3D printing using MRI data offers advantages since it provides exquisite soft tissue contrast needed for accurate organ segmentation and it does not expose patients to unnecessary ionizing radiation. MRI, however, often requires complicated imaging techniques and time-consuming postprocessing procedures to generate high-resolution 3D anatomic models needed for 3D printing. Despite these challenges, 3D modeling and printing from MRI data holds great clinical promises thanks to emerging innovations in both advanced MRI imaging and postprocessing techniques. EVIDENCE LEVEL: 2 TECHNICAL EFFICATCY: 5.

Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.

Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.

Cortical and subcortical signatures of conscious object recognition.

The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network. For recognized images, object category information can be decoded from all of the involved cortical networks but not from subcortical regions. Phase-scrambled images trigger strong involvement of inferior frontal junction, anterior cingulate cortex and default-mode network, implicating these regions in inferential processing under increased uncertainty. Our results indicate that content-specific activity in both activated and deactivated cortical networks and non-content-specific subcortical activity support conscious recognition.

Global decrease in brain sodium concentration after mild traumatic brain injury.

The pathological cascade of tissue damage in mild traumatic brain injury is set forth by a perturbation in ionic homeostasis. However, whether this class of injury can be detected in vivo and serve as a surrogate marker of clinical outcome is unknown. We employ sodium MRI to test the hypotheses that regional and global total sodium concentrations: (i) are higher in patients than in controls and (ii) correlate with clinical presentation and neuropsychological function. Given the novelty of sodium imaging in traumatic brain injury, effect sizes from (i), and correlation types and strength from (ii), were compared to those obtained using standard diffusion imaging metrics. Twenty-seven patients (20 female, age 35.9 ± 12.2 years) within 2 months after injury and 19 controls were scanned with proton and sodium MRI at 3 Tesla. Total sodium concentration, fractional anisotropy and apparent diffusion coefficient were obtained with voxel averaging across 12 grey and white matter regions. Linear regression was used to obtain global grey and white matter total sodium concentrations. Patient outcome was assessed with global functioning, symptom profiles and neuropsychological function assessments. In the regional analysis, there were no statistically significant differences between patients and controls in apparent diffusion coefficient, while differences in sodium concentration and fractional anisotropy were found only in single regions. However, for each of the 12 regions, sodium concentration effect sizes were uni-directional, due to lower mean sodium concentration in patients compared to controls. Consequently, linear regression analysis found statistically significant lower global grey and white matter sodium concentrations in patients compared to controls. The strongest correlation with outcome was between global grey matter sodium concentration and the composite z-score from the neuropsychological testing. In conclusion, both sodium concentration and diffusion showed poor utility in differentiating patients from controls, and weak correlations with clinical presentation, when using a region-based approach. In contrast, sodium linear regression, capitalizing on partial volume correction and high sensitivity to global changes, revealed high effect sizes and associations with patient outcome. This suggests that well-recognized sodium imbalances in traumatic brain injury are (i) detectable non-invasively; (ii) non-focal; (iii) occur even when the antecedent injury is clinically mild. Finally, in contrast to our principle hypothesis, patients' sodium concentrations were lower than controls, indicating that the biological effect of traumatic brain injury on the sodium homeostasis may differ from that in other neurological disorders. Note: This figure has been annotated.

Mapping brain-behavior networks using functional and structural connectome fingerprinting in the HCP dataset.

INTRODUCTION: Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets. MATERIALS AND METHODS: In the present study, we extend connectome fingerprinting from functional connectivity (FC) to structural connectivity (SC), identifying multiple relationships between behavioral traits and brain connectivity. Higher-order neurocognitive tasks were found to have a weaker association with structural connectivity than its functional connectivity counterparts. RESULTS: Neurocognitive tasks with a higher sensory footprint were, however, found to have a stronger association with structural connectivity than their functional connectivity counterparts. Language behavioral measurements had a particularly stronger correlation, especially between performance on the picture language test (Pic Vocab) and both FC (r = .28, p < .003) and SC (r = 0.27, p < .00077). CONCLUSIONS: At the neural level, we found that the pattern of structural brain connectivity related to high-level language performance is consistent with the language white matter regions identified in presurgical mapping. We illustrate how this approach can be used to generalize the connectome fingerprinting framework to structural connectivity and how this can help understand the connections between cognitive behavior and the white matter connectome of the brain.

Fingerprinting Orientation Distribution Functions in diffusion MRI detects smaller crossing angles.

Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.

Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder.

A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.

Low Rank plus Sparse decomposition of ODFs for improved detection of group-level differences and variable correlations in white matter.

A novel approach is presented for group statistical analysis of diffusion weighted MRI datasets through voxelwise Orientation Distribution Functions (ODF). Recent advances in MRI acquisition make it possible to use high quality diffusion weighted protocols (multi-shell, large number of gradient directions) for routine in vivo study of white matter architecture. The dimensionality of these data sets is however often reduced to simplify statistical analysis. While these approaches may detect large group differences, they do not fully capitalize on all acquired image volumes. Incorporation of all available diffusion information in the analysis however risks biasing the outcome by outliers. Here we propose a statistical analysis method operating on the ODF, either the diffusion ODF or fiber ODF. To avoid outlier bias and reliably detect voxelwise group differences and correlations with demographic or behavioral variables, we apply the Low-Rank plus Sparse (L+S) matrix decomposition on the voxelwise ODFs which separates the sparse individual variability in the sparse matrix S whilst recovering the essential ODF features in the low-rank matrix L. We demonstrate the performance of this ODF L+S approach by replicating the established negative association between global white matter integrity and physical obesity in the Human Connectome dataset. The volume of positive findings p<0.01,227cm3, agrees with and expands on the volume found by TBSS (17 cm3), Connectivity based fixel enhancement (15 cm3) and Connectometry (212 cm3). In the same dataset we further localize the correlations of brain structure with neurocognitive measures such as fluid intelligence and episodic memory. The presented ODF L+S approach will aid in the full utilization of all acquired diffusion weightings leading to the detection of smaller group differences in clinically relevant settings as well as in neuroscience applications.

Accelerated radial diffusion spectrum imaging using a multi-echo stimulated echo diffusion sequence.

PURPOSE: Diffusion spectrum imaging (DSI) provides us non-invasively and robustly with anatomical details of brain microstructure. To achieve sufficient angular resolution, DSI requires a large number of q-space samples, leading to long acquisition times. This need is mitigated here by combining the beneficial properties of Radial q-space sampling for DSI with a Multi-Echo Stimulated Echo Sequence (MESTIM). METHODS: Full 2D k-spaces for each of several q-space samples, along the same radially outward line in q-space, are acquired in one readout train with one spin and three stimulated echoes. RF flip angles are carefully chosen to distribute spin magnetization over the echoes and the DSI reconstruction is adapted to account for differences in diffusion time among echoes. RESULTS: Individual datasets and bootstrapped reproducibility analysis demonstrate image quality and SNR of the more-than-twofold-accelerated RDSI MESTIM sequence. Orientation distribution functions (ODF) and tractography results benefit from the longer diffusion times of the latter echoes in the echo train. CONCLUSION: A MESTIM sequence can be used to shorten RDSI acquisition times significantly without loss of image or ODF quality. Further acceleration is possible by combination with simultaneous multi-slice techniques. Magn Reson Med 79:306-316, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Validation of surface-to-volume ratio measurements derived from oscillating gradient spin echo on a clinical scanner using anisotropic fiber phantoms.

A diffusion measurement in the short-time surface-to-volume ratio (S/V) limit (Mitra et al., Phys Rev Lett. 1992;68:3555) can disentangle the free diffusion coefficient from geometric restrictions to diffusion. Biophysical parameters, such as the S/V of tissue membranes, can be used to estimate microscopic length scales non-invasively. However, due to gradient strength limitations on clinical MRI scanners, pulsed gradient spin echo (PGSE) measurements are impractical for probing the S/V limit. To achieve this limit on clinical systems, an oscillating gradient spin echo (OGSE) sequence was developed. Two phantoms containing 10 fiber bundles, each consisting of impermeable aligned fibers with different packing densities, were constructed to achieve a range of S/V values. The frequency-dependent diffusion coefficient, D(ω), was measured in each fiber bundle using OGSE with different gradient waveforms (cosine, stretched cosine, and trapezoidal), while D(t) was measured from PGSE and stimulated-echo measurements. The S/V values derived from the universal high-frequency behavior of D(ω) were compared against those derived from quantitative proton density measurements using single spin echo (SE) with varying echo times, and from magnetic resonance fingerprinting (MRF). S/V estimates derived from different OGSE waveforms were similar and demonstrated excellent correlation with both SE- and MRF-derived S/V measures (ρ ≥ 0.99). Furthermore, there was a smoother transition between OGSE frequency f and PGSE diffusion time when using teffS/V=9/64f, rather than the commonly used teff = 1/(4f), validating the specific frequency/diffusion time conversion for this regime. Our well-characterized fiber phantom can be used for the calibration of OGSE and diffusion modeling techniques, as the S/V ratio can be measured independently using other MR modalities. Moreover, our calibration experiment offers an exciting perspective of mapping tissue S/V on clinical systems.

Radial q-space sampling for DSI.

PURPOSE: Diffusion spectrum imaging (DSI) has been shown to be an effective tool for noninvasively depicting the anatomical details of brain microstructure. Existing implementations of DSI sample the diffusion encoding space using a rectangular grid. Here we present a different implementation of DSI whereby a radially symmetric q-space sampling scheme for DSI is used to improve the angular resolution and accuracy of the reconstructed orientation distribution functions. METHODS: Q-space is sampled by acquiring several q-space samples along a number of radial lines. Each of these radial lines in q-space is analytically connected to a value of the orientation distribution functions at the same angular location by the Fourier slice theorem. RESULTS: Computer simulations and in vivo brain results demonstrate that radial diffusion spectrum imaging correctly estimates the orientation distribution functions when moderately high b-values (4000 s/mm2) and number of q-space samples (236) are used. CONCLUSION: The nominal angular resolution of radial diffusion spectrum imaging depends on the number of radial lines used in the sampling scheme, and only weakly on the maximum b-value. In addition, the radial analytical reconstruction reduces truncation artifacts which affect Cartesian reconstructions. Hence, a radial acquisition of q-space can be favorable for DSI. Magn Reson Med 76:769-780, 2016. © 2015 Wiley Periodicals, Inc.

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors.

PURPOSE: To examine heterogeneous breast cancer through intravoxel incoherent motion (IVIM) histogram analysis. MATERIALS AND METHODS: This HIPAA-compliant, IRB-approved retrospective study included 62 patients (age 48.44 ± 11.14 years, 50 malignant lesions and 12 benign) who underwent contrast-enhanced 3 T breast MRI and diffusion-weighted imaging. Apparent diffusion coefficient (ADC) and IVIM biomarkers of tissue diffusivity (Dt), perfusion fraction (fp), and pseudo-diffusivity (Dp) were calculated using voxel-based analysis for the whole lesion volume. Histogram analysis was performed to quantify tumour heterogeneity. Comparisons were made using Mann-Whitney tests between benign/malignant status, histological subtype, and molecular prognostic factor status while Spearman's rank correlation was used to characterize the association between imaging biomarkers and prognostic factor expression. RESULTS: The average values of the ADC and IVIM biomarkers, Dt and fp, showed significant differences between benign and malignant lesions. Additional significant differences were found in the histogram parameters among tumour subtypes and molecular prognostic factor status. IVIM histogram metrics, particularly fp and Dp, showed significant correlation with hormonal factor expression. CONCLUSION: Advanced diffusion imaging biomarkers show relationships with molecular prognostic factors and breast cancer malignancy. This analysis reveals novel diagnostic metrics that may explain some of the observed variability in treatment response among breast cancer patients. KEY POINTS: • Novel IVIM biomarkers characterize heterogeneous breast cancer. • Histogram analysis enables quantification of tumour heterogeneity. • IVIM biomarkers show relationships with breast cancer malignancy and molecular prognostic factors.