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This technology report describes the longitudinal registration approach that we intend to incorporate into SPM12. It essentially describes a group-wise intra-subject modeling framework, which combines diffeomorphic and rigid-body registration, incorporating a correction for the intensity inhomogeneity artifact usually seen in MRI data. Emphasis is placed on achieving internal consistency and accounting for many of the mathematical subtleties that most implementations overlook. The implementation was evaluated using examples from the OASIS Longitudinal MRI Data in Non-demented and Demented Older Adults.

This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more efficient optimisation scheme — both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss–Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data.

Single-shot Echo Planar Imaging (EPI) is one of the most efficient magnetic resonance imaging (MRI) acquisition schemes, producing relatively high-definition images in 100 ms or less. These qualities make it desirable for Diffusion Tensor Imaging (DTI), functional MRI (fMRI), and Dynamic Susceptibility Contrast MRI (DSC-MRI). However, EPI suffers from severe spatial and intensity distortion due to B0 field inhomogeneity induced by magnetic susceptibility variations. Anatomically accurate, undistorted images are essential for relating DTI and fMRI images with anatomical MRI scans, and for spatial registration with other modalities. We present here a fast, robust, and accurate procedure for correcting EPI images from such spatial and intensity distortions. The method involves acquisition of scans with opposite phase encoding polarities, resulting in opposite spatial distortion patterns, and alignment of the resulting images using a fast nonlinear registration procedure. We show that this method, requiring minimal additional scan time, provides superior accuracy relative to the more commonly used, and more time consuming, field mapping approach. This method is also highly computationally efficient, allowing for direct “real-time” implementation on the MRI scanner. We further demonstrate that the proposed method can be used to recover dropouts in gradient echo (BOLD and DSC-MRI) EPI images.

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.

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.

BACKGROUND: Volumetric measurements of fetal brain maturation in the third trimester of pregnancy are key predictors of developmental outcomes. Improved understanding of fetal brain development trajectories may aid in identifying and clinically managing at-risk fetuses. Currently, fetal brain structures in MRI images must be manually segmented, which requires both time and expertise. To facilitate the targeting and measurement of brain structures in the fetus, we compare the results of five segmentation methods applied to fetal brain MRI data to gold-standard manual tracings. METHODS: Adult women with singleton pregnancies (n=21), of whom five were scanned twice approximately three weeks apart, were recruited (26 total datasets, median gestational age=34.8, IQR=30.9-36.6). T2-weighted single-shot fast spin echo images of the fetal brain were acquired on 1.5T and 3T MRI scanners. Images were first combined into a single 3D anatomical volume. Next, a trained tracer manually segmented the thalamus, cerebellum, and total cerebral volumes. The manual segmentations were compared with five automatic methods of segmentation available within Advanced Normalization Tools (ANTs) and FMRIB's Linear Image Registration Tool (FLIRT) toolboxes. The manual and automatic labels were compared using Dice similarity coefficients (DSC). The DSC values were compared using Friedman's test for repeated measures. RESULTS: Comparing cerebellum and thalamus masks against the manually segmented masks, the median DSC values for ANTs and FLIRT were 0.78 (interquartile range [IQR]= 0.7-0.8) and 0.6 (IQR=0.5-0.7), respectively. A Friedman's test (p<0.001) indicated that the ANTs registration methods, primarily nonlinear methods, performed better than FLIRT. CONCLUSIONS: Deformable registration methods provided the most accurate results relative to manual segmentation. Overall, this semi-automatic subcortical segmentation method provides reliable performance to segment subcortical volumes in fetal MR images. This method reduces the costs of manual segmentation, facilitating the measurement of typical and atypical fetal brain development.

We propose a robust framework for quantitatively comparing model-predicted and experimentally measured strain fields in the human brain during harmonic skull motion. Traumatic brain injuries (TBIs) are typically caused by skull impact or acceleration, but how skull motion leads to brain deformation and consequent neural injury remains unclear and comparison of model predictions to experimental data remains limited. Magnetic resonance elastography (MRE) provides high-resolution, full-field measurements of dynamic brain deformation induced by harmonic skull motion. In the proposed framework, full-field strain measurements from human brain MRE in vivo are compared to simulated strain fields from models with similar harmonic loading. To enable comparison, the model geometry and subject anatomy, and subsequently, the predicted and measured strain fields are nonlinearly registered to the same standard brain atlas. Strain field correlations ( C v ), both global (over the brain volume) and local (over smaller sub-volumes), are then computed from the inner product of the complex-valued strain tensors from model and experiment at each voxel. To demonstrate our approach, we compare strain fields from MRE in six human subjects to predictions from two previously developed models. Notably, global C v values are higher when comparing strain fields from different subjects ( C v ~0.6–0.7) than when comparing strain fields from either of the two models to strain fields in any subject. The proposed framework provides a quantitative method to assess similarity (and to identify discrepancies) between model predictions and experimental measurements of brain deformation and thus can aid in the development and evaluation of improved models of brain biomechanics.

Current methods for processing diffusion MRI (dMRI) to map the connectivity of the human brain require precise delineations of anatomical structures. This requirement has been approached by either segmenting the data in native dMRI space or mapping the structural information from T1-weighted (T1w) images. The characteristic features of diffusion data in terms of signal-to-noise ratio, resolution, as well as the geometrical distortions caused by the inhomogeneity of magnetic susceptibility across tissues hinder both solutions. Unifying the two approaches, we propose regseg, a surface-to-volume nonlinear registration method that segments homogeneous regions within multivariate images by mapping a set of nested reference-surfaces. Accurate surfaces are extracted from a T1w image of the subject, using as target image the bivariate volume comprehending the fractional anisotropy (FA) and the apparent diffusion coefficient (ADC) maps derived from the dMRI dataset. We first verify the accuracy of regseg on a general context using digital phantoms distorted with synthetic and random deformations. Then we establish an evaluation framework using undistorted dMRI data from the Human Connectome Project (HCP) and realistic deformations derived from the inhomogeneity fieldmap corresponding to each subject. We analyze the performance of regseg computing the misregistration error of the surfaces estimated after being mapped with regseg onto 16 datasets from the HCP. The distribution of errors shows a 95% CI of 0.56–0.66mm, that is below the dMRI resolution (1.25mm, isotropic). Finally, we cross-compare the proposed tool against a nonlinear b0-to-T2w registration method, thereby obtaining a significantly lower misregistration error with regseg. The accurate mapping of structural information in dMRI space is fundamental to increase the reliability of network building in connectivity analyses, and to improve the performance of the emerging structure-informed techniques for dMRI data processing. [Display omitted] •Regseg is a surface-to-volume registration & segmentation method with subvoxel accuracy.•Regseg is applied in segmentation of dMRI scalars, jointly tackling EPI distortion.•Regseg enables structure-informed methods in connectome extraction pipelines.•Evaluated on dMRI multivariate-data, regseg overperformed b0-to-T2w registration.

Introduction Multimodality image integration of functional and anatomical data can be performed by means of dedicated hybrid imaging systems or by software image co-registration techniques. Hybrid positron emission tomography (PET)/computed tomography (CT) systems have found wide acceptance in oncological imaging, while software registration techniques have a significant role in patient-specific, cost-effective, and radiation dose-effective application of integrated imaging. Objectives Software techniques allow accurate (2–3 mm) rigid image registration of brain PET with CT and MRI. Nonlinear techniques are used in whole-body image registration, and recent developments allow for significantly accelerated computing times. Nonlinear software registration of PET with CT or MRI is required for multimodality radiation planning. Difficulties remain in the validation of nonlinear registration of soft tissue organs. The utilization of software-based multimodality image integration in a clinical environment is sometimes hindered by the lack of appropriate picture archiving and communication systems (PACS) infrastructure needed to efficiently and automatically integrate all available images into one common database. Discussion In cardiology applications, multimodality PET/single photon emission computed tomography and coronary CT angiography imaging is typically not required unless the results of one of the tests are equivocal. Software image registration is likely to be used in a complementary fashion with hybrid PET/CT or PET/magnetic resonance imaging systems. Software registration of stand-alone scans “paved the way” for the clinical application of hybrid scanners, demonstrating practical benefits of image integration before the hybrid dual-modality devices were available.

Voxel-wise quantitative assessment of typical characteristics in three-dimensional (3D) multiphase computed tomography (CT) imaging, especially arterial phase hyperenhancement (APHE) and subsequent washout (WO), is crucial for the diagnosis and therapy of hepatocellular carcinoma (HCC). However, this process is still missing in practice. Radiologists often visually estimate these features, which limit the diagnostic accuracy due to subjective interpretation and qualitative assessment. Quantitative assessment is one of the solutions to this problem. However, performing voxel-wise assessment in 3D is difficult due to the misalignments between images caused by respiratory and other physiological motions. In this paper, based on the Liver Imaging Reporting and Data System (v2018), we propose a registration-based quantitative model for the 3D voxel-wise assessment of image characteristics through multiple CT imaging phases. Specifically, we selected three phases from sequential CT imaging phases, i.e., pre-contrast phase (Pre), arterial phase (AP), delayed phase (DP), and then registered Pre and DP images to the AP image to extract and assess the major imaging characteristics. An iterative reweighted local cross-correlation was applied in the proposed registration model to construct the fidelity term for comparison of intensity features across different imaging phases, which is challenging due to their distinct intensity appearance. Experiments on clinical dataset showed that the means of dice similarity coefficient of liver were 98.6% and 98.1%, those of surface distance were 0.38 and 0.54 mm, and those of Hausdorff distance were 4.34 and 6.16 mm, indicating that quantitative estimation can be accomplished with high accuracy. For the classification of APHE, the result obtained by our method was consistent with those acquired by experts. For the WO, the effectiveness of the model was verified in terms of WO volume ratio.