Nonlinear Image Registration Research Articles

RBIO-01. CHARACTERIZING THE TEMPORAL EVOLUTION OF BRAIN ARCHITECTURE DURING RADIOTHERAPY FOR GLIOBLASTOMA USING NONLINEAR IMAGE REGISTRATION

Abstract BACKGROUND Previous serial MRI studies following glioblastoma (GBM) resection reveal dynamic changes in the tumor cavity and surrounding tissue. However, the time course of these dynamics is unclear, especially in the immediate postoperative period. METHODS GBM patients (n=8) received three MRIs: within 72 hours post-resection, immediately preceding RT (RTstart), and at the RT midpoint (RTmid). The radiation field (RF) was based on the resection cavity (RC) and surrounding T2-FLAIR hyperintensities at the 72 hour MRI. Nonlinear image registration was performed across the two time intervals, allowing comparison between the intended radiation targets and the actual irradiated tissue. Dice coefficients (DRF and DRC), the relative volume of the RC (RCvol), and the volume of unintended iatrogenic radiation (IRvol) were measured at each timepoint, and IRvol was fitted to an exponential decay. RESULTS DRF showed modest reduction, with mean±SD of 0.97±0.02 at both RTstart and RTmid. In contrast, the RC showed more change, with mean DRC 0.63±0.13 and 0.60±0.12, and mean RCvol of 0.74±0.27 and 0.72±0.25 at RTstart and RTmid, respectively. DRF, DRC, and RCvol showed greater change during the first interval than the second (p=3.5x10-4, 4.1x10-4, and 0.1, respectively). The mean IRvol was 16.7±12.6 at RTstart and 21.7±12.8 at RTmid, with a time constant of -0.031 (95% CI=[-0.017, -0.060]), indicating that 80% of all voxels that receive unintended iatrogenic radiation moved into the radiation field by postoperative day 57, and 95% will have moved in by day 125. CONCLUSIONS Nonlinear registration demonstrated significant postoperative architectural changes, with most occurring in the first two months after surgery near the RC. This potentially suggests a role for adaptive planning during RT, particularly for protocols that focus on the RC rather than T2-FLAIR hyperintensities. Future prospective studies are needed to assess the role of adaptive radiotherapy for GBM.

OSBA: An Open Neonatal Neuroimaging Atlas and Template for Spina Bifida Aperta

We present the Open Spina Bifida Aperta (OSBA) atlas, an open atlas and set of neuroimaging templates for spina bifida aperta (SBA). Traditional brain atlases may not adequately capture anatomical variations present in pediatric or disease-specific cohorts. The OSBA atlas fills this gap by representing the computationally averaged anatomy of the neonatal brain with SBA after fetal surgical repair. The OSBA atlas was constructed using structural T2-weighted and diffusion tensor MRIs of 28 newborns with SBA who underwent prenatal surgical correction. The corrected gestational age at MRI was 38.1 ± 1.1 weeks (mean ± SD). The OSBA atlas consists of T2-weighted and fractional anisotropy templates, along with nine tissue prior maps and region of interest (ROI) delineations. The OSBA atlas offers a standardized reference space for spatial normalization and anatomical ROI definition. Our image segmentation and cortical ribbon definition are based on a human-in-the-loop approach, which includes manual segmentation. The precise alignment of the ROIs was achieved by a combination of manual image alignment and automated, non-linear image registration. From the clinical and neuroimaging perspective, the OSBA atlas enables more accurate spatial standardization and ROI-based analyses and supports advanced analyses such as diffusion tractography and connectomic studies in newborns affected by this condition.

MMORF—FSL’s MultiMOdal Registration Framework

Abstract We present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods—FNIRT, ANTs, and DR-TAMAS—across four domains: FreeSurfer label overlap, diffusion tensor imaging (DTI) similarity, task-fMRI cluster mass, and distortion. The evaluation is based on 100 unrelated subjects from the Human Connectome Project (HCP) dataset registered to the Oxford-MultiModal-1 (OMM-1) multimodal template via either the T1w contrast alone or in combination with a DTI/DTI-derived contrast. Results show that MMORF is the most consistently high-performing method across all domains—both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL.

Time multiscale regularization for nonlinear image registration

Regularization-based methods are commonly used for image registration. However, fixed regularizers have limitations in capturing details and describing the dynamic registration process. To address this issue, we propose a time multiscale registration framework for nonlinear image registration in this paper. Our approach replaces the fixed regularizer with a monotone decreasing sequence, and iteratively uses the residual of the previous step as the input for registration. Particularly, first, we introduce a dynamically varying regularization strategy that updates regularizers at each iteration and incorporates them with a multiscale framework. This approach guarantees an overall smooth deformation field in the initial stage of registration and fine-tunes local details as the images become more similar. We then deduce convergence analysis under certain conditions on the regularizers and parameters. Further, we introduce a TV-like regularizer to demonstrate the efficiency of our method. Finally, we compare our proposed multiscale algorithm with some existing methods on both synthetic images and pulmonary computed tomography (CT) images. The experimental results validate that our proposed algorithm outperforms the compared methods, especially in preserving details during image registration with sharp structures.

Deformable image registration based on single or multi-atlas methods for automatic muscle segmentation and the generation of augmented imaging datasets.

Muscle segmentation is a process relied upon to gather medical image-based muscle characterisation, useful in directly assessing muscle volume and geometry, that can be used as inputs to musculoskeletal modelling pipelines. Manual or semi-automatic techniques are typically employed to segment the muscles and quantify their properties, but they require significant manual labour and incur operator repeatability issues. In this study an automatic process is presented, aiming to segment all lower limb muscles from Magnetic Resonance (MR) imaging data simultaneously using three-dimensional (3D) deformable image registration (single inputs or multi-atlas). Twenty-three of the major lower limb skeletal muscles were segmented from five subjects, with an average Dice similarity coefficient of 0.72, and average absolute relative volume error (RVE) of 12.7% (average relative volume error of -2.2%) considering the optimal subject combinations. The multi-atlas approach showed slightly better accuracy (average DSC: 0.73; average RVE: 1.67%). Segmented MR imaging datasets of the lower limb are not widely available in the literature, limiting the potential of new, probabilistic methods such as deep learning to be used in the context of muscle segmentation. In this work, Non-linear deformable image registration is used to generate 69 manually checked, segmented, 3D, artificial datasets, allowing access for future studies to use these new methods, with a large amount of reliable reference data.

Fine-grained, nonlinear registration of live cell movies reveals spatiotemporal organization of diffuse molecular processes.

We present an application of nonlinear image registration to align in microscopy time lapse sequences for every frame the cell outline and interior with the outline and interior of the same cell in a reference frame. The registration relies on a subcellular fiducial marker, a cell motion mask, and a topological regularization that enforces diffeomorphism on the registration without significant loss of granularity. This allows spatiotemporal analysis of extremely noisy and diffuse molecular processes across the entire cell. We validate the registration method for different fiducial markers by measuring the intensity differences between predicted and original time lapse sequences of Actin cytoskeleton images and by uncovering zones of spatially organized GEF- and GTPase signaling dynamics visualized by FRET-based activity biosensors in MDA-MB-231 cells. We then demonstrate applications of the registration method in conjunction with stochastic time-series analysis. We describe distinct zones of locally coherent dynamics of the cytoplasmic protein Profilin in U2OS cells. Further analysis of the Profilin dynamics revealed strong relationships with Actin cytoskeleton reorganization during cell symmetry-breaking and polarization. This study thus provides a framework for extracting information to explore functional interactions between cell morphodynamics, protein distributions, and signaling in cells undergoing continuous shape changes. Matlab code implementing the proposed registration method is available at https://github.com/DanuserLab/Mask-Regularized-Diffeomorphic-Cell-Registration.

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta.

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675.This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA.

CineCT platform for in vivo and ex vivo measurement of 3D high resolution Lagrangian strains in the left ventricle following myocardial infarction and intramyocardial delivery of theranostic hydrogel

Myocardial infarction (MI) produces acute changes in strain and stiffness within the infarct that can affect remote areas of the left ventricle (LV) and drive pathological remodeling. We hypothesized that intramyocardial delivery of a hydrogel within the MI region would lower wall stress and reduce adverse remodeling in Yorkshire pigs (n = 5). 99mTc-Tetrofosmin SPECT imaging defined the location and geometry of induced MI and border regions in pigs, and in vivo and ex vivo contrast cine computed tomography (cineCT) quantified deformations of the LV myocardium. Serial in vivo cineCT imaging provided data in hearts from control pigs (n = 3) and data from pigs (n = 5) under baseline conditions before MI induction, post-MI day 3, post-MI day 7, and one hour after intramyocardial delivery of a hyaluronic acid (HA)-based hydrogel with shear-thinning and self-healing properties to the central infarct area. Isolated, excised hearts underwent similar cineCT imaging using an ex vivo perfused heart preparation with cyclic LV pressurization. Deformations were evaluated using nonlinear image registration of cineCT volumes between end-diastole (ED) and end-systole (ES), and 3D Lagrangian strains were calculated from the displacement gradients. Post-MI day 3, radial, circumferential, maximum principal, and shear strains were reduced within the MI region (p < 0.04) but were unchanged in normal regions (p > 0.6), and LV end diastolic volume (LV EDV) increased (p = 0.004), while ejection fraction (EF) and stroke volume (SV) decreased (p < 0.02). Post-MI day 7, radial strains in MI border zones increased (p = 0.04) and dilation of LV EDV continued (p = 0.052). There was a significant negative linear correlation between regional radial and maximum principal/shear strains and percent infarcted tissue in all hearts (R2 > 0.47, p < 0.004), indicating that cineCT strain measures could predict MI location and degree of injury. Post-hydrogel day 7 post-MI, LV EDV was significantly reduced (p = 0.009), EF increased (p = 0.048), and radial (p = 0.021), maximum principal (p = 0.051), and shear strain (p = 0.047) increased within regions bordering the infarct. A smaller strain improvement within the infarct and normal regions was also noted on average along with an improvement in SV in 4 out of 5 hearts. CineCT provides a reliable method to assess regional changes in strains post-MI and the therapeutic effects of intramyocardial hydrogel delivery.

CineCT imaging platform for in-vivo and ex-vivo measurement of myocardial biomechanics post myocardial infarction and following intramyocardial delivery of theranostic hydrogel

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH) Purpose Myocardial infarction (MI) induces acute regional changes in myocardial strain and stiffness in the infarct and the remote areas of the left ventricle (LV), which lead to adverse changes in LV geometry and function. We hypothesize that cineCT imaging could evaluate these biomechanical changes along with the effects of intramyocardial delivery of theranostic hydrogels. Introduction We present an experimental platform to assess changes in the deformation of the LV myocardium using contrast cineCT (CCT) imaging of the beating porcine heart (active deformation) before and after acute MI and intramyocardial delivery of an imageable theranostic hydrogel. We then assess the acute effects of hydrogel delivery early post-MI on biomechanics (passive deformation) using an ex vivo perfused heart preparation. Methods Contrast cineCT imaging was performed using 64-slice CT on 5 Yorkshire pigs without MI (n = 3) or with MI (n = 2). MI pigs had serial imaging performed before and 1 hour after acute surgical coronary occlusion to induce anterolateral MI. One MI pig was also imaged 1 hour after intramyocardial injection of a novel imageable theranostic iodinated hydrogel within the MI region. Post euthanasia, excised hearts were flushed with chilled UW cardioplegic solution and mounted on a custom inflation apparatus for cineCT imaging during LV inflation by external pump. LV pressure was cycled between 10 and 60 mmHg at 35 bpm. Dilute iohexol was injected into aortic root and UW perfusate (15 ml, 1 ml/sec). CineCT image series were reconstructed, contrast enhanced, resampled to the LV long axis (Z), and exported as a series of 10 CT volumes covering 0-90% of the cardiac/inflation cycle. Volumes were registered incrementally using nonlinear image registration (BioImageSuite) and the calculated displacement at each time point was exported at a resolution of 1 mm. A custom Matlab program was used to fit the displacement field to local trilinear polynomials and then calculate the displacement gradients and 3D Lagrangian strains. To estimate the accuracy of this approach, cardiac volumes were also numerically deformed using a 10 pixel translation and 5% triaxial stretch. Results We successfully acquired serial in-vivo and ex-vivo 3D CineCT images for assessment of the active and passive LV myocardial deformation and tracked deformation through the full cardiac/inflation cycle (Figure 2). Numerical deformation tests showed average tracking errors of &amp;lt; 0.2 mm (1/4 pixel) in the X,Y,Z directions of the volume. These resulted in Lagrangian strain errors of &amp;lt; 0.47% for the in-plane strains EXX and EYY (radial and circumferential plane) and &amp;lt; 0.5% for EZZ (long axis). Conclusions We have developed a novel CineCT imaging platform that allows for high resolution in-vivo and ex-vivo measurement of myocardial biomechanics post-MI and following intramyocardial delivery of imageable theranostic hydrogels, which may improve early active and passive biomechanics.

Improving Algorithm for the Alignment of Consecutive, Whole-Slide, Immunohistochemical Section Images

Background: Accurate and precise alignment of histopathology tissue sections is a key step for the interpretation of the proteome topology and cell level three-dimensional (3D) reconstruction of diseased tissues. However, the realization of an automated and robust method for aligning nonglobally stained immunohistochemical (IHC) sections is still challenging. In this study, we aim to assess the feasibility of multidimensional graph-based image registration on aligning serial-section and whole-slide IHC section images. Materials and Methods: An automated, patch graph-based registration method was established and applied to align serial, whole-slide IHC sections at ×10 magnification (average 32,947 × 27,054 pixels). The alignment began with the initial alignment of high-resolution reference and translated images (object segmentation and rigid registration) and nonlinear registration of low-resolution reference and translated images, followed by the multidimensional graph-based image registration of the segmented patches, and finally, the fusion of deformed patches for inspection. The performance of the proposed method was formulated and evaluated by the Hausdorff distance between continuous image slices. Results: Sets of average 315 patches from five serial whole slide, IHC section images were tested using 21 different IHC antibodies across five different tissue types (skin, breast, stomach, prostate, and soft tissue). The proposed method was successfully automated to align most of the images. The average Hausdorff distance was 48.93 Lim with a standard deviation of 14.94 Lim, showing a significant improvement from the previously published patch-based nonlinear image registration method (average Hausdorff distance of 93.89 Lim with 50.85 Lim standard deviation). Conclusions: Our method was effective in aligning whole-slide tissue sections at the cell-level resolution. Further advancements in the screening of the proteome topology and 3D tissue reconstruction could be expected.

Hyperspectral super-resolution imaging with far-red emitting fluorophores using a thin-film tunable filter

New innovations in single-molecule localization microscopy (SMLM) have revolutionized optical imaging, enabling the characterization of biological structures and interactions with unprecedented detail and resolution. However, multi-color or hyperspectral SMLM can pose particular challenges which affect image quality and data interpretation, such as unequal photophysical performance of fluorophores and non-linear image registration issues, which arise as two emission channels travel along different optical paths to reach the detector. In addition, using evanescent-wave based approaches (Total Internal Reflection Fluorescence: TIRF) where beam shape, decay depth, and power density are important, different illumination wavelengths can lead to unequal imaging depth across multiple channels on the same sample. A potential useful approach would be to use a single excitation wavelength to perform hyperspectral localization imaging. We report herein on the use of a variable angle tunable thin-film filter to spectrally isolate far-red emitting fluorophores. This solution was integrated into a commercial microscope platform using an open-source hardware design, enabling the rapid acquisition of SMLM images arising from fluorescence emission captured within ∼15 nm to 20 nm spectral windows (or detection bands). By characterizing intensity distributions, average intensities, and localization frequency through a range of spectral windows, we investigated several far-red emitting fluorophores and identified an optimal fluorophore pair for two-color SMLM using this method. Fluorophore crosstalk between the different spectral windows was assessed by examining the effect of varying the photon output thresholds on the localization frequency and fraction of data recovered. The utility of this approach was demonstrated by hyper-spectral super-resolution imaging of the interaction between the mitochondrial protein, TOM20, and the peroxisomal protein, PMP70.

Standard-space atlas of the viscoelastic properties of the human brain.

Standard anatomical atlases are common in neuroimaging because they facilitate data analyses and comparisons across subjects and studies. The purpose of this study was to develop a standardized human brain atlas based on the physical mechanical properties (i.e., tissue viscoelasticity) of brain tissue using magnetic resonance elastography (MRE). MRE is a phase contrast‐based MRI method that quantifies tissue viscoelasticity noninvasively and in vivo thus providing a macroscopic representation of the microstructural constituents of soft biological tissue. The development of standardized brain MRE atlases are therefore beneficial for comparing neural tissue integrity across populations. Data from a large number of healthy, young adults from multiple studies collected using common MRE acquisition and analysis protocols were assembled (N = 134; 78F/ 56 M; 18–35 years). Nonlinear image registration methods were applied to normalize viscoelastic property maps (shear stiffness, μ, and damping ratio, ξ) to the MNI152 standard structural template within the spatial coordinates of the ICBM‐152. We find that average MRE brain templates contain emerging and symmetrized anatomical detail. Leveraging the substantial amount of data assembled, we illustrate that subcortical gray matter structures, white matter tracts, and regions of the cerebral cortex exhibit differing mechanical characteristics. Moreover, we report sex differences in viscoelasticity for specific neuroanatomical structures, which has implications for understanding patterns of individual differences in health and disease. These atlases provide reference values for clinical investigations as well as novel biophysical signatures of neuroanatomy. The templates are made openly available (github.com/mechneurolab/mre134) to foster collaboration across research institutions and to support robust cross‐center comparisons.

Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps.

We present a novel method to assess the variations in protein expression and spatial heterogeneity of tumor biopsies with application in computational pathology. This was done using different antigen stains for each tissue section and proceeding with a complex image registration followed by a final step of color segmentation to detect the exact location of the proteins of interest. For proper assessment, the registration needs to be highly accurate for the careful study of the antigen patterns. However, accurate registration of histopathological images comes with three main problems: the high amount of artifacts due to the complex biopsy preparation, the size of the images, and the complexity of the local morphology. Our method manages to achieve an accurate registration of the tissue cuts and segmentation of the positive antigen areas.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models.

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinaryresearch between thebiomechanics and neuroimagingfields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

A Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration

Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a regularisation method capable of selectively driving solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. This penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that this method is able to significantly reduce local volume changes and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst simultaneously matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Extensive leveraging of GPU parallelisation has allowed us to solve this highly computationally intensive optimisation problem while maintaining reasonable run times of under half an hour.

T154. WHITE MATTER INTEGRITY, DURATION OF UNTREATED PSYCHOSIS, AND ANTIPSYCHOTIC TREATMENT RESPONSE IN MEDICATION-NAïVE FIRST EPISODE PSYCHOSIS PATIENTS

BackgroundIt is becoming increasingly clear that longer duration of untreated psychosis (DUP) is associated with adverse clinical outcomes in patients with psychosis spectrum disorders. Especially because this association is often cited when justifying early intervention efforts, it is imperative to better understand underlying biological mechanisms.MethodsWe recruited 74 antipsychotic-naïve first episode psychosis (FEP) patients and 45 matched healthy controls in this trial. At baseline, we used a human connectome style diffusion weighted imaging (DWI) sequence to quantify white matter integrity in both groups. Patients then received 16 weeks of treatment with risperidone. DWI scans were acquired with opposite phase encoding directions [TR/TE: 3230ms/ 89.20ms; multiband acceleration factor 4, Flip angle: 84°; slice thickness 1.5mm, 92 slices, voxel size 1.5mm3, 92 diffusion weighted images distributed equally over 2 shells with b-values of ~ 1500s/mm2 and ~ 3000s/mm2, as well as 7 interspersed b= ~ 0s/mm2 images]). Preprocessing of DWI images was performed in TORTOISE (version 3.1.2). This included correction for thermal noise, Gibbs-ringing, high b-value based bulk motion and eddy-current distortions using a MAP-MRI model, resampling of images to 1mm3, and rotation of gradient tables independently for each DWI phase encoding direction. Then, DR-BUDDI was used to correct EPI distortions with input from the anatomical image and to combine the two datasets using geometric averaging to generate the final corrected dataset. Tensors were computed with DIFF_CALC using a linear fitting algorithm. To spatially normalize images to the Illinois Institute of Technology atlas (IIT4) space, we implemented an optimized non-linear image registration procedure using a modified version of 3dQwarp in AFNI. We compared whole brain fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD) and radial diffusivity between groups. To test if structural white matter integrity mediates the relationship between longer DUP and poorer treatment response, we fit a mediator model and estimated indirect effects.ResultsGroups did not differ in age (FEP: 23.83+/-6.21 years; HC: 24.78+/-6.24 years), sex (FEP: 65.2% male; HC: 64.4% male), or parental socioeconomic status (FEP: 5.95+/-4.83; HC: 4.22+/-4.06). We found decreased whole brain FA and AD in medication-naive FEP compared to controls. In patients, lower FA was correlated with longer DUP (r= -0.32; p= 0.03) and poorer subsequent response to antipsychotic treatment (r= 0.40; p= 0.01). Importantly, we found a significant mediation effect for FA (indirect effect: -2.70; p= 0.03), indicating that DUP exerts its effects on treatment response through affecting white matter integrity.DiscussionTo our knowledge, this is the first study to examine a putative role of white matter integrity in the observed association between DUP and clinical outcomes in first episode psychosis. Our data provide empirical support to the idea the DUP may have fundamental pathogenic effects on the natural history of psychosis, suggest a biological mechanism underlying this phenomenon, and underscore the importance of early intervention efforts in this disabling neuropsychiatric syndrome.

Spine Surgery Supported by Augmented Reality.

Study Design:A prospective, case-based, observational study.Objectives:To investigate how microscope-based augmented reality (AR) support can be utilized in various types of spine surgery.Methods:In 42 spinal procedures (12 intra- and 8 extradural tumors, 7 other intradural lesions, 11 degenerative cases, 2 infections, and 2 deformities) AR was implemented using operating microscope head-up displays (HUDs). Intraoperative low-dose computed tomography was used for automatic registration. Nonlinear image registration was applied to integrate multimodality preoperative images. Target and risk structures displayed by AR were defined in preoperative images by automatic anatomical mapping and additional manual segmentation.Results:AR could be successfully applied in all 42 cases. Low-dose protocols ensured a low radiation exposure for registration scanning (effective dose cervical 0.29 ± 0.17 mSv, thoracic 3.40 ± 2.38 mSv, lumbar 3.05 ± 0.89 mSv). A low registration error (0.87 ± 0.28 mm) resulted in a reliable AR representation with a close matching of visualized objects and reality, distinctly supporting anatomical orientation in the surgical field. Flexible AR visualization applying either the microscope HUD or video superimposition, including the ability to selectively activate objects of interest, as well as different display modes allowed a smooth integration in the surgical workflow, without disturbing the actual procedure. On average, 7.1 ± 4.6 objects were displayed visualizing target and risk structures reliably.Conclusions:Microscope-based AR can be applied successfully to various kinds of spinal procedures. AR improves anatomical orientation in the surgical field supporting the surgeon, as well as it offers a potential tool for education.

The Genetics of Encephalization in Eight Inbred Mouse Strains and their F1 Crosses

Increased relative brain size (encephalization) is an important feature of primate and human evolution that has been associated with increased intelligence. Although the heritability of brain size is known to be high, the extent to which different genetic factor types (e.g., additive genotype, maternal genotype, sex, nonadditive genetic interactions) contribute to measured brain size and encephalization are not as well understood. Endocast measures from a large normative sample of genetically diverse mouse lines were estimated to quantify 1) how increased encephalization impacts neurocranial shape and 2) the contribution of various genetic factors towards determining encephalization. Digital endocasts were estimated for 1182 mice representing the eight Collaborative Cross mouse inbred founder lines and 62 reciprocal F1 crosses. Endocasts were estimated by applying a single atlas image endocast segmentation to micro‐computed tomography (μCT) images of each specimen’s skull, based on the nonlinear volumetric image registration of each μCT to the representative atlas image. This method was validated by comparison of automatically extracted endocasts to endocasts that were manually segmented (using Endex software) for a large subset of the specimens. Measures of endocast volume, length, width, and height represent different aspects of endocast size. Ratios of volume over cranial base length (IRE) and volume over body weight (V2W) represent encephalization relative to cranial size and body size, respectively. Diallel analysis (BayesDiallel in R) was used to estimate the genetic influence of multiple additive and nonadditive genetic factors on each measure. The total heritability of each size and encephalization measure is between 0.82 and 0.87, with additive founder strain genotype effects accounting for the vast majority of this heritability. There are also significant inbreeding, sex, maternal genotype, and founder strain cross‐specific effects. The additive effects of founder genotypes were more pronounced for V2W than for IRE, with some of the strains showing opposite directions of effect between these encephalization measures. Care must be taken in the measure of overall size used to estimate encephalization. While inbred mice tend to have smaller overall size, they tend to have a higher encephalization, likely because the impact of inbreeding on brain volume is not as severe as the impact on skull size or body weight. In addition to the strength of genotypic effects, we quantified the influence of increased encephalization on overall cranial vault shape. As encephalization increases, endocasts display more substantial increases in cranial vault height than either length or width. Variation in the mouse cranial base size appears limited, while the shape of later ossifying vault bones strongly covaries with relative brain size. These results support the conclusion that encephalization is highly heritable and indicate which types of genetic factors contribute most strongly. Additionally, relatively large mouse brains are accommodated primarily through variation in cranial vault height, leading to a round cranial vault shape that is analogous to globular vault of modern humans.Support or Funding InformationFinancial support provided by Stony Brook University Startup Funds to CJP

High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly

Normative brain atlases are a standard tool for neuroscience research and are, for example, used for spatial normalization of image datasets prior to voxel-based analyses of brain morphology and function. Although many different atlases are publicly available, they are usually biased with respect to an imaging modality and the age distribution. Both effects are well known to negatively impact the accuracy and reliability of the spatial normalization process using non-linear image registration methods. An important and very active neuroscience area that lacks appropriate atlases is lesion-related research in elderly populations (e.g. stroke, multiple sclerosis) for which FLAIR MRI and non-contrast CT are often the clinical imaging modalities of choice. To overcome the lack of atlases for these tasks and modalities, this paper presents high-resolution, age-specific FLAIR and non-contrast CT atlases of the elderly generated using clinical images.

Accelerated Generation of Spatiotemporal Atlas through Fast Template Matching

A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects. Recent years have witnessed an increasing interest in using spatiotemporal atlas for scientific research and clinical applications in image processing, data analysis and medical imaging. However, the generation of spatiotemporal atlas is often time-consuming and computationally expensive due to the nonlinear image registration procedures involved. This research targets at accelerating the generation of spatiotemporal atlas by formulating the atlas generation procedure as a multi-level modulation (M-ary) classification problem. In particular, we have implemented a fast template matching method based on singular value decomposition, and applied it to generate high quality spatiotemporal atlas with reasonable time and computational complexity. The performance has been systematically evaluated on public accessible data sets. The results and conclusions hold promise for further developing advanced algorithms for accelerating generation of spatiotemporal atlas.