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One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a functional dependence between the intensities of two perfectly corresponding patches, we investigate how their Hessians relate to each other. Secondly, we suggest a closed-form expression to quantify the deviation from this relationship, given arbitrary pairs of image patches. We propose a geometrical interpretation of the new similarity metric and an efficient implementation for registration. We demonstrate the robustness of the metric to intensity nonuniformities using synthetic bias fields. By integrating the new metric in an affine registration framework, we evaluate its performance for MRI and ultrasound registration in the context of image-guided neurosurgery using target registration error and computation time.

The industrialization of Laser Additive Manufacturing (LAM) is challenged by the undesirable microstructures and high residual stresses originating from the fast and complex solidification process. Non-destructive assessment of the mechanical performance controlling deformation patterning is therefore critical. Here, we use Dark Field X-ray Microscopy (DFXM) to non-destructively map the 3D intragranular orientation and strain variations throughout a surface breaking grain within a directed energy deposition nickel superalloy. DFXM results reveal a highly heterogenous 3D microstructure in terms of the local orientation and lattice strain. The grain comprises $\approx$ 5$\mu$m-sized cells with alternating strain states, as high as 5 $\times 10^{-3}$, and orientation differences <0.5{\deg} . The DFXM results are compared to Electron Backscatter Diffraction measurements of the same grain from its cut-off surface. We discuss the microstructure developments during LAM, rationalising the development of the deformation patterning from the extreme thermal gradients during processing and the susceptibility for solute segregation., Comment: Corresponding author:can.yildirim@esrf.fr - Submitted to Scripta Materialia on 8 March2 023

We present and analyze a near infrared(NIR) spectrum of the under-luminous Type Ia supernova SN~2020qxp/ASASSN-20jq obtained with NIRES at the Keck Observatory 191 days after B-band maximum. The spectrum is dominated by a number of broad emission features including the [FeII] at 1.644mu which is highly asymmetric with a tilted top and a peak red-shifted by ~2,000km/s. In comparison with 2-D non-LTE synthetic spectra computed from 3-D simulations of off-center delayed-detonation Chandrasekhar-mass white-dwarf(WD) models, we find good agreement between the observed lines and the synthetic profiles, and are able to unravel the structure of the progenitor's envelope. We find that the size and tilt of the [Fe II] 1.644mu-profile (in velocity space) is an effective way to determine the location of an off-center delayed-detonation transition (DDT) and the viewing angle, and it requires a WD with a high central density of ~4E9$g/cm^3$. We also tentatively identify a stable Ni feature around 1.9mu characterized by a `pot-belly' profile that is slightly offset with respect to the kinematic center. In the case of SN~2020qxp/ASASSN-20jq, we estimate that the location of the DDT is ~0.3M(WD) off-center, which gives rise to an asymmetric distribution of the underlying ejecta. We also demonstrate that low-luminosity and high-density WD SNIa progenitors exhibit a very strong overlap of Ca and 56Ni in physical space. This results in the formation of a prevalent [Ca II] 0.73mu emission feature, which is sensitive to asymmetry effects. Our findings are discussed within the context of alternative scenarios, including off-center C/O detonations in He-triggered sub-M(Ch)-WDs and the direct collision of two WDs. Snapshot programs with Gemini/Keck/VLT/ELT class instruments and our spectropolarimetry program are complementary to mid-IR spectra by JWST., Comment: 29 pages, 6 figures, 6 Tables, to appear in The Astrophysical Journal (submitted: 7/26/21, revised: 9/4/21, accepted: 9/7/21)

INTRODUCTION: Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. METHODS: Using 678 subjects (2750 timepoints), differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. RESULTS: Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with ventricular features, and 76% accuracy using only the single APR feature. DISCUSSION: APR could be a useful and easy-to-implement feature to aid bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.

INTRODUCTION: Heterogeneity in the progression of Alzheimer's disease makes it challenging to predict the rate of cognitive and functional decline for individual patients. Tools for short-term prediction could help enrich clinical trial designs and focus prevention strategies on the most at-risk patients. METHOD: We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects with mild cognitive impairment remained stable and which functionally declined (measured by a two-point increase in CDR-SB) over 2 and 3-year follow-up periods, periods typical of the length of clinical trials. RESULTS: Combining both sets of features yields 77% accuracy (81% sensitivity and 75% specificity) to predict cognitive decline at 2 years (74% accuracy at 3 years with 75% sensitivity and 73% specificity). Using this tool to select trial participants yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25% treatment effect to reduce cognitive decline. DISCUSSION: This cohort enrichment tool could accelerate treatment development by increasing power in clinical trials.

Turbulence is a key process in many fields of astrophysics. Advances in numerical simulations of fluids over the last several decades have revolutionized our understanding of turbulence and related processes such as star formation and cosmic ray propagation. However, data from numerical simulations of astrophysical turbulence are often not made public. We introduce a new simulation-oriented database for the astronomical community: The Catalogue for Astrophysical Turbulence Simulations (CATS), located at www.mhdturbulence.com. CATS includes magnetohydrodynamic (MHD) turbulent box simulation data products generated by the public codes athena++, arepo, enzo, and flash. CATS also includes several synthetic observational data sets, such as turbulent HI data cubes. We also include measured power spectra and 3-point correlation functions from some of these data. We discuss the importance of open source statistical and visualization tools for the analysis of turbulence simulations such as those found in CATS., Comment: Accepted to ApJ

Stroke is the second leading cause of disability worldwide. In order to minimize disability, the goal of stroke treatment is to preserve tissue in the area where blood supply is decreased but sufficient to stave off cell death. Thrombectomy has been shown to offer fast and efficient reperfusion with high recanalization rates. However, due to the inherent risks of thrombectomy, indications including evidence of good collateral circulation should be present. Currently, methods for evaluating collateral circulation are limited. In this work, we present an automated technique to compute a collateral circulation score based on differences seen in mean intensities between left and right cerebral hemispheres in 4D angiography images. In this preliminary study, we analyzed scans of 28 subjects: 4 with normal collateral circulation, 7 with good, 10 with intermediate, and 7 with poor collateral circulation, (based on radiologist score) and found a good correlation between the computed score and radiologist score (r2 = 0.71) and good separation between good and intermediate/poor groups. This work shows promise as the first step in building an automated collateral circulation score., Comment: 8 pages, 7 7 figures, MICCAI Computing and Visualization for Intravascular Imaging (CVII) Workshop

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness. Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation., Comment: Accepted for publication in IEEE Transactions on Medical Imaging

Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations., Comment: Neuroimage 2016

Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for volume, thickness and shape measurements. This work introduces a new highly accurate and versatile method based on 3D convolutional neural networks for the automatic segmentation of neuroanatomy in T1-weighted MRI. In combination with a deep 3D fully convolutional architecture, efficient linear registration-derived spatial priors are used to incorporate additional spatial context into the network. An aggressive data augmentation scheme using random elastic deformations is also used to regularize the networks, allowing for excellent performance even in cases where only limited labelled training data are available. Applied to hippocampus segmentation in an elderly population (mean Dice coefficient = 92.1%) and sub-cortical segmentation in a healthy adult population (mean Dice coefficient = 89.5%), we demonstrate new state-of-the-art accuracies and a high robustness to outliers with the same architecture. Further validation on a multi-structure segmentation task in a scan-rescan dataset demonstrates accuracy (mean Dice coefficient = 86.6%) similar to the scan-rescan reliability of expert manual segmentations (mean Dice coefficient = 86.9%), and improved reliability compared to both expert manual segmentations and automated segmentations using FIRST. Furthermore, our method maintains a highly competitive runtime performance (e.g. requiring only 10 seconds for left/right hippocampal segmentation in 1x1x1 MNI stereotaxic space), orders of magnitude faster than conventional multi-atlas segmentation methods.

As the closest Type Ia supernova in decades, SN 2014J provides a unique opportunity for detailed investigation into observational signatures of the progenitor system and explosion mechanism in addition to burning product distribution. We present a late-time near-infrared spectral series from Gemini-N at $307-466$ days after the explosion. Following the $H$-band evolution probes the distribution of radioactive iron group elements, the extent of mixing, and presence of magnetic fields in the expanding ejecta. Comparing the isolated $1.6440$ $\mu$m [Fe II] emission line with synthetic models shows consistency with a Chandrasekhar-mass white dwarf of $\rho_c=0.7\times10^9$ g cm${}^{-3}$ undergoing a delayed detonation. The ratio of the flux in the neighboring $1.54$ $\mu$m emission feature to the flux in the $1.6440$ $\mu$m feature shows evidence of some limited mixing of stable and radioactive iron group elements in the central regions. Additionally, the evolution of the $1.6440$ $\mu$m line shows an intriguing asymmetry. When measuring line-width of this feature, the data show an increase in line width not seen in the evolution of the synthetic spectra, corresponding to $\approx1{,}000$ km s${}^{-1}$, which could be caused by a localized transition to detonation producing asymmetric ionization in the ejecta. Using the difference in width between the different epochs, an asymmetric component in the central regions, corresponding to approximately the inner $2\times10^{-4}$ of white dwarf mass suggests an off-center ignition of the initial explosion and hence of the kinematic center from the chemical center. Several additional models investigated, including a He detonation and a merger, have difficulty reproducing the features seen these spectra., Comment: 21 pages, 16 figures, accepted in ApJ

We present an analysis of photometric observations of the eclipsing novalike variable DW UMa made by the CBA consortium between 1999 and 2015. Analysis of 372 new and 260 previously published eclipse timings reveals a 13.6 year period or quasi-period in the times of minimum light. The seasonal light curves show a complex spectrum of periodic signals: both positive and negative superhumps, likely arising from a prograde apsidal precession and a retrograde nodal precession of the accretion disc. These signals appear most prominently and famously as sidebands of the orbital frequency but the precession frequencies themselves, at 0.40 and 0.22 cycles per day, are also seen directly in the power spectrum. The superhumps are sometimes seen together and sometimes separately. The depth, width and skew of eclipses are all modulated in phase with both nodal and apsidal precession of the tilted and eccentric accretion disc. The superhumps, or more correctly the precessional motions which produce them, may be essential to understanding the mysterious SW Sextantis syndrome. Disc wobble and eccentricity can both produce Doppler signatures inconsistent with the true dynamical motions in the binary, and disc wobble might boost the mass-transfer rate by enabling the hot white dwarf to directly irradiate the secondary star., Comment: 16 pages, 19 figures, accepted for publication in MNRAS

We report the results of a long campaign of time-series photometry on the nova-like variable UX Ursae Majoris during 2015. It spanned 150 nights, with ~1800 hours of coverage on 121 separate nights. The star was in its normal `high state' near magnitude V=13, with slow waves in the light curve and eclipses every 4.72 hours. Remarkably, the star also showed a nearly sinusoidal signal with a full amplitude of 0.44 mag and a period of 3.680 +/- 0.007 d. We interpret this as the signature of a retrograde precession (wobble) of the accretion disc. The same period is manifest as a +/-33 s wobble in the timings of mid-eclipse, indicating that the disc's centre of light moves with this period. The star also showed strong `negative superhumps' at frequencies w_orb+N and 2w_orb+N, where w_orb and N are respectively the orbital and precession frequencies. It is possible that these powerful signals have been present, unsuspected, throughout the more than 60 years of previous photometric studies., Comment: 9 pages, 10 figures, accepted by MNRAS