PURPOSE: To accelerate the acquisition of relaxation-diffusion imaging by integrating time-division multiplexing (TDM) with simultaneous multi-slice (SMS) for EPI and evaluate imaging quality and diffusion measures. METHODS: The time-division multiplexing (TDM) technique and SMS method were integrated to achieve a high slice-acceleration (e.g., 6×) factor for acquiring relaxation-diffusion MRI. Two variants of the sequence, referred to as TDM3e-SMS and TDM2s-SMS, were developed to simultaneously acquire slice groups with three distinct TEs and two slice groups with the same TE, respectively. Both sequences were evaluated on a 3T scanner with in vivo human brains and compared with standard single-band (SB) -EPI and SMS-EPI using diffusion measures and tractography results. RESULTS: Experimental results showed that the TDM3e-SMS sequence with total slice acceleration of 6 (multiplexing factor (MP) = 3 × multi-band factor (MB) = 2) provided similar image intensity and microstructure measures compared to standard SMS-EPI with MB = 2, and yielded less bias in intensity compared to standard SMS-EPI with MB = 4. The three sequences showed a similar positive correlation between TE and mean kurtosis (MK) and a negative correlation between TE and mean diffusivity (MD) in white matter. Multi-fiber tractography also shows consistency of results in TE-dependent measures between different sequences. The TDM2s-SMS sequence (MP = 2, MB = 2) also provided imaging measures similar to standard SMS-EPI sequences (MB = 2) for single-TE diffusion imaging. CONCLUSIONS: The TDM-SMS sequence can provide additional 2x to 3x acceleration to SMS without degrading imaging quality. With the significant reduction in scan time, TDM-SMS makes joint relaxation-diffusion MRI a feasible technique in neuroimaging research to investigate new markers of brain disorders.

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain’s white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain’s structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain’s structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain’s white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Matrix metalloproteinases 9 (MMP9) are enzymes involved in regulating neuroplasticity in the hippocampus. This, combined with evidence for disrupted hippocampal structure and function in schizophrenia, has prompted our current investigation into the relationship between MMP9 and hippocampal volumes in schizophrenia. 34 healthy individuals (mean age = 32.50, male = 21, female = 13) and 30 subjects with schizophrenia (mean age = 33.07, male = 19, female = 11) underwent a blood draw and T1-weighted magnetic resonance imaging. The hippocampus was automatically segmented utilizing FreeSurfer. MMP9 plasma levels were measured with ELISA. ANCOVAs were conducted to compare MMP9 plasma levels (corrected for age and sex) and hippocampal volumes between groups (corrected for age, sex, total intracranial volume). Spearman correlations were utilized to investigate the relationship between symptoms, medication, duration of illness, number of episodes, and MMP9 plasma levels in patients. Last, we explored the correlation between MMP9 levels and hippocampal volumes in patients and healthy individuals separately. Patients displayed higher MMP9 plasma levels than healthy individuals (F(1, 60) = 21.19, p < 0.0001). MMP9 levels correlated with negative symptoms in patients (R = 0.39, p = 0.035), but not with medication, duration of illness, or the number of episodes. Further, patients had smaller left (F(1,59) = 9.12, p = 0.0040) and right (F(1,59) = 6.49, p = 0.013) hippocampal volumes. Finally, left (R = -0.39, p = 0.034) and right (R = -0.37, p = 0.046) hippocampal volumes correlated negatively with MMP9 plasma levels in patients. We observe higher MMP9 plasma levels in SCZ, associated with lower hippocampal volumes, suggesting involvement of MMP9 in the pathology of SCZ. Future studies are needed to investigate how MMP9 influences the pathology of SCZ over the lifespan, whether the observed associations are specific for schizophrenia, and if a therapeutic modulation of MMP9 promotes neuroprotective effects in SCZ.

We present a volumetric mesh-based algorithm for parameterizing the placenta to a flattened template to enable effective visualization of local anatomy and function. MRI shows potential as a research tool as it provides signals directly related to placental function. However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult. We address interpretation challenges by mapping the placenta so that it resembles the familiar ex vivo shape. We formulate the parameterization as an optimization problem for mapping the placental shape represented by a volumetric mesh to a flattened template. We employ the symmetric Dirichlet energy to control local distortion throughout the volume. Local injectivity in the mapping is enforced by a constrained line search during the gradient descent optimization. We validate our method using a research study of 111 placental shapes extracted from BOLD MRI images. Our mapping achieves sub-voxel accuracy in matching the template while maintaining low distortion throughout the volume. We demonstrate how the resulting flattening of the placenta improves visualization of anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening.

We propose a novel pairwise distance measure between image keypoint sets, for the purpose of large-scale medical image indexing. Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry. A new kernel is proposed to quantify the variability of keypoint geometry in location and scale. Our distance measure may be estimated between O (N 2) image pairs in [Formula: see text] operations via keypoint indexing. Experiments report the first results for the task of predicting family relationships from medical images, using 1010 T1-weighted MRI brain volumes of 434 families including monozygotic and dizygotic twins, siblings and half-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalence and the keypoint geometry kernel improve upon standard hard set equivalence (HSE) and appearance kernels alone in predicting family relationships. Monozygotic twin identification is near 100%, and three subjects with uncertain genotyping are automatically paired with their self-reported families, the first reported practical application of image-based family identification. Our distance measure can also be used to predict group categories, sex is predicted with an AUC = 0.97. Software is provided for efficient fine-grained curation of large, generic image datasets.

Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations.

Measuring and understanding functional fetal brain development in utero is critical for the study of the developmental foundations of our cognitive abilities, possible early detection of disorders, and their prevention. Thalamocortical connections are an intricate component of shaping the cortical layout, but so far, only ex-vivo studies provide evidence of how axons enter the sub-plate and cortex during this highly dynamic phase. Evidence for normal in-utero development of the functional thalamocortical connectome in humans is missing. Here, we modeled fetal functional thalamocortical connectome development using in-utero functional magnetic resonance imaging in fetuses observed from 19th to 40th weeks of gestation (GW). We observed a peak increase of thalamocortical functional connectivity strength between 29th and 31st GW, right before axons establish synapses in the cortex. The cortico-cortical connectivity increases in a similar time window, and exhibits significant functional laterality in temporal-superior, -medial, and -inferior areas. Homologous regions exhibit overall similar mirrored connectivity profiles, but this similarity decreases during gestation giving way to a more diverse cortical interconnectedness. Our results complement the understanding of structural development of the human connectome and may serve as the basis for the investigation of disease and deviations from a normal developmental trajectory of connectivity development.

Tumour resection requires precise planning and navigation to maximise tumour removal while simultaneously protecting nearby healthy tissues. Neurosurgeons need to know the location of the remaining tumour after partial tumour removal before continuing with the resection. Our approach to the problem uses biomechanical modelling and computer simulation to compute the brain deformations after the tumour is resected. In this study, we use meshless Total Lagrangian explicit dynamics as the solver. The problem geometry is extracted from the patient-specific magnetic resonance imaging (MRI) data and includes the parenchyma, tumour, cerebrospinal fluid and skull. The appropriate non-linear material formulation is used. Loading is performed by imposing intra-operative conditions of gravity and reaction forces between the tumour and surrounding healthy parenchyma tissues. A finite frictionless sliding contact is enforced between the skull (rigid) and parenchyma. The meshless simulation results are compared to intra-operative MRI sections. We also calculate Hausdorff distances between the computed deformed surfaces (ventricles and tumour cavities) and surfaces observed intra-operatively. Over 80% of points on the ventricle surface and 95% of points on the tumour cavity surface were successfully registered (results within the limits of two times the original in-plane resolution of the intra-operative image). Computed results demonstrate the potential for our method in estimating the tissue deformation and tumour boundary during the resection.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.