Volumetric Imaging Research Articles

Decreased Cerebral Perfusion in Chronic Migraine: A Voxel-based Cerebral Blood Flow Analysis Using 3D Pseudo-continuous Arterial Spin Labeling.

A contrast agent-free approach would be preferable to the frequently used invasive approaches for evaluating cerebral perfusion in chronic migraineurs (CM). In this work, non-invasive quantitative volumetric perfusion imaging was used to evaluate alterations in cerebral perfusion in CM. We used conventional brain structural imaging sequences and 3D pseudo-continuous arterial spin labeling (3D PCASL) to examine thirteen CM patients and fifteen normal controls (NCs). The entire brain gray matter underwent voxel-based analysis, and the cerebral blood flow (CBF) values of the altered positive areas were retrieved to look into the clinical variables' significant correlation. Brain regions with the decreased perfusion were located in the left postcentral gyrus, bilateral middle frontal gyrus, left middle occipital gyrus, left superior parietal lobule, left medial segment of superior frontal gyrus, and right orbital part of the inferior frontal gyrus. White matter fibers with decreased perfusion were located in bilateral superior longitudinal tracts, superior corona radiata, external capsules, anterior and posterior limbs of the internal capsule, anterior corona radiata, inferior longitudinal fasciculus, and right corticospinal tract. However, the correlation analysis showed no significant correlation between the CBF value of the above positive brain regions with clinical variables (p > 0.05). The current study provided more useful information to comprehend the pathophysiology of CM and revealed a new insight into the neural mechanism of CM from the pattern of cerebral hypoperfusion.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Practical considerations for using petrophysics and geoelectrical methods on clay rich landslides

Understanding the geological and hydrological conditions present within an unstable slope is crucial for assessing the likelihood of failure. Recently, geoelectrical characterization and monitoring of landslides has become increasingly prevalent in this context, due to the spatial sensitivity of electrical methods to critical hydro-mechanical parameters. We explore a situational relationship between resistivity and matric potential (or negative pore pressure), which is a key parameter in estimating the resistance to shear in geological materials, and gravimetric moisture content (GMC). We have chosen a well-characterized active landslide instrumented with geoelectrical monitoring technology, the Hollin Hill Landslide Observatory, situated in Lias rocks in the southern Howardian Hills, United Kingdom. We report on petrophysical relationships between porosity, GMC, electrical resistivity, and matric potential. We trial the application of these petrophysical relationships to inverted resistivity images. Ground model development is achieved through a mixture of clustering resistivity distributions and analysis of surface movements. Our findings show the shrink swell properties of clay result in a variable porosity, which is problematic for applying classic petrophysical relationships documented in the literature. Moreover, directly translating resistivity distributions into matric potential has additional challenges. Nonetheless, volumetric imaging of resistivity suggest that low shear strengths are concentrated downslope of a rotational backscarp. We infer that an accumulation of moisture drives the development of a slip surface at depth, which subsequently manifests in failure at the ground surface. We conclude that the time-lapse resistivity images alone could not be used to infer the pore pressure conditions present within the slope without development of the petrophysical relationships shown here. Therefore, we suggest that the results have practical implications for landslide monitoring with geophysical methods.

Visualization of Cataract Surgery Steps With 4D Microscope-Integrated Swept-Source Optical Coherence Tomography in Ex Vivo Porcine Eyes.

To investigate the visualization capabilities of high-speed swept-source optical coherence tomography (SS-OCT) in cataract surgery. Cataract surgery was simulated in wet labs with ex vivo porcine eyes. Each phase of the surgery was visualized with a novel surgical microscope-integrated SS-OCT with a variable imaging speed of over 1 million A-scans per second. It was designed to provide four-dimensional (4D) live-volumetric videos, live B-scans, and volume capture scans. Four-dimensional videos, B-scans, and volume capture scans of corneal incision, ophthalmic viscosurgical device injection, capsulorrhexis, phacoemulsification, intraocular lens (IOL) injection, and position of unfolded IOL in the capsular bag were recorded. The flexibility of the SS-OCT system allowed us to tailor the scanning parameters to meet the specific demands of dynamic surgical steps and static pauses. The entire length of the eye was recorded in a single scan, and unfolding of the IOL was visualized dynamically. The presented novel visualization method for fast ophthalmic surgical microscope-integrated intraoperative OCT imaging in cataract surgery allowed the visualization of all major steps of the procedure by achieving large imaging depths covering the entire eye and high acquisition speeds enabling live volumetric 4D-OCT imaging. This promising technology may become an integral part of routine and advanced robotic-assisted cataract surgery in the future. We demonstrate the visualization capabilities of a cutting edge swept-source OCT system integrated into an ophthalmic surgical microscope during cataract surgery.

Stimulus encoding by specific inactivation of cortical neurons

Neuronal ensembles are groups of neurons with correlated activity associated with sensory, motor, and behavioral functions. To explore how ensembles encode information, we investigated responses of visual cortical neurons in awake mice using volumetric two-photon calcium imaging during visual stimulation. We identified neuronal ensembles employing an unsupervised model-free algorithm and, besides neurons activated by the visual stimulus (termed “onsemble”), we also find neurons that are specifically inactivated (termed “offsemble”). Offsemble neurons showed faster calcium decay during stimuli, suggesting selective inhibition. In response to visual stimuli, each ensemble (onsemble+offsemble) exhibited small trial-to-trial variability, high orientation selectivity, and superior predictive accuracy for visual stimulus orientation, surpassing the sum of individual neuron activity. Thus, the combined selective activation and inactivation of cortical neurons enhances visual encoding as an emergent and distributed neural code.

Development of a large volume line scanning, high spectral range and resolution 3D hyperspectral photoluminescence imaging microscope for diamond and other high refractive index materials

Hyperspectral photoluminescence (PL) imaging is a powerful technique that can be used to understand the spatial distribution of emitting species in many materials. Volumetric hyperspectral imaging of weakly emitting color centers often necessitates considerable data collection times when using commercial systems. We report the development of a line-scanning hyperspectral imaging microscope capable of measuring the luminescence emission spectra for diamond volumes up to 2.20 × 30.00 × 6.30 mm with a high lateral spatial resolution of 1–3 µm. In an single X-λ measurement, spectra covering a 711 nm range, in a band from 400–1100 nm, with a spectral resolution up to 0.25 nm can be acquired. Data sets can be acquired with 723 (X) × 643 (Y) × 1172 (λ) pixels at a rate of 6 minutes/planar image slice, allowing for volumetric hyperspectral imaging with high sampling. This instrument demonstrates the ability to detect emission from several different color centers in diamond both at the surface and internally, providing a non-destructive method to probe their 3D spatial distribution, and is currently not achievable with any other commonly used system or technique.

Volumetric Imaging Biomarkers of Freezing of Gait (FoG) in Patients with Parkinson’s Disease (P8-3.019)

Volumetric Imaging Biomarkers of Freezing of Gait (FoG) in Patients with Parkinson’s Disease (P8-3.019)

Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions

Abstract. X-ray computed tomography has established itself as a crucial tool in the analysis of rock materials, providing the ability to visualise intricate 3D microstructures and capture quantitative information about internal phenomena such as structural damage, mineral reactions, and fluid–rock interactions. The efficacy of this tool, however, depends significantly on the precision of image segmentation, a process that has seen varied results across different methodologies, ranging from simple histogram thresholding to more complex machine learning and deep-learning strategies. The irregularity in these segmentation outcomes raises concerns about the reproducibility of the results, a challenge that we aim to address in this work. In our study, we employ the mass balance of a metamorphic reaction as an internal standard to verify segmentation accuracy and shed light on the advantages of deep-learning approaches, particularly their capacity to efficiently process expansive datasets. Our methodology utilises deep learning to achieve accurate segmentation of time-resolved volumetric images of the gypsum dehydration reaction, a process that traditional segmentation techniques have struggled with due to poor contrast between reactants and products. We utilise a 2D U-net architecture for segmentation and introduce machine-learning-obtained labelled data (specifically, from random forest classification) as an innovative solution to the limitations of training data obtained from imaging. The deep-learning algorithm we developed has demonstrated remarkable resilience, consistently segmenting volume phases across all experiments. Furthermore, our trained neural network exhibits impressively short run times on a standard workstation equipped with a graphic processing unit (GPU). To evaluate the precision of our workflow, we compared the theoretical and measured molar evolution of gypsum to bassanite during dehydration. The errors between the predicted and segmented volumes in all time series experiments fell within the 2 % confidence intervals of the theoretical curves, affirming the accuracy of our methodology. We also compared the results obtained by the proposed method with standard segmentation methods and found a significant improvement in precision and accuracy of segmented volumes. This makes the segmented computed tomography images suited for extracting quantitative data, such as variations in mineral growth rate and pore size during the reaction. In this work, we introduce a distinctive approach by using an internal standard to validate the accuracy of a segmentation model, demonstrating its potential as a robust and reliable method for image segmentation in this field. This ability to measure the volumetric evolution during a reaction with precision paves the way for advanced modelling and verification of the physical properties of rock materials, particularly those involved in tectono-metamorphic processes. Our work underscores the promise of deep-learning approaches in elevating the quality and reproducibility of research in the geosciences.

Purge–Mainstream Interactions in a Turbine Stage With Rotor Endwall Contouring

Abstract Purge flows are prevalent in modern gas turbine design, allowing for increased turbine entry temperatures. The purge flow passes through a rim seal and interacts with the mainstream flow, modifying the blade secondary flow structures and reducing stage efficiency. These structures may be controlled using end wall contouring (EWC), though experimental demonstration of their benefit is seldom reported in the literature. The optically accessible turbine at the University of Bath was designed to directly measure and visualize the flow field within the blade passage for a rotor with EWC. The single-stage turbine enables phase-locked flow field measurements with volumetric particle image velocimetry (PIV). Purge flow was supplied to investigate a range of operating conditions in which the secondary flow structures were modified. The modular turbine rotor allowed for expedient change of a bladed ring, or bling, featuring non-axisymmetric EWC. The identified secondary flow structures were the pressure-side leg of the horse shoe vortex (PS-HSV) and an egress vortex (EV) of concurrent rotational direction. An increase in purge flowrate monotonically shifted the EV toward the suction-side (SS) of the adjacent blade. The migration of the PS-HSV toward the SS caused the two aforementioned vortices to merge. The EWC rotor design included a leading-edge (LE) feature to alter the PS-HSV and a trough to guide the EV low spanwise in the passage and maintain displacement from the adjacent suction-side. The EWC rotor was found to be effective at altering the formation and positioning of the secondary flow structures at a range of purge flow conditions.

Coherent diffractive imaging with twisted X-rays: Principles, applications, and outlook

Recent technological breakthroughs in synchrotron and x-ray free electron laser facilities have revolutionized nanoscale structural and dynamic analyses in condensed matter systems. This review provides a comprehensive overview of the advancements in coherent scattering and diffractive imaging techniques, which are now at the forefront of exploring materials science complexities. These techniques, notably Bragg coherent diffractive imaging and x-ray photon correlation spectroscopy, x-ray magnetic dichroism, and x-ray correlation analysis leverage beam coherence to achieve volumetric three-dimensional imaging at unprecedented sub-nanometer resolutions and explore dynamic phenomena within sub-millisecond timeframes. Such capabilities are critical in understanding and developing advanced materials and technologies. Simultaneously, the emergence of chiral crystals—characterized by their unique absence of standard inversion, mirror, or other roto-inversion symmetries—presents both challenges and opportunities. These materials exhibit distinctive interactions with light, leading to phenomena such as molecular optical activity, chiral photonic waveguides, and valley-specific light emissions, which are pivotal in the burgeoning fields of photonic and spintronic devices. This review elucidates how novel x-ray probes can be leveraged to unravel these properties and their implications for future technological applications. A significant focus of this review is the exploration of new avenues in research, particularly the shift from conventional methods to more innovative approaches in studying these chiral materials. Inspired by structured optical beams, the potential of coherent scattering techniques utilizing twisted x-ray beams is examined. This promising direction not only offers higher spatial resolution but also opens the door to previously unattainable insights in materials science. By contextualizing these advancements within the broader scientific landscape and highlighting their practical applications, this review aims to chart a course for future research in this rapidly evolving field.

Learnable weight initialization for volumetric medical image segmentation

Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention. While mainly focusing on architectural modifications, most existing hybrid approaches still use conventional data-independent weight initialization schemes which restrict their performance due to ignoring the inherent volumetric nature of the medical data. To address this issue, we propose a learnable weight initialization approach that utilizes the available medical training data to effectively learn the contextual and structural cues via the proposed self-supervised objectives. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach, leading to state-of-the-art segmentation performance. Our proposed data-dependent initialization approach performs favorably as compared to the Swin-UNETR model pretrained using large-scale datasets on multi-organ segmentation task. Our source code and models are available at: https://github.com/ShahinaKK/LWI-VMS.

Unsupervised Multi-parametric MRI Registration Using Neural Optimal Transport.

Precise deformable image registration of multi-parametric MRI sequences is necessary for radiologists in order to identify abnormalities and diagnose diseases, such as prostate cancer and lymphoma. Despite recent advances in unsupervised learning-based registration, volumetric medical image registration that requires considering the variety of data distributions is still challenging. To address the problem of multi-parametric MRI sequence data registration, we propose an unsupervised domain-transported registration method, called OTMorph by employing neural optimal transport that learns an optimal transport plan to map different data distributions. We have designed a novel framework composed of a transport module and a registration module: the former transports data distribution from the moving source domain to the fixed target domain, and the latter takes the transported data and provides the deformed moving volume that is aligned with the fixed volume. Through end-to-end learning, our proposed method can effectively learn deformable registration for the volumes in different distributions. Experimental results with abdominal multi-parametric MRI sequence data show that our method has superior performance over around 67-85% in deforming the MRI volumes compared to the existing learning-based methods. Our method is generic in nature and can be used to register inter-/intra-modality images by mapping the different data distributions in network training.

Characterizing Low-cost Registration for Photographic Images to Computed Tomography.

Mapping information from photographic images to volumetric medical imaging scans is essential for linking spaces with physical environments, such as in image-guided surgery. Current methods of accurate photographic image to computed tomography (CT) image mapping can be computationally intensive and/or require specialized hardware. For general purpose 3-D mapping of bulk specimens in histological processing, a cost-effective solution is necessary. Here, we compare the integration of a commercial 3-D camera and cell phone imaging with a surface registration pipeline. Using surgical implants and chuck-eye steak as phantom tests, we obtain 3-D CT reconstruction and sets of photographic images from two sources: Canfield Imaging's H1 camera and an iPhone 14 Pro. We perform surface reconstruction from the photographic images using commercial tools and open-source code for Neural Radiance Fields (NeRF) respectively. We complete surface registration of the reconstructed surfaces with the iterative closest point (ICP) method. Manually placed landmarks were identified at three locations on each of the surfaces. Registration of the Canfield surfaces for three objects yields landmark distance errors of 1.747, 3.932, and 1.692 mm, while registration of the respective iPhone camera surfaces yields errors of 1.222, 2.061, and 5.155 mm. Photographic imaging of an organ sample prior to tissue sectioning provides a low-cost alternative to establish correspondence between histological samples and 3-D anatomical samples.

SUPPOSe 3Dge: A Method for Super-Resolved Detection of Surfaces in Volumetric Fluorescence Microscopy

We present SUPPOSe 3Dge, a method for the detection of surfaces in volumetric fluorescence microscopy images with resolution better than the diffraction limit. The method works by approximating an arbitrary object surface as a polyhedron formed by small triangular faces, which are thought as virtual point like sources of signal gradient. Then, the super resolution arises from solving a least square problem only on the positions of the sources, by comparing the measured image gradient with a synthetic one built from the approximated surface and the known point spread function of the instrument. We present the formulation of the method and its characterization using simulated measurements of two different types. We show that the method is able to identify with high precision and accuracy the sizes and shapes of objects smaller than the instrumental resolution. Using different quantifiers that measure the distance between the solution and the target object, we report reconstructions with an accuracy 100 times better than the axial resolution and 40 times the lateral resolution of the instrument.

The association between cannabis use and neuroimaging measures in older adults: findings from the UK biobank.

Cannabis use has increased in recent years. However, the long-term implications of cannabis use on brain health remain unknown. We explored the associations of cannabis use with volumetric brain magnetic resonance imaging (MRI) measures in dementia-free older adults. This cross-sectional and longitudinal study included dementia-free participants of the UK Biobank aged ≥60years. Linear regression models were used to evaluate the association of cannabis use and patterns of use with volumetric brain MRI measures. The association between cannabis use and change in brain MRI measures over time was also tested. All models were adjusted for potential confounders. The sample included 19,932 participants (mean age 68 ± 5years, 48% men), 3,800 (19%) reported lifetime use of cannabis. Cannabis use was associated with smaller total, white, grey and peripheral cortical grey matter volumes (B= -6,690 ± 1,157; P< 0.001, B= -4,396 ± 766; P< 0.001, B= -2,140 ± 690; P= 0.002 and B= -2,451 ± 606; P< 0.001, respectively). Among cannabis users, longer duration of use was associated with smaller total brain, grey and cortical grey matter volumes (B= -7,878 ± 2,396; P= 0.001, B= -5,411 ± 1,430; P< 0.001, B = -5,396 ± 1,254; P< 0.001, respectively), and with increased white matter hyperintensity volume (B= 0.09 ± 0.03; P= 0.008). Additionally, current vs. former users (B= -10,432 ± 4,395; P= 0.020) and frequent versus non-frequent users (B= -2,274 ± 1,125; P= 0.043) had smaller grey and cortical grey matter volumes, respectively. No significant associations were observed between cannabis use and change in brain MRI measures. Our findings suggest that cannabis use, particularly longer duration and frequent use, may be related to smaller grey and white matter volumes in older ages, but not to late-life changes in these measures over time.

Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models.

Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0 or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.

Deep learning-enhanced snapshot hyperspectral confocal microscopy imaging system

Laser-scanning confocal hyperspectral microscopy is a powerful technique to identify the different sample constituents and their spatial distribution in three-dimensional (3D). However, it suffers from low imaging speed because of the mechanical scanning methods. To overcome this challenge, we propose a snapshot hyperspectral confocal microscopy imaging system (SHCMS). It combined coded illumination microscopy based on a digital micromirror device (DMD) with a snapshot hyperspectral confocal neural network (SHCNet) to realize single-shot confocal hyperspectral imaging. With SHCMS, high-contrast 160-bands confocal hyperspectral images of potato tuber autofluorescence can be collected by only single-shot, which is almost 5 times improvement in the number of spectral channels than previously reported methods. Moreover, our approach can efficiently record hyperspectral volumetric imaging due to the optical sectioning capability. This fast high-resolution hyperspectral imaging method may pave the way for real-time highly multiplexed biological imaging.

Non-destructive three-dimensional imaging of artificially degraded CdS paints by pump-probe microscopy

Cadmium sulfide (CdS) pigments have degraded in several well-known artworks, but the influence of pigment properties and environmental conditions on the degradation process have yet to be fully understood. Traditional non-destructive analysis techniques primarily focus on macroscopic degradation, whereas microscopic information is typically obtained with invasive techniques that require sample removal. Here, we demonstrate the use of pump-probe microscopy to nondestructively visualize the three-dimensional structure and degradation progress of CdS pigments in oil paints. CdS pigments, reproduced following historical synthesis methods, were reproduced as oil paints and artificially aged by exposure to high relative humidity and light. The degradation of CdS to CdSO4·xH2O was confirmed by both FTIR (Fourier-transform infrared) and XPS (x-ray photoelectron spectroscopy) experiments. During the degradation process, optical pump-probe microscopy was applied to track the degradation progress in single grains, and volumetric imaging revealed early CdS degradation of small particles and on the surface of large particles. This indicates that the particle dimension influences the extent and evolution of degradation of historical CdS. In addition, the pump-probe signal decrease in degraded CdS is observable before visible changes to the eye, demonstrating that pump-probe microscopy is a promising tool to detect early-stage degradation in artworks.

Adaptable cascaded registration for personalized maxilla completion and cleft defect volume estimation.

Cone-beam computed tomography (CBCT) images provide high-resolution insights into the underlying craniofacial anomaly in patients with cleft lip and palate (CLP), requiring non-negligible annotation costs to measure the cleft defect for the guidance of the clinical secondary alveolar bone graft procedures. Considering the cumbersome volumetric image acquisition, there is a lack of paired CLP CBCTs and normal CBCTs for learning-based anatomical structure restoration models. Nowadays, the registration-based method relieves the annotation burden, though one-shot registration and the regular mask are limited to handling fine-grained shape variations and harmony between restored bony tissues and the defectedmaxilla. This study aimed to design and evaluate a novel method for deformable partial registration of the CLP CBCTs and normal CBCTs, enabling personalized maxilla completion and cleft defect volume prediction from CLPCBCTs. We proposed an adaptable deep registration framework for personalized maxilla completion and cleft defect volume prediction from CLP CBCTs. The key ingredient was a cascaded partial registration to exploit the maxillary morphology prior and attribute transfer. Cascaded registration with coarse-to-fine registration fields handled morphological variations of cleft defects and fine-grained maxillary restoration. We designed an adaptable cleft defect mask and volumetric Boolean operators for reliable voxel filling of the defected maxilla. A total of 36 clinically obtained CLP CBCTs were used to train and validate the proposed model, among which 22 CLP CBCTs were used to generate a training dataset with 440 synthetic CBCTs by B-spline deformation-based data augmentation and the remaining for testing. The proposed model was evaluated on maxilla completion and cleft defect volume prediction from clinically obtained unilateral and bilateral CLPCBCTs. Extensive experiments demonstrated the effectiveness of the adaptable cleft defect mask and the cascaded partial registration on maxilla completion and cleft defect volume prediction. The proposed method achieved state-of-the-art performances with the Dice similarity coefficient of 0.90 0.02 on the restored maxilla and 0.84 0.04 on the estimated cleft defect, respectively. The average Hausdorff distance between the estimated cleft defect and the manually annotated ground truth was 0.30 0.08 mm. The relative volume error of the cleft defect was 0.08. The proposed model allowed for the prediction of cleft defect maps that were in line with the ground truth in the challenging unilateral and bilateral CLP CBCTs. The results suggest that the proposed adaptable deep registration model enables patient-specific maxilla completion and automatic annotation of cleft defects, relieving tedious voxel-wise annotation and image acquisitionburdens.

Resolution analysis of a volumetric coded aperture X-ray diffraction imaging system.

A coded aperture X-ray diffraction (XRD) imaging system can measure the X-ray diffraction form factor from an object in three dimensions -X, Y and Z (depth), broadening the potential application of this technology. However, to optimize XRD systems for specific applications, it is critical to understand how to predict and quantify system performance for each use case. The purpose of this work is to present and validate 3D spatial resolution models for XRD imaging systems with a detector-side coded aperture. A fan beam coded aperture XRD system was used to scan 3D printed resolution phantoms placed at various locations throughout the system's field of view. The multiplexed scatter data were reconstructed using a model-based iterative reconstruction algorithm, and the resulting volumetric images were evaluated using multiple resolution criteria to compare against the known phantom resolution. We considered the full width at half max and Sparrow criterion as measures of the resolution and compared our results against analytical resolution models from the literature as well as a new theory for predicting the system resolution based on geometric arguments. We show that our experimental measurements are bounded by the multitude of theoretical resolution predictions, which accurately predict the observed trends and order of magnitude of the spatial and form factor resolutions. However, we find that the expected and observed resolution can vary by approximately a factor of two depending on the choice of metric and model considered. We observe depth resolutions of 7-16 mm and transverse resolutions of 0.6-2 mm for objects throughout the field of view. Furthermore, we observe tradeoffs between the spatial resolution and XRD form factor resolution as a function of sample location. The theories evaluated in this study provide a useful framework for estimating the 3D spatial resolution of a detector side coded aperture XRD imaging system. The assumptions and simplifications required by these theories can impact the overall accuracy of describing a particular system, but they also can add to the generalizability of their predictions. Furthermore, understanding the implications of the assumptions behind each theory can help predict performance, as shown by our data's placement between the conservative and idealized theories, and better guide future systems for optimized designs.