Simplified statistical image reconstruction for X-ray CT with beam-hardening artifact compensation

CT images are often affected by beam-hardening artifacts due to the polychromatic nature of the X-ray spectra. These artifacts appear in the image as cupping in homogeneous areas and as dark bands between dense regions, such as bones. This paper proposes a simplified statistical reconstruction method for X-ray CT based on Poisson statistics that accounts for the non-linearities caused by beam hardening. The main advantages of the proposed method over previous algorithms is that it avoids the preliminary segmentation step, which can be tricky, especially for low-dose scans, and it does not require knowledge of the whole source spectrum, which is often unknown. Each voxel attenuation is modeled as a mixture of bone and soft tissue by defining density-dependent tissue fractions, maintaining one unknown per voxel. We approximate the energy-dependent attenuation corresponding to different combinations of bone and soft tissue, so called beam-hardening function, with the 1D function corresponding to water plus two parameters that can be tuned empirically. Results on both simulated data with Poisson sinogram noise and two rodent studies acquired with the ARGUSCT system showed a beam hardening reduction (both cupping and dark bands) similar to analytical reconstruction followed by post-processing techniques, but with reduced noise and streaks in cases with low number of projections, as expected for statistical image reconstruction.This work was partially funded by NIH grants R01-HL-098686 and U01 EB018753, by Spanish Ministerio de Economia y Competitividad (projects TEC2013-47270-R and RTC-2014-3028-1) and the Spanish Ministerio de Economia, Industria y Competitividad (projects DPI2016-79075-R AEI/FEDER, UE - Agencia Estatal de Investigación and DTS17/00122 Instituto de Salud Carlos III - FIS), and co-financed by ERDF (FEDER) Funds from the European Commission, “A way of making Europe”. The CNIC is supported by the Spanish Ministerio de Economia, Industria y Competitividad and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).En prens

New reconstruction strategies for polyenergetic X-ray computer tomography

Mención Internacional en el título de doctorX-ray computed tomography (CT) provides a 3D representation of the attenuation coefficients of patient tissues, which are roughly decreasing functions of energy in the usual range of energies used in clinical and preclinical scenarios (from 30 KeV to 150 KeV). Commercial scanners use polychromatic sources, producing a beam having a range of photon energies, because no X-ray lasers exist as a usable alternative. Due to the energy dependence of the attenuation coefficients, low-energy photons are preferably absorbed, causing a shift of the mean energy of the X-ray beam to higher values; this effect is known as beam hardening. Classical reconstruction methods assume a monochromatic source and do not take into account the polychromatic nature of the spectrum, producing two artifacts in the reconstructed image: 1) cupping in large homogeneous areas and 2) dark bands between dense objects such as bone. These artifacts hinder a correct visualization of the image and the recovery of the true attenuation coefficient values. A fast correction of the beam-hardening artifacts can be performed with the so-called post-processing methods, which use the information of a segmentation obtained in a preliminary reconstruction. Nevertheless, this segmentation may fail in low-dose scenarios, leading to an increase of the artifacts. An alternative for these scenarios is the use of iterative methods that incorporate a beam-hardening model, at a cost of higher of computational time compared to post-processing methods. All previously proposed methods require either knowledge of the X-ray spectrum, which is not always available, or the heuristic selection of some parameters, which have been shown not to be optimal for the correction of different slices in heterogeneous studies. This thesis is framed in a research line focused on improving radiology systems of the Biomedical Imaging and Instrumentation Group (BiiG) from the Bioengineering and Aerospace Department of Universidad Carlos III de Madrid. This research line is carried out in collaboration with the Unidad de Medicina y Cirugía experimental of Hospital Gregorio Marañón through Instituto de Investigación Sanitaria Gregorio Marañón, the Electrical Engineering and Computer Science (EECS) department of the University of Michigan and SEDECAL, a Spanish company among the ten best world companies in medical imaging that exports medical devices to 130 countries. As part of this research line, a high-resolution micro-CT was developed for small-animal samples, which operates at low voltages, leading to strong beam-hardening artifacts. This scanner allows preclinical studies to be carried out, which can be divided into cross-sectional and longitudinal studies. Since cross-sectional studies consist of one acquisition at a specific point in time, radiation dose is not an issue, allowing for the use of standard-dose protocols with good image quality. In contrast, longitudinal studies consist of several acquisitions over time, so it is advisable to use low-dose protocols, despite the reduction of signal to noise ratio and the risk of artifacts in the image. This thesis presents a bundle of reconstruction strategies to cope with the beam-hardening effect in different dose scenarios, overcoming the problems of methods previously proposed in the literature. Since image quality is not an issue in the standard-dose scenarios, the speed of the strategies becomes a priority, advising for post-processing strategies. The main advantage of the proposed post-processing strategy is the inclusion of empirical models of the beam-hardening effect, either through a simple calibration phantom or through the information provided by the sample, which eliminates the need of the knowledge of the spectrum or tunning parameters. The evaluation against previously proposed correction methods with real and simulated data showed a good artifact compensation for a standarddose scenario (cross-sectional studies), while not optimum in a low-dose scenario, as expected. For longitudinal studies, where the reduction of dose delivered to the sample is advisable, this thesis presents an iterative method that incorporates the mentioned experimental beam-hardening models. The evaluation with real and simulated data and different dose scenarios showed excellent results but with the known drawback of high computational time. Finally, a deep-learning approach was explored with the idea of looking for a joint solution that would require low-computational time and, at the same time, compensate the beam-hardening artifacts regardless the dose scenario. The chosen architecture is U-net++, based on an encoder-decoder, with the mean-squared error as the cost function. Results in real data showed a good compensation of the beam-hardening and low-dose artifacts with a considerable reduction of time, rising the interest of further exploring this path in the future. The incorporation of these reconstruction strategies in real scanners is straightforward, only requiring a small modification of the calibration step already implemented in commercial scanners. The methods are being transferred to the company SEDECAL for their implementation in the new generation of micro-CT scanners for preclinical research and a multipurpose C-arm for veterinary applications.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Jorge Ripoll Lorenzo.- Secretario: José Vicente Manjón Herrera.- Vocal: Adam M. Alessi

New method for correcting beam-hardening artifacts in CT images via deep learning

Proceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, 19-23 July 2021, Leuven, Belgium.Beam-hardening is the increase of the mean energy of an X-ray beam as it traverses a material. This effect produces two artifacts in the reconstructed image: cupping in homogeneous regions and dark bands among dense areas in heterogeneous regions. The correction methods proposed in the literature can be divided into post-processing and iterative methods. The former methods usually need a bone segmentation, which can fail in low-dose acquisitions, while the latter methods need several projections and reconstructions, increasing the computation time. In this work, we propose a new method for correcting the beamhardening artifacts in CT based on deep learning. A U-Net network was trained with rodent data for two scenarios: standard and low-dose. Results in an independent rodent study showed an optimum correction for both scenarios, similar to that of iterative approaches, but with a reduction of computational time of two orders of magnitude.This work has been supported by project "DEEPCT-CMUC3M", funded by the call "Programa de apoyo a la realización de proyectos interdisciplinares de I+D para jóvenes investigadores de la UC3M 2019-2020, Convenio Plurianual CAM - UC3M" and project "RADCOV19", funded by CRUE Universidades, CSIC and Banco Santander (Fondo Supera). The CNIC is supported by the Ministerio de Ciencia, Innovación y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505)

State of the art: iterative CT reconstruction techniques

Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have become a clinically viable option in computed tomographic (CT) imaging. Substantial evidence is accumulating about the advantages of IR algorithms over established analytical methods, such as filtered back projection. IR improves image quality through cyclic image processing. Although all available solutions share the common mechanism of artifact reduction and/or potential for radiation dose savings, chiefly due to image noise suppression, the magnitude of these effects depends on the specific IR algorithm. In the first section of this contribution, the technical bases of IR are briefly reviewed and the currently available algorithms released by the major CT manufacturers are described. In the second part, the current status of their clinical implementation is surveyed. Regardless of the applied IR algorithm, the available evidence attests to the substantial potential of IR algorithms for overcoming traditional limitations in CT imaging

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry.This work was partially funded by AMIT project from the CDTI CENIT program, TEC2007-64731, TEC2008-06715- C02-01, RD07/0014/2009, TRA2009 0175, RECAVA-RETIC, and RD09/0077/00087 (Ministerio de Ciencia e Inovación), and ARTEMIS S2009/DPI-1802 (Comunidad de Madrid).Publicad

Maximum-Likelihood Dual-Energy TomographicImage Reconstruction

Dual-energy (DE) X-ray computed tomography (CT) has shown promise for material characterization and for providing quantitatively accurate CT values in a variety of applications. However, DE-CT has not been used routinely in medicine to date, primarily due to dose considerations. Most methods for DE-CT have used the filtered backprojection method for image reconstruction, leading to suboptimal noise/dose properties. This paper describes a statistical (maximum-likelihood) method for dual-energy X-ray CT that accommodates a wide variety of potential system configurations and measurement noise models. Regularized methods (such as penalized-likelihood or Bayesian estimation) are straightforward extensions. One version of the algorithm monotonically decreases the negative log-likelihood cost function each iteration. An ordered-subsets variation of the algorithm provides a fast and practical version.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85934/1/Fessler172.pd

Evaluation of Motion Artifact Metrics for Coronary CT Angiography

Purpose This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best‐phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground‐truth motion artifact scores from a series of pairwise comparisons. Method Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low‐Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine‐filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground‐truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground‐truth reader score. The Kendall\u27s Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed. Results On phantom images, the Kendall\u27s Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall\u27s Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall\u27s Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall\u27s Tau coefficient of 0.65. Conclusion The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images

IMPROVED IMAGE QUALITY IN CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED INTERVENTIONS

In the past few decades, cone-beam computed tomography (CBCT) emerged as a rapidly developing imaging modality that provides single rotation 3D volumetric reconstruction with sub-millimeter spatial resolution. Compared to the conventional multi-detector CT (MDCT), CBCT exhibited a number of characteristics that are well suited to applications in image-guided interventions, including improved mechanical simplicity, higher portability, and lower cost. Although the current generation of CBCT has shown strong promise for high-resolution and high-contrast imaging (e.g., visualization of bone structures and surgical instrumentation), it is often believed that CBCT yields inferior contrast resolution compared to MDCT and is not suitable for soft-tissue imaging. Aiming at expanding the utility of CBCT in image-guided interventions, this dissertation concerns the development of advanced imaging systems and algorithms to tackle the challenges of soft-tissue contrast resolution. The presented material includes work encompassing: (i) a comprehensive simulation platform to generate realistic CBCT projections (e.g., as training data for deep learning approaches); (ii) a new projection domain statistical noise model to improve the noise-resolution tradeoff in model-based iterative reconstruction (MBIR); (iii) a novel method to avoid CBCT metal artifacts by optimization of the source-detector orbit; (iv) an integrated software pipeline to correct various forms of CBCT artifacts (i.e., lag, glare, scatter, beam hardening, patient motion, and truncation); (v) a new 3D reconstruction method that only reconstructs the difference image from the image prior for use in CBCT neuro-angiography; and (vi) a novel method for 3D image reconstruction (DL-Recon) that combines deep learning (DL)-based image synthesis network with physics-based models based on Bayesian estimation of the statical uncertainty of the neural network. Specific clinical challenges were investigated in monitoring patients in the neurological critical care unit (NCCU) and advancing intraoperative soft-tissue imaging capability in image-guided spinal and intracranial neurosurgery. The results show that the methods proposed in this work substantially improved soft-tissue contrast in CBCT. The thesis demonstrates that advanced imaging approaches based on accurate system models, novel artifact reduction methods, and emerging 3D image reconstruction algorithms can effectively tackle current challenges in soft-tissue contrast resolution and expand the application of CBCT in image-guided interventions

The color of X-rays: Spectral X-ray computed tomography using energy sensitive pixel detectors

Energy sensitive X-ray imaging detectors are produced by connecting a semiconductor sensor to a spectroscopic pixel readout chip. In this thesis, the applicability of such detectors to X-ray Computed Tomography (CT) is studied. A prototype Medipix based silicon detector is calibrated using X-ray fluorescence. The charge transport properties of the sensor are characterized using a high energy beam of charged particles at the Super Proton Synchrotron (SPS) at the European Center for Nuclear Research (CERN). Monochromatic X-rays at the European Synchrotron Radiation Facility (ESRF) are used to determined the energy response function. These data are used to implement a physics-based CT projection operator that accounts for the transmission of the source spectrum through the sample and detector effects. Based on this projection operator, an iterative spectral CT reconstruction algorithm is developed by extending an Ordered Subset Expectation Maximization (OSEM) method. Subsequently, a maximum likelihood based algorithm is implemented by exporting RooFit, an analysis tool widely employed in high energy physics, to CT. Simulations in both cases show that spectral CT is beneficial for minimizing beam hardening artifacts and achieve improved material resolution. Finally, the results and methods are discussed in terms of their potential societal impact