Magnetic resonance elastography (MRE) is widely recognized as the most accurate, non-invasive imaging technique for evaluating liver fibrosis. However, MRE requires dedicated hardware, including an external vibration device, which limits its availability. Virtual MRE (VMRE) has recently emerged as a promising alternative technique in which tissue stiffness is estimated using diffusion-weighted imaging data without the need for external mechanical vibrations. VMRE is based on the hypothesis that tissue elasticity and water diffusivity share common mechanical features at the microstructural level. By calculating the shift in the apparent diffusion coefficient from the optimized b-values (200 and 1,500 s/mm²), the virtual stiffness can be derived and calibrated against the MRE-derived stiffness. Studies in cohorts with chronic viral hepatitis have demonstrated strong correlations between VMRE and MRE findings, with both techniques showing good agreement in liver fibrosis staging. Although the utility of VMRE in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) was previously considered debatable because of the confounding effects of steatosis, recent evidence suggests that VMRE performed using an optimized fat-correction method combined with MASLD-specific b-values (200 and 1,200 s/mm²) can achieve diagnostic performance comparable to that of MRE. Beyond liver fibrosis assessment, the combination of VMRE and MRE has shown potential for distinguishing hepatocellular carcinoma from metastatic liver cancers. This article provides a comprehensive overview of VMRE, including its technical basis and current clinical applications in chronic liver diseases and focal liver lesions.

OBJECTIVE:To evaluate the potential of the signature index (S-index) and the shifted apparent diffusion coefficient (sADC), both markers of non-Gaussian diffusion, for the diagnosis of prostate cancer. METHODS:This retrospective study included 180 patients with 264 target lesions (95 cancerous) who underwent MRI-US fusion-guided prostate biopsy. Multiparametric MRI, including diffusion-weighted imaging with three b values (0, 200, and 1500 s/mm2), was performed to calculate sADC and the S-index. The diagnostic performances of these biomarkers were assessed using ROC analyses and compared with PI-RADS. RESULTS:The global S-index and its 85th percentile values for clinically significant cancers (ISUP ≥ 2) in both the peripheral zone (PZ: 103.0 ± 30.2 and 139.4 ± 31.6) and the transition zone (TZ: 106.6 ± 32.3 and 142.2 ± 33.9) were significantly higher (p < 0.001) than those for all other lesions and normal tissue (PZ: 45.7 ± 27.9 and 71.1 ± 38.1; TZ: 67.6 ± 25.4 and 93.0 ± 30.5). In the PZ, the S-index (global and 85th percentile) and sADC showed higher diagnostic performance for classifying clinically significant cancers (AUC = 0.92 [0.87-0.95], 0.90 [0.86-0.94], and 0.92 [0.87-0.95], respectively) compared with PI-RADS alone (AUC = 0.70 [0.59-0.70]), whereas performance in the TZ was similar across markers (AUC ∼ 0.82). The reduction in unnecessary biopsies for PI-RADS > 3 lesions in the PZ was 22.4% with sADC and 21% with the S-index. In the TZ, the highest reduction rate was achieved with the S-index 85th percentile (9.5%). CONCLUSION:The S-index and other diffusion MRI biomarkers demonstrate strong performance for the classification of clinically significant prostate cancer and may represent an important step toward enhancing the clinical value of MRI for improving prostate cancer management.

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.

Music, though not biologically essential for survival, engages the brain as a whole-system phenomenon. Neuroimaging studies show that music perception and performance recruit distributed networks involving auditory, motor, emotional, memory, and reward circuits, partially distinct from language systems. Musical training induces measurable structural plasticity in gray and white matter, particularly when begun early in life, though excessive repetition may lead to maladaptive changes such as focal dystonia. Music performance relies on multimodal integration and predictive coding, with mental imagery activating neural patterns similar to actual playing. Live music further enhances emotional and social neural coupling. By engaging ancient reward pathways, music demonstrates how abstract sound structures can reshape brain anatomy, synchronize networks, and evoke powerful emotional experiences.

Alzheimer's disease (AD) is usually framed as a proteinopathy and network disorder, but this view may be incomplete. We propose a mechanobiological hypothesis in which synaptic micromechanics, regional brain softening, vascular pulsatility, and glymphatic transport are parts of a coupled fluid-solid system whose failure contributes to AD progression. In this framework, early synaptic and glial mechanical fragility reduces the capacity of vulnerable circuits to maintain stable structure, efficient signaling, and waste clearance, while age-related tissue softening and impaired perivascular transport amplify amyloid and tau accumulation, network dysfunction, and cognitive decline. This framework integrates converging evidence from dendritic spine to glymphatic system biology, concordant results obtained with diffusion MRI and magnetic resonance elastography, and treats altered tissue mechanics not merely as a correlate of degeneration but as a potentially active multicomponent of disease expression. It further predicts that biomechanical alterations should be detectable before gross atrophy, should covary with glymphatic impairment, and may help explain why molecular pathology and clinical symptoms are often only partly aligned. By positioning brain mechanics as an interface between protein aggregation, synaptic dysfunction, and impaired clearance, this framework identifies testable imaging biomarkers and suggests potential early-stage intervention strategies aimed at preserving tissue resilience as well as reducing pathological protein burden.

Diffusion-weighted imaging (DWI) offers critical insights into tissue microstructure through the assessment of water molecule random displacements and plays a central role in the assessment of neoplastic and non-neoplastic diseases. To successfully implement and use DWI in clinical practice, guidelines for acquisition, interpretation of image contrast and of artefacts should be followed, taking the disease process and body part into account. We recommend covering a b-value range of 0–1000 s/mm2 in the brain (along at least six directions for white matter), and 50–800 s/mm2 in the body. Available acquisition acceleration options should be used to reduce repetition time (TR), echo time (TE), and echo-planar imaging (EPI) distortions, while considering the penalty in signal-to-noise ratio (SNR) and image sharpness. DW images and the apparent diffusion coefficient (ADC) map should be read jointly for the clinical interpretation. Areas of slower diffusion are hyperintense on DW images and hypointense on the ADC map, and vice versa. Magnetic susceptibility distortions and signal drop-outs or pile-ups are particularly pronounced at air-tissue or metal-tissue interfaces and may obscure areas of interest or hinder the co-localisation with structural scans. By following these guidelines and recommendations, radiologists and imaging professionals can enhance diagnostic accuracy, reduce variability, and maximise the clinical value of DWI across diverse applications.

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts. Another major advantage of ex vivo dMRI is the direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We give general considerations and foundational knowledge that must be considered when designing experiments. We describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

This study compared the diagnostic performance of diffusion biomarkers estimated from an abbreviated diffusion-weighted imaging (DWI) protocol and assessed their potential to reduce unnecessary biopsies of benign BI-RADS 4 lesions identified on dynamic contrast-enhanced (DCE) MRI. A retrospective study was conducted from 2019 to 2023. All patients underwent abbreviated DWI at 3 T with four b-values (0 s/mm2, 200 s/mm2, 800 s/mm2, and 1500 s/mm2). Regions of interest were manually placed on DWI, and biomarkers, including the apparent diffusion coefficient (ADC0–800), perfusion fraction intravoxel incoherent motion, non-Gaussian diffusion (ADC0 and kurtosis [K]), signature index (S-index), and shifted ADC (sADC), were estimated. Diagnostic performance and the potential to reduce unnecessary biopsies were evaluated for each parameter. In total, 168 female patients (mean age ± standard deviation, 56.2 ± 13.5 years) with 178 BI-RADS 4 lesions on DCE MRI were analyzed. The median ADC0–800, sADC, and ADC0 were significantly lower in malignant lesions, while S-index and K were significantly higher (all p ≤ 0.001). The diagnostic performance to reclassify lesions as benign or malignant was identical for ADC0–800 (area under the curve = 0.67), sADC (0.69), S-index (0.69), ADC0 (0.68), and K (0.66). Applying an ad-hoc threshold cutoff, all parameters reduced unnecessary biopsies (around 16

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

Objective To assess the possible influence of third-order shim coils on the behavior of the gradient field and in gradient–magnet interactions at 7 T and above. Materials and methods Gradient impulse response function measurements were performed at 5 sites spanning field strengths from 7 to 11.7 T, all of them sharing the same exact whole-body gradient coil design. Mechanical fixation and boundary conditions of the gradient coil were altered in several ways at one site to study the impact of mechanical coupling with the magnet on the field perturbations. Vibrations, power deposition in the He bath, and field dynamics were characterized at 11.7 T with the third-order shim coils connected and disconnected inside the Faraday cage. Results For the same whole-body gradient coil design, all measurements differed greatly based on the third-order shim coil configuration (connected or not). Vibrations and gradient transfer function peaks could be affected by a factor of 2 or more, depending on the resonances. Disconnecting the third-order shim coils at 11.7 T also suppressed almost completely power deposition peaks at some frequencies. Discussion Third-order shim coil configurations can have major impact in gradient–magnet interactions with consequences on potential hardware damage, magnet heating, and image quality going beyond EPI acquisitions.

Purpose: We aimed to investigate the changes in intravoxel incoherent motion (IVIM) and diffusion parameters between in vivo and post-mortem conditions and the time dependency of these parameters using two different mouse tumor models with different vessel lumen sizes. Methods: Six B16 and six MDA-MB-231 xenograft mice were scanned using 7 Tesla MRI under both in vivo / post-mortem conditions. Diffusion weighted imaging with 17 b-values (0 - 3000 s/mm(2)) were obtained at two diffusion times (9 and 27.6 ms). The shifted apparent diffusion coefficient (sADC) using 2 b-values (200 and 1500 s/mm(2)), non-Gaussian diffusion and IVIM parameters (ADC(0) , K, f IVIM ) were estimated at each of the diffusion times. The results were evaluated by repeated measures two-way analysis of variance and post hoc Bonferroni test. Results: In B16 tumors, f IVIM significantly decreased with post-mortem conditions (from 12.6 +/- 6.5% to 5.2 +/- 1.9%, P < 0.05 at long diffusion time; from 11.0 +/- 2.4% to 4.6 +/- 2.7%, P < 0.05 at short diffusion time). In MDA-MB-231 tumors, f IVIM also significantly decreased (from 8.8 +/- 3.8% to 2.6 +/- 1.1%, P < 0.05 at long; from 7.9 +/- 5.4% to 2.9 +/- 1.1%, P < 0.05 at short). No diffusion time dependency was observed ( P = 0.59 in B16 and P = 0.77 in MDA-MB-231). The sADC and ADC 0 values tended to decrease and the K value tended to increase after sacrificing and when increasing the diffusion time. Conclusion: The f IVIM values dropped after sacrificing, confirming that IVIM MRI is a promising quantitative parameter to evaluate blood microcirculation. The presence of residual post-mortem f IVIM values suggested that the influence of water molecule diffusion in the blood lumen may contribute to the IVIM effect. Diffusion MRI parameter ' s time dependency and those changes after sacrificing could possibly provide additional insights into diffusion hindrance mechanisms.

Diffusion MRI was introduced in 1985, showing how the diffusive motion of molecules, especially water, could be spatially encoded with MRI to produce images revealing the underlying structure of biologic tissues at a microscopic scale. Diffusion is one of several Intravoxel Incoherent Motions (IVIM) accessible to MRI together with blood microcirculation. Diffusion imaging first revolutionized the management of acute cerebral ischemia by allowing diagnosis at an acute stage when therapies can still work, saving the outcomes of many patients. Since then, the field of diffusion imaging has expanded to the whole body, with broad applications in both clinical and research settings, providing insights into tissue integrity, structural and functional abnormalities from the hindered diffusive movement of water molecules in tissues. Diffusion imaging is particularly used to manage many neurologic disorders and in oncology for detecting and classifying cancer lesions, as well as monitoring treatment response at an early stage. The second major impact of diffusion imaging concerns the wiring of the brain (Diffusion Tensor Imaging, DTI), allowing to obtain from the anisotropic movement of water molecules in the brain white-matter images in 3 dimensions of the brain connections making up the Connectome. DTI has opened up new avenues of clinical diagnosis and research to investigate brain diseases, neurogenesis and aging, with a rapidly extending field of application in psychiatry, revealing how mental illnesses could be seen as Connectome spacetime disorders. Adding that water diffusion is closely associated to neuronal activity, as shown from diffusion fMRI, one may consider that diffusion MRI is ideally suited to investigate both brain structure and function. This article retraces the early days and milestones of diffusion MRI which spawned over 40 years, showing how diffusion MRI emerged and expanded in the research and clinical fields, up to become a pillar of modern clinical imaging.

Very high-resolution images of the human brain obtained in vivo in a few minutes with MRI at an ultra-high magnetic field of 11.7 T reveal exquisite details. Biological and behavioral tests confirm the safety of the method, opening the door for human brain exploration at mesoscale resolution.

Fat-signal suppression is essential for breast diffusion magnetic resonance imaging (or diffusion-weighted MRI, DWI) as the very low diffusion coefficient of fat tends to decrease absolute diffusion coefficient (ADC) values. Among several methods, the STIR (short-tau inversion recovery) method is a popular approach, but signal suppression/attenuation is not specific to fat contrary to other methods such as SPAIR (spectral adiabatic (or attenuated) inversion recovery). This article focuses on those two techniques to illustrate the importance of appropriate fat suppression in breast DWI, briefly presenting the pros and cons of both approaches. We show here through simulation and data acquired in a dedicated breast DWI phantom made of vials with water and various concentrations of polyvinylpyrrolidone (PVP) how ADC values obtained with STIR DWI may be biased toward tissue components with the longest T1 values: ADC values obtained with STIR fat suppression may be over/underestimated depending on the T1 and ADC profile within tissues. This bias is also illustrated in two clinical examples. Fat-specific methods should be preferred over STIR for fat-signal suppression in breast DWI, such as SPAIR which also provides a higher sensitivity than STIR for lesion detection. One should remain aware, however, that efficient fat-signal suppression with SPAIR requires good B0 shimming to avoid ADC underestimation from residual fat contamination. The spectral adiabatic (or attenuated) inversion recovery (SPAIR) method should be preferred over short-tau inversion recovery (STIR) for fat suppression in breast DWI.

The understanding of the human brain is one of the main scientific challenges of the twenty-first century. In the early 2000s, the French Atomic Energy Commission launched a program to conceive and build a human magnetic resonance imaging scanner operating at 11.7 T. We have now acquired human brain images in vivo at such a magnetic field. We deployed parallel transmission tools to mitigate the radiofrequency field inhomogeneity problem and tame the specific absorption rate. The safety of human imaging at such high field strength was demonstrated using physiological, vestibular, behavioral and genotoxicity measurements on the imaged volunteers. Our technology yields T2 and T2*-weighted images reaching mesoscale resolutions within short acquisition times and with a high signal and contrast-to-noise ratio. In a technological tour de force, a whole-body 11.7-T MRI scanner has been developed. Here images of the human brain are presented while safety for the imaged human volunteers has been ascertained.

Chapter 5 Functional MRI Laura Adela HARSAN, Laura Adela HARSAN ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorLaetitia DEGIORGIS, Laetitia DEGIORGIS ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorMarion SOURTY, Marion SOURTY ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorÉléna CHABRAN, Éléna CHABRAN ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorDenis LE BIHAN, Denis LE BIHAN NeuroSpin, CEA, Gif-sur-Yvette, FranceSearch for more papers by this author Laura Adela HARSAN, Laura Adela HARSAN ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorLaetitia DEGIORGIS, Laetitia DEGIORGIS ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorMarion SOURTY, Marion SOURTY ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorÉléna CHABRAN, Éléna CHABRAN ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this authorDenis LE BIHAN, Denis LE BIHAN NeuroSpin, CEA, Gif-sur-Yvette, FranceSearch for more papers by this author Hélène Ratiney, Hélène RatineySearch for more papers by this authorOlivier Beuf, Olivier BeufSearch for more papers by this author Book Author(s):Hélène Ratiney, Hélène RatineySearch for more papers by this authorOlivier Beuf, Olivier BeufSearch for more papers by this author First published: 19 April 2024 https://doi.org/10.1002/9781394284030.ch5 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary This chapter presents the basics of the blood oxygenation level dependent (BOLD) signal's origin and describes how the variations of this signal are related to transient changes in neuronal activity. It also presents the principles of functional connectivity mapping based on resting state functional magnetic resonance imaging (fMRI) data modeling and examples of clinical applications and translational animal studies. The temporal resolution of BOLD fMRI is intrinsically limited by the physiological delay in triggering the vascular response after the activation. Tractography based on diffusion tensor MRI is an indispensable tool for interpreting functional MRI data and determining the anatomical connection networks behind cognitive processes. Water is fundamental for life and its molecular diffusion naturally occurs outside MRI magnets. Sensitivity is described as the capability of BOLD fMRI to reveal brain activity with an increase in neuronal activity. Diffusion MRI provides magnificent maps of brain connections, in color and in three dimensions. References Abe , Y. , Van Nguyen , K. , Tsurugizawa , T. , Ciobanu , L. , Le Bihan , D. ( 2017a ). 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