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Background Fiducial markers in image‐guided prostate cancer radiotherapy reduce geometric uncertainty during daily patient setup and enable assessment of target position changes. Diffusion‐weighted magnetic resonance imaging (MRI) for target delineation improves prostate cancer localization, beneficial for intraprostatic focal boost. Artifacts from fiducial markers on prostate diffusion‐weighted MRI (DWI) need to be investigated, as they could be detrimental for target delineation. This study aims to determine the distances of artifact extensions caused by fiducial markers in DWI and in the apparent diffusion coefficient (ADC) maps and to assess how motion and signal‐to‐noise ratio (SNR) influence the artifact size in ADC maps. Materials and methods Three phantoms were used: two homogeneous gel phantoms—one containing three cylindrical gold fiducial markers (GFM) and the other containing three spherical gold anchor (GA) markers—and a third heterogeneous phantom consisting of a piece of sirloin embedded with three GFM and three GA. Diffusion‐weighted images were acquired on a 3T MRI system. The artifacts were analyzed along the phase‐encoding (PE) and frequency‐encoding (FE) directions. Motion was induced and simulated during acquisition, and SNR was varied. The impact of motion and SNR on the artifact extension was evaluated, and the artifact extensions in diffusion images from eight patients were also analyzed. Results The artifacts were smaller in the ADC maps compared to DWI. The largest artifact extension occurred along the PE‐direction. Larger artifact extensions were observed in homogeneous phantom images compared to patient images. In homogeneous phantom images: 13.8  ±  0.4 mm / 9.1  ±  0.4 mm (PE/FE) in DWI with b = 0 s/mm2 and 11.6  ±  0.9 mm / 8.1  ±  0.4 mm (PE/FE) in the ADC map. In patient images: 10.7  ±  1.2 mm / 8.2  ±  1.3 mm (PE/FE) in DWI with b = 0 s/mm2 and 7.3  ±  1.6 mm / 6.8  ±  1.1 mm (PE/FE) in the ADC map. Motion caused larger artifact extensions compared to no motion. A motion of 2 mm increased the artifact from 11.6 ± 0.9 mm / 8.1 ± 0.4 mm (PE/FE) to 14.1 ± 0.8 mm / 9.7 ± 0.4 mm (PE/FE) in homogeneous phantom images and from 10.3 ± 0.8 mm / 8.1 ± 0.4 mm (PE/FE) to 13.1 ± 0.8 mm / 8.4 ± 0.8 mm (PE/FE) in heterogeneous phantom images. Lower SNR resulted in smaller visible artifact extensions. Conclusion This study assessed the distances of artifact extensions in homogeneous phantoms, heterogeneous phantoms, and patient images caused by fiducial markers in DWI and ADC maps. ADC maps had smaller artifact extensions compared to DWI. The artifact extensions were largest in the homogeneous phantom, smaller in the heterogeneous phantom, and the smallest in the patient images. In patient images, the extensions were approximately 7–11 mm (PE) and 7‐8 mm (FE). However, extensions reached up to ∼14 mm (PE) and ∼9 mm (FE) in homogeneous phantom images, suggesting that the true artifact extension may be partially obscured in patient images. Further, motion in images caused larger artifact extensions, and lower SNR caused smaller artifact extensions. The study underlines the need for precise marker placement to avoid obscuring critical anatomical structures, especially for delineation of small boost volumes, and distorting ADC values in quantitative analyses of tumors.

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data‐driven approaches including deep‐learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas‐based direct tract mapping technique called ‘tractfinder’, utilising tract‐specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10–20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro‐anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel‐wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep‐learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics. A rapid atlas‐based direct white matter tract segmentation technique is extensively validated in three different datasets with consistent and strong results. Improved performance and explainability in the presence of pathology is demonstrated over alternatives methods.

ABSTRACT Time‐dependent diffusion MRI (td‐dMRI) has potential in characterizing microstructural features; however, its value in imaging endometrioid endometrial adenocarcinoma (EEA) remains uncertain. Patients surgically confirmed with EEA were finally enrolled in our study. The td‐dMRI data were acquired using pulsed gradient spin echo sequence and oscillating gradient spin echo sequences. The microstructural markers, including cell diameter, intracellular volume fraction (Vin), cellularity, and extracellular diffusivity (Dex), were fitted with the imaging microstructural parameters using a limited spectrally edited diffusion (IMPULSED) model. The parameters were compared between low‐ and high‐risk groups and between low‐ and high‐proliferation groups. The diagnostic performance was evaluated using receiver‐operating characteristic curve and logistic regression analysis. Diameter, Dex, ADCPGSE, ADCN1, and ADCN2 were significantly low, whereas cellularity, ΔADC1 and ΔADC2 were significantly high in the high‐risk and high‐proliferation groups. Cellularity, ΔADC1, and ΔADC2 demonstrated excellent diagnostic efficacy in predicting both risk stratification and proliferation status. Cellularity was the only independent predictor for risk stratification, which exhibited a satisfactory positive correlation with cell density in histopathologic examination. The diagnostic potential of td‐dMRI‐based microstructural mapping was demonstrated to noninvasively probe the pathologic characteristics of patients with EEA in a clinical setting, which provided a valuable contribution to surgical guidance. This work, demonstrating the diagnostic potential of td‐dMRI‐based microstructural mapping in noninvasively probing the pathologic characteristics of patients with EEA in a clinical setting and providing a valuable contribution to surgical guidance, would be of interest to a broad readership in the fields of oncology, preoperative evaluation, and cancer treatment strategies.

As sessile organisms, plants must respond to the environment by adjusting their growth and development. Most of the plant body is formed post-embryonically by continuous activity of apical and lateral meristems. The development of lateral adventitious roots is a complex process, and therefore the development of methods that can visualize, non-invasively, the plant microstructure and organ initiation that occur during growth and development is of paramount importance. In this study, relaxation-based and advanced diffusion magnetic resonance imaging (MRI) methods including diffusion tensor (DTI), q-space diffusion imaging (QSI), and double-pulsed-field-gradient (d-PFG) MRI, at 14.1 T, were used to characterize the hypocotyl microstructure and the microstructural changes that occurred during the development of lateral adventitious roots in tomato. Better contrast was observed in relaxation-based MRI using higher in-plane resolution but this also resulted in a significant reduction in the signal-to-noise ratio of the T2-weighted MR images. Diffusion MRI revealed that water diffusion is highly anisotropic in the vascular cylinder. QSI and d-PGSE MRI showed that in the vascular cylinder some of the cells have sizes in the range of 6–10 μm. The MR images captured cell reorganization during adventitious root formation in the periphery of the primary vascular bundles, adjacent to the xylem pole that broke through the cortex and epidermis layers. This study demonstrates that MRI and diffusion MRI methods allow the non-invasive study of microstructural features of plants, and enable microstructural changes associated with adventitious root formation to be followed.

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture. In the present work, we show that by using a myelin-sensitive measure we can complement the diffusion MRI-based connectivity and provide a different picture of the brain organization. We show that the R1-weighted average distribution does not follow the same trend as the number of streamlines strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions have higher R1-weighted average but fewer streamlines. This could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions rely on connections with higher myelination.

Susceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons. In this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP, and PA data. We found that and offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. and performed well in correcting images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric. We suggest researchers to use or for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. The performance ranking of the various tools inferred from direct and indirect metrics differs slightly. However, across all tools, the results of direct and indirect metrics are highly correlated indicating that the analysis of indirect metrics may provide a good proxy of the performance of a correction tool if assessment using direct metrics is not feasible.

Background: Inherited white matter (WM) disorders of the central nervous systems (CNS), or leukodystrophies, are devastating diseases that primarily affect children, many of whom die early in life or suffer from long-term disability. Methods: q-Space diffusion MR imaging (QSI) and diffusion tensor MR imaging (DTI) with the same resolution and timing parameters were used to study the spinal cords (SCs) of two myelin mutants that are experimental models of WM diseases of different severity, namely the 28-day-old taiep and Long–Evans Shaker (les) rats. The aim was to verify if and which of the diffusion methodologies used is more suitable for early detection of the milder taiep pathology and to characterize its early phase. We also aimed to compare the diffusion MRI results with quantitative electron microscopy (EM) findings. Results: We found that at this early age (28 days), both QSI and DTI were able to detect the severe les WM pathology, while the milder WM pathology in the SC of the taiep rats was detected only by QSI. An increase in the mean radial displacement (RaDis), representing the MRI axon diameter (AD), and a decrease in the probability for zero displacement (PZD) were observed in the dorsal column (ROI 1) of the taiep SCs. In other WM areas, the same trends were observed but the differences were not of statistical significance. In DTI, we found some lower fractional anisotropy (FA) values in the taiep SCs compared to the controls; however, these differences were not statistically significant. For the more severe les pathology, we observed a dramatic increase in the RaDis values and a large decrease in PZD values in all ROIs examined. There, even the FA values were lower than that of the control SCs in all ROIs, albeit with much smaller statistical significance. These MRI results, which show a higher detectability of WM pathology with heavier diffusion weighting, followed histological findings that showed significant myelin deficiency in the dorsal column in the taiep SCs and a practically complete myelin loss in all WM areas in the les SCs. This study also revealed that, under the experimental conditions used here, the apparent increase in RaDis agrees better with myelin thickness and not with average AD extracted form EM, probably reflecting the effect of water exchange. Conclusions: These results, corroborated by diffusion time-dependent QSI, also imply that while diffusion MRI in general and QSI in particular provide acceptable apparent axon diameter estimations in heathy and mature WM, this appears not to be the case in severely damaged WM where exchange appears to play a more important role.

Desmoid tumor of the breast (DTB), also referred to as desmoid-type fibromatosis, is a rare tumor with an unknown etiology, imaging findings of which are often confused with breast cancer. The aim of this report is to present a case of DTB with its clinical, radiological, and pathological features. A 42-year-old woman with Turner syndrome, who had undergone bilateral saline-filled breast implantation surgery, presented with a complaint of a lump in her left breast. The patient underwent mammography (MG), ultrasound (US), and dynamic contrast-enhanced magnetic resonance imaging (MRI). The MG examination revealed afocal asymmetric density in the upper middle quadrant of the left breast. A core biopsy was performed on the irregularly shaped spiculated mass identified on US, and the results indicated either cellular fibroadenoma or a phyllodes tumor. On MRI, the mass was hypointense on T1-weighted and short tau inversion recovery (STIR) sequences. The dynamic MRI scan revealed a type 1 enhancement curve, while the apparent diffusion coefficient (ADC), fractional anisotropy (FA) and mean diffusivity (MD) values obtained from diffusion and diffusion tensor imaging were similar to those of the surrounding parenchyma. The mass was removed while preserving the implant, and the pathology results confirmed a diagnosis of DTB.The pathology report indicated microscopic proximity to the cauterized edge, but there was no evidence of recurrent mass detected on the follow-up US performed six months later. This is the first case of DTB reported in a patient with Turner syndrome. DTB is a rare tumor that can often be confused with breast cancer based on clinical and radiological findings, and it may not be accurately diagnosed by core biopsy alone. In cases where a clean surgical margin cannot be achieved, close follow-up and intervention may be considered when recurrence is observed.

In this article, we consider how differing approaches that characterize biological microstructure with diffusion weighted magnetic resonance imaging intersect. Without geometrical boundary assumptions, there are techniques that make use of power law behavior which can be derived from a generalized diffusion equation or intuited heuristically as a time dependent diffusion process. Alternatively, by treating biological microstructure (e.g., myelinated axons) as an amalgam of stick-like geometrical entities, there are approaches that can be derived utilizing convolution-based methods, such as the spherical means technique. Since data acquisition requires that multiple diffusion weighting sensitization conditions or b-values are sampled, this suggests that implicit mutual information may be contained within each technique. The information intersection becomes most apparent when the power law exponent approaches a value of 12, whereby the functional form of the power law converges with the explicit stick-like geometric structure by way of confluent hypergeometric functions. While a value of 12 is useful for the case of solely impermeable fibers, values that diverge from 12 may also reveal deep connections between approaches, and potentially provide insight into the presence of compartmentation, exchange, and permeability within heterogeneous biological microstructures. All together, these disparate approaches provide a unique opportunity to more completely characterize the biological origins of observed changes to the diffusion attenuated signal.

Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion‐weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model—diffusion tensor imaging (DTI)—and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease. We rigorously characterized developmental sex differences in white matter microstructure using both conventional (diffusion tensor imaging [DTI]) and advanced (restriction spectrum imaging [RSI]) diffusion‐weighted MRI models among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Significant, replicable, and robust sex differences in DTI or RSI microstructure metrics were observed in every white matter region examined across the brain. These robust and replicable findings provide an important foundation for understanding sex differences in health and disease.