Truncation artifact correction for micro-CT scanners

The work included in this project is framed on one of the lines of research carried out at the Laboratorio de Imagen Médica de la Unidad de Medicina y Cirugía Experimental (UMCE) of Hospital General Universitario Gregorio Marañón and the Bioengineering and Aerospace Department of Universidad Carlos III de Madrid. Its goal is to design, develop and evaluate new data acquisition systems, processing and reconstruction of multimodal images for application in preclinical research. Inside this research line, an x-ray computed tomography (micro-CT add on) system of high resolution has been designed for small animal. Nowadays, computed tomography (CT) is one of the techniques most widely used to obtain anatomical information from living subjects. Different artifacts from different nature usually degrade the qualitative and quantitative analysis of these images. This creates the urgent need of developing algorithms to compensate and/or reduce these artifacts. The general objective of the present thesis is to implement a method for compensating truncation artifact in the micro-CT add-on scanner for small animal developed at Hospital Universitario Gregorio Marañón. This artifact appears due to the acquisition of incomplete x-ray projections when part of the sample, especially obese rats, lies outside the field of view. As a result of these data inconsistencies, bright shading artifacts and quantification errors in the images may appear after the reconstruction process. First of all, truncation artifact in the high resolution micro-CT add-on scanner was studied. Then, after a review of the proposed methods in the literature, the optimal approach for the micro-CT add-on was selected, based on a sinogram extrapolation technique developed by Ohnesorge et al [1]. This method consists on a symmetric mirroring extrapolation of the truncated projections that guarantees continuity at the truncation point. It includes a sine shaping effect that ensures a smooth attenuation signal drop. Truncation artifact correction method has been validated in simulated and real studies. Results show an overall significant reduction of truncation artifact. This algorithm has been adapted and implemented in the reconstruction interface of the preclinical high-resolution micro-CT scanner, which is manufactured by SEDECAL S.L. and commercialized worldwide.El trabajo de este proyecto se encuadra dentro de una línea de investigación que se desarrolla en el Laboratorio de Imagen Médica de la Unidad de Medicina y Cirugía Experimental (UMCE) del Hospital General Universitario Gregorio Marañón y el Departamento de Bioingeniería e Ingeniería Aeroespacial de la Universidad Carlos III de Madrid. Su objetivo es diseñar, desarrollar y evaluar nuevos sistemas de adquisición de datos, procesamiento y reconstrucción de imágenes multi-modales para aplicaciones en investigación preclínica. Dentro de esta línea de investigación se ha desarrollado un tomógrafo de rayos X de alta resolución para pequeños animales (micro-TAC add-on). Actualmente, la tomografía axial computarizada es una de las técnicas más ampliamente utilizadas para la obtención de información anatómica in vivo. Existe una serie de artefactos de distinta naturaleza en este tipo de imágenes que generalmente degradan y dificultan el análisis cualitativo y cuantitativo de las imágenes, dando lugar a una necesidad imperante de desarrollar algoritmos de corrección y/o reducción de estos artefactos. El objetivo general del presente proyecto es la implementación de un algoritmo para la corrección del artefacto de truncamiento en el escáner micro-TAC add-on desarrollado en el Hospital Universitario Gregorio Marañón. Este artefacto aparece debido a la adquisición de proyecciones incompletas cuando parte de la muestra, especialmente ratas obesas, se extiende fuera del campo de visión. Estas inconsistencias en los datos obtenidos pueden dar lugar a la aparición de bandas brillantes y errores en la cuantificación de las imágenes después del proceso de reconstrucción. En primer lugar, se ha estudiado el artefacto de truncamiento en el escáner micro-TAC add-on de alta resolución. Seguidamente, se ha llevado a cabo una revisión de los métodos propuestos en la bibliografía, seleccionando una estrategia óptima para el micro-TAC add-on bajo estudio: una técnica de extrapolación del sinograma publicado por Ohnesorge et al [1]. Este método consiste en una extrapolación de espejo simétrico de las proyecciones truncadas que garantiza la continuidad en el punto de truncamiento. Incluye el modelado de una sinusoide que asegura una caída de señal en los valores de atenuación suave. Este método ha sido validado en estudios simulados y reales. Los resultados muestran una clara reducción del artefacto de truncamiento. El resultado de este proyecto ha sido incorporado en la interfaz de reconstrucción del escáner pre-clínico micro-TAC add-on de alta resolución fabricado por SEDECAL S.A. y comercializado por todo el mundo.Ingeniería Biomédic

Improving Image Reconstruction for Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) has been developed to reduce the issue of overlapping tissue in conventional 2-D mammography for breast cancer screening and diagnosis. In the DBT procedure, the patient’s breast is compressed with a paddle and a sequence of x-ray projections is taken within a small angular range. Tomographic reconstruction algorithms are then applied to these projections, generating tomosynthesized image slices of the breast, such that radiologists can read the breast slice by slice. Studies have shown that DBT can reduce both false-negative diagnoses of breast cancer and false-positive recalls compared to mammography alone. This dissertation focuses on improving image quality for DBT reconstruction. Chapter I briefly introduces the concept of DBT and the inspiration of my study. Chapter II covers the background of my research including the concept of image reconstruction, the geometry of our experimental DBT system and figures of merit for image quality. Chapter III introduces our study of the segmented separable footprint (SG) projector. By taking into account the finite size of detector element, the SG projector improves the accuracy of forward projections in iterative image reconstruction. Due to the more efficient access to memory, the SG projector is also faster than the traditional ray-tracing (RT) projector. We applied the SG projector to regular and subpixel reconstructions and demonstrated its effectiveness. Chapter IV introduces a new DBT reconstruction method with detector blur and correlated noise modeling, called the SQS-DBCN algorithm. The SQS-DBCN algorithm is able to significantly enhance microcalcifications (MC) in DBT while preserving the appearance of the soft tissue and mass margin. Comparisons between the SQS-DBCN algorithm and several modified versions of the SQS-DBCN algorithm indicate the importance of modeling different components of the system physics at the same time. Chapter V investigates truncated projection artifact (TPA) removal algorithms. Among the three algorithms we proposed, the pre-reconstruction-based projection view (PV) extrapolation method provides the best performance. Possible improvements of the other two TPA removal algorithms have been discussed. Chapter VI of this dissertation examines the effect of source blur on DBT reconstruction. Our analytical calculation demonstrates that the point spread function (PSF) of source blur is highly shift-variant. We used CatSim to simulate digital phantoms. Analysis on the reconstructed images demonstrates that a typical finite-sized focal spot (~ 0.3 mm) will not affect the image quality if the x-ray tube is stationary during the data acquisition. For DBT systems with continuous-motion data acquisition, the motion of the x-ray tube is the main cause of the effective source blur and will cause loss in the contrast of objects. Therefore modeling the source blur for these DBT systems could potentially improve the reconstructed image quality. The final chapter of this dissertation discusses a few future studies that are inspired by my PhD research.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144059/1/jiabei_1.pd

Cost-effective non-destructive testing of biomedical components fabricated using additive manufacturing

Biocompatible titanium-alloys can be used to fabricate patient-specific medical components using additive manufacturing (AM). These novel components have the potential to improve clinical outcomes in various medical scenarios. However, AM introduces stability and repeatability concerns, which are potential roadblocks for its widespread use in the medical sector. Micro-CT imaging for non-destructive testing (NDT) is an effective solution for post-manufacturing quality control of these components. Unfortunately, current micro-CT NDT scanners require expensive infrastructure and hardware, which translates into prohibitively expensive routine NDT. Furthermore, the limited dynamic-range of these scanners can cause severe image artifacts that may compromise the diagnostic value of the non-destructive test. Finally, the cone-beam geometry of these scanners makes them susceptible to the adverse effects of scattered radiation, which is another source of artifacts in micro-CT imaging. In this work, we describe the design, fabrication, and implementation of a dedicated, cost-effective micro-CT scanner for NDT of AM-fabricated biomedical components. Our scanner reduces the limitations of costly image-based NDT by optimizing the scanner\u27s geometry and the image acquisition hardware (i.e., X-ray source and detector). Additionally, we describe two novel techniques to reduce image artifacts caused by photon-starvation and scatter radiation in cone-beam micro-CT imaging. Our cost-effective scanner was designed to match the image requirements of medium-size titanium-alloy medical components. We optimized the image acquisition hardware by using an 80 kVp low-cost portable X-ray unit and developing a low-cost lens-coupled X-ray detector. Image artifacts caused by photon-starvation were reduced by implementing dual-exposure high-dynamic-range radiography. For scatter mitigation, we describe the design, manufacturing, and testing of a large-area, highly-focused, two-dimensional, anti-scatter grid. Our results demonstrate that cost-effective NDT using low-cost equipment is feasible for medium-sized, titanium-alloy, AM-fabricated medical components. Our proposed high-dynamic-range strategy improved by 37% the penetration capabilities of an 80 kVp micro-CT imaging system for a total x-ray path length of 19.8 mm. Finally, our novel anti-scatter grid provided a 65% improvement in CT number accuracy and a 48% improvement in low-contrast visualization. Our proposed cost-effective scanner and artifact reduction strategies have the potential to improve patient care by accelerating the widespread use of patient-specific, bio-compatible, AM-manufactured, medical components

Automatic quantification of intravascular optical coherence tomography

As mentioned above, IVOCT, as a novel imaging modality, has played an active role in a wide range of CAD applications, including research and clinical routine. Due to its unparalleled high resolution and the ability to delineate complex vascular structures, IVOCT technology makes many precise measurement and novel applications possible. However, currently, a lot of analyses in IVOCT images are still relying on the manual work which decreases their value. The goal of this thesis is to develop robust and accurate (semi)automated methods that can detect and segment the interesting components in IVOCT pullback runs, such as implanted stent struts and side branches in 3D for accurate measurement, so that the results could contribute to medical research as well as for clinical decision-making. My thesis presents four different automated algorithms to detect metallic stent struts, bioresorbable vascular scaffold struts, side branches and all the common components in IVOCT images. It also presented a semi-automated method to assess the stent support to vessel wall and the stent-jailed side branch access through stent cells in 3-dimentional spaceChina Scholarship Council (CSC)UBL - phd migration 201

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra