Volumetric Image Reconstruction Research Articles

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging.

Convolutional neural networks (CNNs), initially developed for image processing applications, have recently received significant attention within the field of medical ultrasound imaging. In this study, passive cavitation imaging/mapping (PCI/PAM), which is used to map cavitation sources based on the correlation of signals across an array of receivers, is evaluated. Traditional reconstruction techniques in PCI, such as delay-and-sum, yield high spatial resolution at the cost of a substantial computational time. This results from the resource-intensive process of determining sensor weights for individual pixels in these methodologies. Consequently, the use of conventional algorithms for image reconstruction does not meet the speed requirements that are essential for real-time monitoring. Here, we show that a three-dimensional (3D) convolutional network can learn the image reconstruction algorithm for a 16×16 element matrix probe with a receive frequency ranging from 256 kHz up to 1.0 MHz. The network was trained and evaluated using simulated data representing point sources, resulting in the successful reconstruction of volumetric images with high sensitivity, especially for single isolated sources (100% in the test set). As the number of simultaneous sources increased, the network's ability to detect weaker intensity sources diminished, although it always correctly identified the main lobe. Notably, however, network inference was remarkably fast, completing the task in approximately 178 s for a dataset comprising 650 frames of 413 volume images with signal duration of 20μs. This processing speed is roughly thirty times faster than a parallelized implementation of the traditional time exposure acoustics algorithm on the same GPU device. This would open a new door for PCI application in the real-time monitoring of ultrasound ablation.

Volume integral model for algebraic image reconstruction and computed tomography

Industrial computed tomography (CT) has seen widespread adoption as an inspection technique due to its ability to resolve small defects and perform high-resolution measurements on complex structures. The reconstruction of CT data is usually performed using filtered back-projection (FBP) methods, such as the Feldkamp-Davis-Kress (FDK) method, and are selected as they offer a good compromise between reconstruction time and quality. More recently, iterative reconstruction algorithms have seen a resurgence in research interest as computing power has increased. Iterative reconstruction algorithms, such as the algebraic reconstruction technique (ART), use a reconstruction approach based on linear algebra to determine voxel attenuation coefficients based on the measured attenuation of the sample at the detector and calculation of the ray paths traversing the voxel grid. This offers a more precise model for CT reconstruction but at the cost of computational complexity and reconstruction time. Existing ART implementations are based on the 2D weighting models of the binary integral method (BIM), line integral method (LIM) and area integral method (AIM). For full 3D reconstruction, BIM and LIM only offer approximations leading to numerical inaccuracies. AIM for 2D reconstruction is mathematically exact but considers the divergent nature of a fan beam for 2D only. For a full 3D volumetric reconstruction, the X-ray cone beam is divergent in all directions and therefore AIM cannot be applied in its current form. A novel voxel weighting method for 3D volumetric image reconstruction using ART and providing a mathematically exact fractional volume weighting is introduced in this paper and referred to as the volume integral method (VIM). A set of algorithms is provided based on computer graphics techniques to determine ray/voxel intersections with volume reconstruction computed based on the divergence theorem. A set of experimental configurations is developed to provide a comparison against existing methods and conclusions are provided. Optimisation is achieved through graphic acceleration.

Exploiting volumetric wave correlation for enhanced depth imaging in scattering medium

Imaging an object embedded within a scattering medium requires the correction of complex sample-induced wave distortions. Existing approaches have been designed to resolve them by optimizing signal waves recorded in each 2D image. Here, we present a volumetric image reconstruction framework that merges two fundamental degrees of freedom, the wavelength and propagation angles of light waves, based on the object momentum conservation principle. On this basis, we propose methods for exploiting the correlation of signal waves from volumetric images to better cope with multiple scattering. By constructing experimental systems scanning both wavelength and illumination angle of the light source, we demonstrated a 32-fold increase in the use of signal waves compared with that of existing 2D-based approaches and achieved ultrahigh volumetric resolution (lateral resolution: 0.41 mu m, axial resolution: 0.60 mu m) even within complex scattering medium owing to the optimal coherent use of the broad spectral bandwidth (225 nm).

Toward Ultrasensitive, Broadband, Reflection‐Mode In Vivo Photoacoustic Microscopy Using a Bare Glass

AbstractAccurate volumetric photoacoustic image reconstruction requires ultrasonic detection with sufficient sensitivity to pressure transients over a broad spectral bandwidth. By using the ultrafast temporal dynamics and high‐sensitivity responses to refractive index changes of optical surface waves, a structureless ultrasonic sensor is proposed for photoacoustic impulse measurement that is manufactured simply by covering a ubiquitous bare glass with several drops of water. Ultrasensitive photoacoustic detection with a broadband frequency response is achieved by converting the photoacoustically modulated phase shift in the optical reflection into a time‐varying polarization. This sensor achieves noise‐equivalent pressure sensitivity at ≈93 Pa while maintaining an acoustic bandwidth of ≈174 MHz. Incorporation of the transparent sensor into an optical‐resolution photoacoustic microscope enables reflection‐mode 3D imaging with isometric spatial resolution on the micrometer scale. Label‐free volumetric visualizations of adult zebrafish are obtained in vivo. The favorable performance of the proposed sensor offers considerable potential for advancement of biomedical photoacoustic investigations.

Spatiotemporal and Multicellular Intravital Microscopy Analysis during Cardiac Injury and Repair

BackgroundUnderstanding the response to cardiac injury across multiple cell types and timescales has relied on techniques that reduce the complexity of the in vivo environment to provide a step‐by‐step picture of heart function. These models do not recapitulate the temporal and spatial complexities of the true in vivo environment. We have developed a suite of in vivo imaging methods that enable the study of cardiac injury, and here demonstrate intravital cardiac multiphoton microscopy (MPM) to assess cardiomyocyte function and the dynamics of heart and inflammatory cell types in response to cardiac injury and repair.MethodsIntravital cardiac MPM of male and female mice was performed in the anesthetized mechanically ventilated mouse following left thoracotomy and placement of a customized imaging window on the left ventricle. Fast image acquisition (30 frame/sec) and simultaneous electrocardiogram and respiratory pressure recording enable volumetric image reconstruction throughout the cardiac cycle and quantitative measures of tissue motion and deformation. To demonstrate tracking of acute cellular changes over 1‐2 h, we performed focal sterile injury (FSI; non‐scanning high‐power laser exposure causing capillary and cell disruption) and measured cardiomyocyte calcium dynamics, as well as neutrophil and macrophage/monocyte trafficking in αMHC‐GCaMP8, Rosa26‐Cre‐Ly6G+/TdTom, and Cx3Cr1+/GFP‐CCR2+/RFP mice, respectively. To demonstrate monitoring of inflammatory cell interactions and tissue function in actively repairing heart, we performed left ventricle cryoinfarction (CI) or sham in the same mouse strains and imaged after 7‐days. To explore the behavior of a recently focused upon cell class expressing c‐Kit, we performed FSI and CI in c‐KitBAC‐EGFP and c‐Kit‐rtTA/H2B‐EGFP mice. Intravenous injection of fluorescent dyes labelled vasculature (Texas Red or fluoresceine conjugated 70kDa‐dextran, or Q‐dot 655 or 705) and nuclei (Hoechst).ResultsIntravital cardiac MPM and FSI, enabled the visualization and quantification of non‐cyclical and uncoordinated cardiomyocyte calcium activity associated with a 40% reduction in cell motion and deformation, increased Ly6G+ neutrophil trafficking and Cx3Cr+ resident macrophage polarity over 2 h (p<0.05). Resident c‐Kit+ cell morphology and position did not change over 150 min in both c‐KitBAC‐EGFP and c‐Kit‐rtTA/H2B‐EGFP mouse strains. Imaging 7‐days after‐CI, our approaches were able to identify reduced cardiomyocyte calcium activity within the infarct zone associated with a 90% reduction in tissue motion and deformation, morphological alterations in the microvasculature (wider, reduced bifurcations; p<0.05), increased tissue Cx3Cr1+ macrophage number, Ly6G+ neutrophil arrest in vasculature (p<0.05), and punctate perivascular c‐kit expression that shifts to a vascular phenotype.ConclusionWe have demonstrated the ability to image and quantify the spatiotemporal and multicellular dynamics of cardiac injury and repair using intravital cardiac MPM. These techniques provide a powerful tool to investigate active phases of cardiac repair post‐injury in the true in vivo environment.

Static coded aperture in robotic X-ray tomography systems.

Coded aperture X-ray computed tomography is a computational imaging technique capable of reconstructing inner structures of an object from a reduced set of X-ray projection measurements. Coded apertures are placed in front of the X-ray sources from different views and thus significantly reduce the radiation dose. This paper introduces coded aperture X-ray computed tomography for robotic X-ray systems which offer positioning flexibility. While single coded-aperture 3D tomography was recently introduced for standard trajectory CT scanning, it is shown that significant gains in imaging performance can be attained by simple modifications in the CT scanning trajectories enabled by emerging dual robotic CT systems. In particular, the subject is fixed on a plane and the CT system uniformly rotates around the r -axis which is misaligned with the coordinate axes. A single stationary coded aperture is placed on front of the robotic X-ray source above the plane and the corresponding X-ray projections are measured by a two-dimensional detector on the second arm of the robotic system. The compressive measurements with misalignment enable the reconstruction of high-resolution three-dimensional volumetric images from the low-resolution coded projections on the detector at a sub-sampling rate. An efficient algorithm is proposed to generate the rotation matrix with two basic sub-matrices and thus the forward model is formulated. The stationary coded aperture is designed based on the Pearson product-moment correlation coefficient analysis and the direct binary search algorithm is used to obtain the optimized coded aperture. Simulations using simulated datasets show significant gains in reconstruction performance compared to conventional coded aperture CT systems.

Hybrid Convolutional Neural Network (hCNN) for Image Reconstruction in Near-Infrared Optical Tomography.

Near-infrared optical tomography (NIROT), a promising imaging modality for early detection of oxygenation in the brain of preterm infants, requires data acquisition at the tissue surface and thus an image reconstruction adaptable to cephalometric variations and surface topologies. Widely used model-based reconstruction methods come with the drawback of huge computational cost. Neural networks move this computational load to an offline training phase, allowing much faster reconstruction. Our aim is a data-driven volumetric image reconstruction that generalises well to different surfaces, increases reconstruction speed, localisation accuracy and image quality. We propose a hybrid convolutional neural network (hCNN) based on the well-known V-net architecture to learn inclusion localisation and absorption coefficients of heterogenous arbitrary shapes via a joint cost function. We achieved an average reconstruction time of 30.45s, a time reduction of 89% and inclusion detection with an average Dice score of 0.61. The CNN is flexible to surface topologies and compares well in quantitative metrics with the traditional model-based (MB) approach and state-of-the-art neuronal networks for NIROT. The proposed hCNN was successfully trained, validated and tested on in-silico data, excels MB methods in localisation accuracy and provides a remarkable increase in reconstruction speed.

Tomosynthesis in pediatrics: a retrospective of its application in the world practice and own data

Tomosynthesis is a modern effective and informative method of low-dose X-ray diagnostics, which allows obtaining a significant number of layered images with the subsequent volumetric image reconstruction. The use of tomosynthesis provides a one-time examination of a large anatomical area without loss of the image quality and diagnostics of difficult-to-visualize pathological changes that are not detected by digital radiography. The article presents an overview of the problem of improving low-dose imaging options in the radiation diagnostics, as well as the authors own data on the use of tomosynthesis for the diagnosis of community-acquired pneumonia in children.

Novel 3D ultrasound imaging and automated deep learning analysis in ophthalmology

We present novel 3D ultrasound technologies for imaging of the eye. We computationally align and improve imaging volumes acquired by mechanical scanning of conventional ophthalmic ultrasound systems. We also present results of using emerging ultrahigh frequency array systems that increase volumetric image reconstruction speed by several orders of magnitude and allowing Doppler processing of blood flow. The easily interpretable 3D visualizations can greatly assist in automated analysis and reducing operator dependency. We present deep learning methods for volumetric segmentation and quantification. The 3D ophthalmic ultrasound combined with automated deep learning analysis allows non-expert users to easily use this technology for diagnosis, monitoring response to therapy and improving overall ocular disease management at low cost, versatility, and safety for repeated and timely scanning.

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy.

Several hip pathologies have been attributed to abnormal morphology with an underlying assumption of aberrant biomechanics. However, structure-function relationships at the joint level remain challenging to quantify due to difficulties in accurately measuring dynamic joint motion. The soft tissue artifact errors inherent in optical skin marker motion capture are exacerbated by the depth of the hip joint within the body and the large mass of soft tissue surrounding the joint. Thus, the complex relationship between bone shape and hip joint kinematics is more difficult to study accurately than in other joints. Herein, a protocol incorporating computed tomography (CT) arthrography, three-dimensional (3D) reconstruction of volumetric images, dual fluoroscopy, and optical motion capture to accurately measure the dynamic motion of the hip joint is presented. The technical and clinical studies that have applied dual fluoroscopy to study form-function relationships of the hip using this protocol are summarized, and the specific steps and future considerations for data acquisition, processing, and analysis are described.

Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox

3D tomographic imaging requires the computation of solutions to very large inverse problems. In many applications, iterative algorithms provide superior results, however, memory limits in available computing hardware restrict the size of problems that can be solved. For this reason, iterative methods are not normally used to reconstruct typical data sets acquired with lab based CT systems. We thus use state of the art techniques such as dual buffering to develop an efficient strategy to compute the required operations for iterative reconstruction. This allows the iterative reconstruction of volumetric images of arbitrary size using any number of GPUs, each with arbitrarily small memory. Strategies for both the forward and backprojection operators are presented, along with two regularization approaches that are easily generalized to other projection types or regularizers. The proposed improvement also accelerates reconstruction of smaller images on single or multiple GPU systems, providing faster code for time-critical applications. The resulting algorithm has been added to the TIGRE toolbox, a repository for iterative reconstruction algorithms for general CT, but this memory-saving and problem-splitting strategy can be easily adapted for use with other GPU-based tomographic reconstruction code.

Microwave Photonic MIMO Radar for Short-Range 3D Imaging

In the fields of radar imaging and detection, the use of optical components appears as an attractive solution to overcome limitations of current technologies. This paper focuses on the development of a new class of microwave-photonic radar imaging systems enabling volumetric image reconstruction. These operations are made possible using a multiple-input multiple-output antenna array architecture combined with the conception of a dedicated optical multiplexing technology to limit the complexity of the whole reception chain. Particular emphasis is placed on the development of mathematical formalisms specifically adapted to these microwave-photonic radar architectures, making it possible to model the measurements carried out and to reconstruct three-dimensional radar images. These solutions are validated by the reconstruction of complex scenes with a multiple-input multiple-output radar made of up to 16 transmitters and 16 receivers.

Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.

Математическое моделирование реконструкции объемных изображений в рентгеновской компьютерной томографии с применением голографических методов

Математическое моделирование реконструкции объемных изображений в рентгеновской компьютерной томографии с применением голографических методов

Разработка вычислительного алгоритма реконструкции объемных изображений в рентгеновской компьютерной томографии с применением голографических методов

Симонов Евгений Николаевич, д-р техн. наук, профессор кафедры техники и технологии Политехнического института, Южно-Уральский государственный университет, г. Челябинск; e.n.simonov@yandex.ru. Аврамов Денис Витальевич, аспирант, Южно-Уральский государственный университет, г. Челябинск; avramov.91@mail.ru. E.N. Simonov, e.n.simonov@yandex.ru, D.V. Avramov, avramov.91@mail.ru South Ural State University, Chelyabinsk, Russian Federation

Slice-to-volume medical image registration: A survey.

During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field.

Single step full volumetric reconstruction optical coherence tomography utilizing compressive sensing

3D optical coherence tomography imaging (OCT) combined with compressive sensing (CS) has been proved to be an attractive and effective tool in a variety of fields, such as medicine and biology. To achieve high quality imaging while using as less CS sampling rate as possible is the goal of this approach. Here we present an innovative single step fully 3D CS-OCT volumetric image recovery method, in which 3D OCT volumetric image of the object is compressively sampled via our proposed CS coding strategies in all three dimensions while its sparsity is simultaneously taken into consideration in every direction. The object can be directly recovered as the whole volume reconstruction via our advanced full 3D CS reconstruction algorithm. The numerical simulations of a human retina OCT volumetric image reconstruction by our method demonstrate a PSNR of as high as 38dB at a sampling rate of less than 10%.

Interior tomographic imaging of mouse heart in a carbon nanotube micro-CT.

The relatively high radiation dose from micro-CT is a cause for concern in preclinical research involving animal subjects. Interior region-of-interest (ROI) imaging was proposed for dose reduction, but has not been experimentally applied in micro-CT. Our aim is to implement interior ROI imaging in a carbon nanotube (CNT) x-ray source based micro-CT, and present the ROI image quality and radiation dose reduction for interior cardiac micro-CT imaging of a mouse heart in situ. An aperture collimator was mounted at the source-side to induce a small-sized cone beam (10 mm width) at the isocenter. Interior in situ micro-CT scans were conducted on a mouse carcass and several micro-CT phantoms. A GPU-accelerated hybrid iterative reconstruction algorithm was employed for volumetric image reconstruction. Radiation dose was measured for the same system operated at the interior and global micro-CT modes. Visual inspection demonstrated comparable image quality between two scan modes. Quantitative evaluation demonstrated high structural similarity index (up to 0.9614) with improved contrast-noise-ratio (CNR) on interior micro-CT mode. Interior micro-CT mode yielded significant reduction (up to 83.9%) for dose length product (DLP). This work demonstrates the applicability of using CNT x-ray source based interior micro-CT for preclinical imaging with significantly reduced radiation dose.

ABSOLUTE IMAGING OF LOW CONDUCTIVITY MATERIAL DISTRIBUTIONS USING NONLINEAR RECONSTRUCTION METHODS IN MAGNETIC INDUCTION TOMOGRAPHY

Magnetic Induction Tomography (MIT) is a newly developing technique of electrical tomography that in principle is able to map the electrical conductivity distribution in the volume of objects. The image reconstruction problem in MIT is similar to electrical impedance tomography (EIT) in the sense that both seek to recover the conductivity map, but differ remarkably in the fact that data being inverted in MIT is derived from induction theory and related sources of noise are different. Progress in MIT image reconstruction is still limited, and so far mainly linear algorithms have been implemented. In difference imaging, step inversion was demonstrated for recovering perturbations within conductive media, but at the cost of producing qualitative images, whilst in absolute imaging, linear iterative algorithms have mostly been employed but mainly offering encouraging results for imaging isolated high conductive targets. In this paper, we investigate the possibility of absolute imaging in 3D MIT of a target within conductive medium for low conductivity application ( σ< 5S m −1 ). For this class of problems, the MIT image reconstruction exhibits non-linearity and ill-posedness that cannot be treated with linear algorithms. We propose to implement for the first time in MIT two effective inversion methods known in non-linear optimization as Levenberg Marquardt (LMM) and Powell's Dog Leg (PDLM) methods. These methods employ damping and trust region techniques for controlling convergence and improving minimization of the objective function. An adaptive version of Gauss Newton is also presented (AGNM), which implements a damping mechanism to the regularization parameter. Here, the level of penalty is varied during the iterative process. As a comparison between the methods, different criteria are examined from image reconstructions using the LMM, PDLM and AGNM. For test examples, volumetric image reconstruction of a perturbation within homogeneous cylindrical background is considered. For inversion, an independent finite element FEM software package Maxwell by Ansys is employed to generate simulated data using a model of a 16 channel MIT system. Numerical results are employed to show different performance characteristics between the methods based on convergence, stability and sensitivity to the choice of the regularization parameter. To demonstrate the effect of scalability of absolute imaging in MIT for more realistic problems, a human head model with an internal anomaly is used to produce reconstructions for different finer resolutions. AGNM is adopted here and employs the Krylov subspace method to replace the computationally demanding direct inversion of the regularized Hessian.

WE-AB-303-09: Rapid Projection Computations for On-Board Digital Tomosynthesis in Radiation Therapy

Purpose: To facilitate fast and accurate iterative volumetric image reconstruction from limited-angle on-board projections. Methods: Intrafraction motion hinders the clinical applicability of modern radiotherapy techniques, such as lung stereotactic body radiation therapy (SBRT). The LIVE system may impact clinical practice by recovering volumetric information via Digital Tomosynthesis (DTS), thus entailing low time and radiation dose for image acquisition during treatment. The DTS is estimated as a deformation of prior CT via iterative registration with on-board images; this shifts the challenge to the computational domain, owing largely to repeated projection computations across iterations. We address this issue by composing efficient digital projection operators from their constituent parts. This allows us to separate the static (projection geometry) and dynamic (volume/image data) parts of projection operations by means of pre-computations, enabling fast on-board processing, while also relaxing constraints on underlying numerical models (e.g. regridding interpolation kernels). Further decoupling the projectors into simpler ones ensures the incurred memory overhead remains low, within the capacity of a single GPU. These operators depend only on the treatment plan and may be reused across iterations and patients. The dynamic processing load is kept to a minimum and maps well to the GPU computational model. Results: We have integrated efficient, pre-computable modules for volumetric ray-casting and FDK-based back-projection with the LIVE processing pipeline. Our results show a 60x acceleration of the DTS computations, compared to the previous version, using a single GPU; presently, reconstruction is attained within a couple of minutes. The present implementation allows for significant flexibility in terms of the numerical and operational projection model; we are investigating the benefit of further optimizations and accurate digital projection sub-kernels. Conclusion: Composable projection operators constitute a versatile research tool which can greatly accelerate iterative registration algorithms and may be conducive to the clinical applicability of LIVE. National Institutes of Health Grant No. R01-CA184173; GPU donation by NVIDIA Corporation