Browsing by Subject "Image reconstruction"

There always exists a demand to accelerate the time-consuming MRI acquisition process. Many methods have been proposed to achieve this goal, including deep learning method which appears to be a robust tool compared to conventional methods. While many works have been done to evaluate the performance of neural networks on standard anatomical MR images, few attentions have been paid to accelerating other less conventional MR image acquisitions. This work aims to evaluate the feasibility of neural networks on accelerating Brain DTI and Gynecological Brachytherapy MRI. Three neural networks including U-net, Cascade-net and PD-net were evaluated. Brain DTI data was acquired from public database RIDER NEURO MRI while cervix gynecological MRI data was acquired from Duke University Hospital clinic data. A 25% Cartesian undersampling strategy was applied to all the training and test data. Diffusion weighted images and quantitative functional maps in Brain DTI, T1-spgr and T2 images in GYN studies were reconstructed. The performance of the neural networks was evaluated by quantitatively calculating the similarity between the reconstructed images and the reference images, using the metric Total Relative Error (TRE). Results showed that with the architectures and parameters set in this work, all three neural networks could accelerate Brain DTI and GYN T2 MR imaging. Generally, PD-net slightly outperformed Cascade-net, and they both outperformed U-net with respect to image reconstruction performance. While this was also true for reconstruction of quantitative functional diffusion weighted maps and GYN T1-spgr images, the overall performance of the three neural networks on these two tasks needed further improvement. To be concluded, PD-net is very promising on accelerating T2-weighted-based MR imaging. Future work can focus on adjusting the parameters and architectures of the neural networks to improve the performance on accelerating GYN T1-spgr MR imaging and adopting more robust undersampling strategy such as radial undersampling strategy to further improve the overall acceleration performance.

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others.

This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system.

Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity.

Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.

Diffusion tensor imaging (DTI) has emerged as a unique method to characterize brain tissue microstructure non-invasively. DTI typically provides the ability to study white matter structure with a standard voxel resolution of 8μL over imaging field-of-views of the extent of the human brain. As such, it has long been recognized as a promising tool not only in clinical research for the diagnostic and monitoring of white matter diseases, but also for investigating the fundamental biological principles underlying the organization of long and short-range cortical networks. However, the complexity of brain structure within an MRI voxel makes it difficult to dissociate the tissue origins of the measured anisotropy. The tensor characterization is a composite result of proton pools in different tissue and cell structures with diverse diffusion properties. As such, partial volume effects introduce a strong bias which can lead to spurious measurements, especially in regions with a complex tissue structure such as interdigitating crossing fibers or in convoluted cortical folds near the grey/white matter interface.

This dissertation focuses on the design and development of acquisition and image reconstruction strategies to improve the spatial resolution of diffusion imaging. After a brief review of the theory of diffusion MRI and of the basic principles of streamline tractography in the human brain, the main challenges to increasing the spatial resolution are discussed. A comprehensive characterization of artifacts due to motion and field inhomogeneities is provided and novel corrective methods are proposed to enable the acquisition of diffusion weighted data with 2D mulitslice imaging techniques with full brain coverage, increased SNR and high spatial resolutions of 1.25×1.25×1.25 mm3 within an acceptable scan time. The method is extended to a multishot k_{_z}-encoded 3D multislab spiral DTI and evaluated in normal human volunteers.

To demonstrate the increased SNR and enhanced resolution capability of the proposed methods and more generally to assess the value of high-spatial resolution in diffusion imaging, a study of cortical depth-dependence of fractional anisotropy was performed at an unprecedented in-vivo inplane resolution of 0.390×0.390μm2 and an investigation of the trade-offs between spatial resolution and cortical specificity was conducted within the connectome framework.

Neutron stimulated emission computed tomography (NSECT) is a non-invasive, tomographic imaging technique with the ability to locate and quantify elemental concentration in a tissue sample. Previous studies have shown that NSECT has the ability to differentiate between benign and malignant tissue and diagnose liver iron overload while using a neutron beam tomographic acquisition protocol followed by iterative image reconstruction. These studies have shown that moderate concentrations of iron can be detected in the liver with moderate dose levels and long scan times. However, a low-dose, reduced scan time technique to differentiate various liver diseases has not been tested. As with other imaging modalities, the performance of NSECT in detecting different diseases while reducing dose and scan time will depend on the acquisition techniques and parameters that are used to scan the patients. In order to optimize a clinical liver imaging system based on NSECT, it is important to implement low-dose techniques and evaluate their feasibility, sensitivity, specificity and accuracy by analyzing the generated liver images from a patient population. This research work proposes to use Monte-Carlo simulations to optimize a clinical NSECT system for detection, localization, quantification and classification of liver diseases. This project has been divided into three parts; (a) implement two novel acquisition techniques for dose reduction, (b) modify MLEM iterative image reconstruction algorithm to incorporate the new acquisition techniques and (c) evaluate the performance of this combined technique on a simulated patient population.

The two dose-reduction, acquisition techniques that have been implemented are; (i) use of a single angle scanning, multi-detector acquisition system and (ii) the neutron-time resolved imaging (n-TRI) technique. In n-TRI, the NSECT signal has been resolved in time by a function of the speed of the incident neutron beam and this information has been used to locate the liver lesions in the tissue. These changes in the acquisition system have been incorporated and used to modify MLEM iterative image reconstruction algorithm to generate liver images. The liver images are generated from sinograms acquired by the simulated n-TRI based NSECT scanner from a simulated patient population.

The simulated patient population has patients of different sizes, with different liver diseases, multiple lesions with different sizes and locations in the liver. The NSECT images generated from this population have been used to validate the liver imaging system developed in this project. Statistical tests such as ROC and student t-tests have been used to evaluate this system. The overall improvement in dose and scan time as compared to the NSECT tomographic system have been calculated to verify the improvement in the imaging system. The patient dose was calculated by measuring the energy deposited by the neutron beam in the liver and surrounding body tissue. The scan time was calculated by measuring the time required by a neutron source to produce the neutron fluence required to generate a clinically viable NSECT image.

Simulation studies indicate that this NSECT system can detect, locate, quantify and classify liver lesions in different sized patients. The n-TRI imaging technique can detect lesions with wet iron concentration of 0.5 mg/g or higher in liver tissue in patients with 30 cm torso and can quantify lesions at 0.3 ns timing resolution with errors ≤ 17.8%. The NSECT system can localize and classify liver lesions of hemochromatosis, hepatocellular carcinoma, fatty liver tissue and cirrhotic liver tissue based on bulk and trace element concentrations. In a small patient with a torso major axis of 30 cm, the n-TRI based liver imaging technique can localize 91.67% of all lesions and classify lesions with an accuracy of 88.23%. The dose to the small patient is 0.37 mSv a reduction of 39.9% as compared to the NSECT tomographic system and scan times are comparable to that of an abdominal MRI scan. In a bigger patient with a torso major axis of 50cm, the n-TRI based technique can detect 75% of the lesions, while localizing 66.67% of the lesions, the accuracy of classification is 76.47%. The effective dose equivalent delivered to the larger patient is 1.57 mSv for a 68.8% decrease in dose as compared to a tomographic NSECT system.

The research performed for this dissertation has two important outcomes. First, it demonstrates that NSECT has the clinical potential for detection, localization and classification of liver diseases in patients. Second, it provides a validation of the simulation of a novel low-dose liver imaging technique which can be used to guide future development and experimental implementation of the technique.

Digital tomosynthesis (DTS) is a quasi-three-dimensional (3D) imaging technique which reconstructs images from a limited angle of cone-beam projections with shorter acquisition time, lower imaging dose, and less mechanical constraint than full cone-beam CT (CBCT). However, DTS images reconstructed by the conventional filtered back projection method have low plane-to-plane resolution, and they do not provide full volumetric information for target localization due to the limited angle of the DTS acquisition.

This dissertation presents the optimization and clinical implementation of image guided radiation therapy using limited-angle projections.

A hybrid multiresolution rigid-body registration technique was developed to automatically register reference DTS images with on-board DTS images to guide patient positioning in radiation therapy. This hybrid registration technique uses a faster but less accurate static method to achieve an initial registration, followed by a slower but more accurate adaptive method to fine tune the registration. A multiresolution scheme is employed in the registration to further improve the registration accuracy, robustness and efficiency. Normalized mutual information is selected as the criterion for the similarity measure, and the downhill simplex method is used as the search engine. This technique was tested using image data both from an anthropomorphic chest phantom and from head-and-neck cancer patients. The effects of the scan angle and the region-of-interest size on the registration accuracy and robustness were investigated. The average capture ranges in single-axis simulations with a 44° scan angle and a large ROI covering the entire DTS volume were between -31 and +34 deg for rotations and between -89 and +78 mm for translations in the phantom study, and between -38 and +38 deg for rotations and between -58 and +65 mm for translations in the patient study.

Additionally, a novel limited-angle CBCT estimation method using a deformation field map was developed to optimally estimate volumetric information of organ deformation for soft tissue alignment in image guided radiation therapy. The deformation field map is solved by using prior information, a deformation model, and new projection data. Patients' previous CBCT data are used as the prior information, and the new patient volume to be estimated is considered as a deformation of the prior patient volume. The deformation field is solved by minimizing bending energy and maintaining new projection data fidelity using a nonlinear conjugate gradient method. The new patient CBCT volume is then obtained by deforming the prior patient CBCT volume according to the solution to the deformation field. The method was tested for different scan angles in 2D and 3D cases using simulated and real projections of a Shepp-Logan phantom, liver, prostate and head-and-neck patient data. Hardware acceleration and multiresolution scheme are used to accelerate the 3D estimation process. The accuracy of the estimation was evaluated by comparing organ volume, similarity and pixel value differences between limited-angle CBCT and full-rotation CBCT images. Results showed that the respiratory motion in the liver patient, rectum volume change in the prostate patient, and the weight loss and airway volume change in the head-and-neck patient were accurately estimated in the 60° CBCT images. This new estimation method is able to optimally estimate the volumetric information using 60-degree projection images. It is both technically and clinically feasible for image-guidance in radiation therapy.

Purpose: A variety of cone beam CT (CBCT) systems has been used in the clinic for image guidance in interventional radiology and radiation therapy. Compared with conventional single-source CBCT, dual-source CBCT has the potential for dual-energy imaging and faster scanning. However, it adds additional cross-scatter when compared to a single-source CBCT system, which degrades the image quality. Previously, we developed a synchronized moving grid (SMOG) system to reduce and correct scatter for a single-source CBCT system. The purpose of this work is to implement the SMOG system on a prototype dual-source CBCT system and to investigate its efficacy in scatter reduction and correction under various imaging acquisition settings.

Methods:A 1-D grid was attached to each x-ray source during dual-source CBCT imaging to acquire partially blocked projections. As the grid partially blocked the x-ray primary beams and divided it into multiple quasi-fan beams during the scan, it produced a physical scatter reduction effect in the projections. Phantom data were acquired in the unblocked area, while scatter signal was measured from the blocked area in projections. The scatter distribution was estimated from the measured scatter signals using a cubic spline interpolation for post-scan scatter correction. Complimentary partially blocked projections were acquired at each scan angle by positioning the grid at different locations, and were merged to obtain full projections for reconstruction. In this study, three sets of CBCT images were reconstructed from projections acquired: (a) without grid, (b) with grid but without scatter correction, and (c) with grid and with scatter correction to evaluate the effects of scatter reduction and scatter correction on artifact reduction and improvements of contrast-to-noise ratio index (CNR') and CT number accuracy. The efficacy of the scatter reduction and correction method was evaluated for CATphan phantoms of different diameters (15cm, 20cm, and 30cm), different grids (grid blocking ratios of 1:1 and 2:1), different acquisition modes (simultaneous: two tubes firing at the same time, interleaved: tube alternatively firing and sequential: only one tube firing in one rotation) and different reconstruction algorithms (iterative reconstruction method vs Feldkamp, Davis, and Kress (FDK) back projection method).

Results: The simultaneous scanning mode had the most severe scatter artifacts and the most degraded CNR' when compared to either the interleaved mode or the sequential mode. This is due to the cross-scatter between the two x-ray sources in the simultaneous mode. Scatter artifacts were substantially reduced by scatter reduction and correction. CNR's of the different inserts in the CATphan were enhanced on average by 24%, 13%, and 33% for phantom sizes of 15cm, 20cm, and 30cm, respectively, with only scatter reduction and a 1:1 grid. Correspondingly, CNR's were enhanced by 34%, 18%, and 11%, respectively, with both scatter reduction and correction. However, CNR' may decrease with scatter correction alone for the larger phantom and low contrast ROIs, because of an increase in noise after scatter correction. In addition, the reconstructed HU numbers were linearly correlated to nominal HU numbers. A higher grid blocking ratio, i.e. with a greater blocked area, resulted in better scatter artifact removal and CNR' improvement at the cost of complexity and increased number of exposures. Iterative reconstruction with total variation regularization resulted in better noise reduction and enhanced CNR', in comparison to the FDK method.

Conclusion:Our method with a pre-patient grid can effectively reduce the scatter artifacts, enhance CNR', and modestly improve the CT number linearity for the dual-source CBCT system. The settings such as grid blocking ratio and acquisition mode can be optimized based on the patient-specific condition to further improve image quality.