Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation

The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency in reconstruction kernels is important as the underlying CT texture can impact measurements during quantitative image analysis. Harmonization (i.e., kernel conversion) minimizes differences in measurements due to inconsistent reconstruction kernels. Existing methods investigate harmonization of CT scans in single or multiple manufacturers. However, these methods require paired scans of hard and soft reconstruction kernels that are spatially and anatomically aligned. Additionally, a large number of models need to be trained across different kernel pairs within manufacturers. In this study, we adopt an unpaired image translation approach to investigate harmonization between and across reconstruction kernels from different manufacturers by constructing a multipath cycle generative adversarial network (GAN). We use hard and soft reconstruction kernels from the Siemens and GE vendors from the National Lung Screening Trial dataset. We use 50 scans from each reconstruction kernel and train a multipath cycle GAN. To evaluate the effect of harmonization on the reconstruction kernels, we harmonize 50 scans each from Siemens hard kernel, GE soft kernel and GE hard kernel to a reference Siemens soft kernel (B30f) and evaluate percent emphysema. We fit a linear model by considering the age, smoking status, sex and vendor and perform an analysis of variance (ANOVA) on the emphysema scores. Our approach minimizes differences in emphysema measurement and highlights the impact of age, sex, smoking status and vendor on emphysema quantification.Comment: 9 pages, 6 figures, 1 table, Submitted to SPIE Medical Imaging : Image Processing. San Diego, CA. February 202

A study of wavelet-based noise reduction techniques in mammograms

Breast cancer is one of the most common cancers and claims over one thousand lives every day. Breast cancer turns fatal only when diagnosed in late stages, but can be cured when diagnosed in its early stages. Over the last two decades, Digital Mammography has served the diagnosis of breast cancer. It is a very powerful aid for early detection of breast cancer. However, the images produced by mammography typically contain a great amount noise from the inherent characteristics of the imaging system and the radiation involved. Shot noise or quantum noise is the most significant noise which emerges as a result of uneven distribution of incident photons on the receptor. The X-ray dose given to patients must be minimized because of the risk of exposure. This noise present in mammograms manifests itself more when the dose of X-ray radiation is less and therefore needs to be treated before enhancing the mammogram for contrast and clarity. Several approaches have been taken to reduce the amount of noise in mammograms. This thesis presents a study of the wavelet-based techniques employed for noise reduction in mammograms --Abstract, page iii

Advanced acquisition and reconstruction techniques in magnetic resonance imaging

Mención Internacional en el título de doctorMagnetic Resonance Imaging (MRI) is a biomedical imaging modality with outstanding features such as excellent soft tissue contrast and very high spatial resolution. Despite its great properties, MRI suffers from some drawbacks, such as low sensitivity and long acquisition times. This thesis focuses on providing solutions for the second MR drawback, through the use of compressed sensing methodologies. Compressed sensing is a novel technique that enables the reduction of acquisition times and can also improve spatiotemporal resolution and image quality. Compressed sensing surpasses the traditional limits of Nyquist sampling theories by enabling the reconstruction of images from an incomplete number of acquired samples, provided that 1) the images to reconstruct have a sparse representation in a certain domain, 2) the undersampling applied is random and 3) specific non-linear reconstruction algorithms are used. Cardiovascular MRI has to overcome many limitations derived from the respiratory and cardiac cycles, and has very strict requirements in terms of spatiotemporal resolution. Hence, any improvement in terms of reducing acquisition times or increasing image quality by means of compressed sensing will be highly beneficial. This thesis aims to investigate the benefits that compressed sensing may provide in two cardiovascular MR applications: The acquisition of small-animal cardiac cine images and the visualization of human coronary atherosclerotic plaques. Cardiac cine in small-animals is a widely used approach to assess cardiovascular function. In this work we proposed a new compressed sensing methodology to reduce acquisition times in self-gated cardiac cine sequences. This methodology was developed as a modification of the Split Bregman reconstruction algorithm to include the minimization of Total Variation across both spatial and temporal dimensions. We simulated compressed sensing acquisitions by retrospectively undersampling complete acquisitions. The accuracy of the results was evaluated with functional measurements in both healthy animals and animals with myocardial infarction. The method reached accelerations rates of 10-14 for healthy animals and acceleration rates of 10 in the case of unhealthy animals. We verified these theoretically-feasible acceleration factors in practice with the implementation of a real compressed sensing acquisition in a 7 T small-animal MR scanner. We demonstrated that acceleration factors around 10 are achievable in practice, close to those obtained in the previous simulations. However, we found some small differences in image quality between simulated and real undersampled compressed sensing reconstructions at high acceleration rates; this might be explained by differences in their sensitivity to motion contamination during acquisition. The second cardiovascular application explored in this thesis is the visualization of atherosclerotic plaques in coronary arteries in humans. Nowadays, in vivo visualization and classification of plaques by MRI is not yet technically feasible. Acceleration techniques such as compressed sensing may greatly contribute to the feasibility of the application in vivo. However, it is advisable to carry out a systematic study of the basic technical requirements for the coronary plaque visualization prior to designing specific acquisition techniques. On simulation studies we assessed spatial resolution, SNR and motion limits required for the proper visualization of coronary plaques and we proposed a new hybrid acquisition scheme that reduces sensitivity to motion. In order to evaluate the benefits that acceleration techniques might provide, we evaluated different parallel imaging algorithms and we also implemented a compressed sensing methodology that incorporates information from the coil sensitivity profile of the phased-array coil used. We found that, with the coil setup analyzed, acceleration benefits were greatly limited by the small size of the FOV of interest. Thus, dedicated phased-arrays need to be designed to enhance the benefits that accelerating techniques may provide on coronary artery plaque imaging in vivo.La Imagen por Resonancia Magnética (IRM) es una modalidad de imagen biomédica con notables características tales como un excelente contraste en tejidos blandos y una muy alta resolución espacial. Sin embargo, a pesar de estas importantes propiedades, la IRM tiene algunos inconvenientes, como una baja sensibilidad y tiempos de adquisición muy largos. Esta tesis se centra en buscar soluciones para el segundo inconveniente mencionado a través del uso de metodologías de compressed sensing. Compressed sensing es una técnica novedosa que permite la reducción de los tiempos de adquisición y también la mejora de la resolución espacio-temporal y la calidad de las imágenes. La teoría de compressed sensing va más allá los límites tradicionales de la teoría de muestreo de Nyquist, permitiendo la reconstrucción de imágenes a partir de un número incompleto de muestras siempre que se cumpla que 1) las imágenes a reconstruir tengan una representación dispersa (sparse) en un determinado dominio, 2) el submuestreo aplicado sea aleatorio y 3) se usen algoritmos de reconstrucción no lineales específicos. La resonancia magnética cardiovascular tiene que superar muchas limitaciones derivadas de los ciclos respiratorios y cardiacos, y además tiene que cumplir unos requisitos de resolución espacio-temporal muy estrictos. De ahí que cualquier mejora que se pueda conseguir bien reduciendo tiempos de adquisición o bien aumentando la calidad de las imágenes resultaría altamente beneficiosa. Esta tesis tiene como objetivo investigar los beneficios que la técnica de compressed sensing puede proporcionar a dos aplicaciones punteras en RM cardiovascular, la adquisición de cines cardiacos de pequeño animal y la visualización de placas ateroscleróticas en arterias coronarias en humano. La adquisición de cines cardiacos en pequeño animal es una aplicación ampliamente usada para evaluar función cardiovascular. En esta tesis, proponemos una metodología de compressed sensing para reducir los tiempos de adquisición de secuencias de cine cardiaco denominadas self-gated. Desarrollamos esta metodología modificando el algoritmo de reconstrucción de Split-Bregman para incluir la minimización de la Variación Total a través de la dimensión temporal además de la espacial. Para ello, simulamos adquisiciones de compressed sensing submuestreando retrospectivamente adquisiciones completas. La calidad de los resultados se evaluó con medidas funcionales tanto en animales sanos como en animales a los que se les produjo un infarto cardiaco. El método propuesto mostró que factores de aceleración de 10-14 son posibles para animales sanos y en torno a 10 para animales infartados. Estos factores de aceleración teóricos se verificaron en la práctica mediante la implementación de una adquisición submuestreada en un escáner de IRM de pequeño animal de 7 T. Se demostró que aceleraciones en torno a 10 son factibles en la práctica, valor muy cercano a los obtenidos en las simulaciones previas. Sin embargo para factores de aceleración muy altos, se apreciaron algunas diferencias entre la calidad de las imágenes con submuestreo simulado y las realmente submuestreadas; esto puede ser debido a una mayor sensibilidad a la contaminación por movimiento durante la adquisición. La segunda aplicación cardiovascular explorada en esta tesis es la visualización de placas ateroscleróticas en arterias coronarias en humanos. Hoy en día, la visualización y clasificación in vivo de es te tipo de placas mediante IRM aún no es técnicamente posible. Pero no hay duda de que técnicas de aceleración, como compressed sensing, pueden contribuir enormemente a la consecución de la aplicación in vivo. Sin embargo, como paso previo a la evaluación de las técnicas de aceleración, es conveniente hacer un estudio sistemático de los requerimientos técnicos necesarios para la correcta visualización y caracterización de las placas coronarias. Mediante simulaciones establecimos los límites de señal a ruido, resolución espacial y movimiento requeridos para la correcta visualización de las placas y propusimos un nuevo esquema de adquisición híbrido que reduce la sensibilidad al movimiento. Para valorar los beneficios que las técnicas de aceleración pueden aportar, evaluamos diferentes algoritmos de imagen en paralelo e implementamos una metodología de compresed sensing que tiene en cuenta la información de los mapas de sensibilidad de las antenas utilizadas. En este estudio se encontró, que para la configuración de antenas analizadas, los beneficios de la aceleración están muy limitados por el pequeño campo de visón utilizado. Por tanto, para incrementar los beneficios que estas técnicas de aceleración pueden aportar la imagen de placas coronarias in vivo, es necesario diseñar antenas específicas para esta aplicación.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Elfar Adalsteinsson.- Secretario: Juan Miguel Parra Robles.- Vocal: Pedro Ramos Cabre

Innovative Acoustic Reflection Imaging Techniques And Application To Clinical Breast Tomography

Conventional ultrasound techniques use beam-formed, constant sound speed ray models for fast image reconstruction. However, these techniques are inadequate for the emerging new field of ultrasound tomography (UST). We present a new technique for reconstruction of reflection images from UST data. We have extended the planar Kirchhoff migration method used in geophysics, and combined it with sound speed and attenuation data obtained from the transmission signals to create reflection ultrasound images that are corrected for refractive and attenuative effects. The resulting techniques were applied to simulated numerical phantom data, physical phantom data and in-vivo breast data obtained with an experimental ring transducer prototype. Additionally, the ring transducer was customized to test compatibility with an existing ultrasound workstation. We were able to obtain independently recorded radio-frequency (RF) data for individual transmit-receive pair combinations for all 128 transducers. The signal data was then successfully reconstructed into reflection data using the Kirchhoff migration techniques. The results from the use of sound speed and attenuation corrections lead to significant improvements in image quality, particularly in dense tissues where the refractive and scattering effects are the greatest. The procedure was applied to a variety of breast densities and masses of different natures. The resulting reflection images successfully resolved boundaries and textures. The reflection characteristics of tomographic ultrasound maintain an indispensible position in the quantification of proper mass identification. The results of this project indicate the clinical significance of the invocation of properly compensated Kirchhoff based reconstruction method with the use of sound speed and attenuation parameters for the visualization and classification of masses and tissue

Innovative Acoustic Reflection Imaging Techniques And Application To Clinical Breast Tomography

Conventional ultrasound techniques use beam-formed, constant sound speed ray models for fast image reconstruction. However, these techniques are inadequate for the emerging new field of ultrasound tomography (UST). We present a new technique for reconstruction of reflection images from UST data. We have extended the planar Kirchhoff migration method used in geophysics, and combined it with sound speed and attenuation data obtained from the transmission signals to create reflection ultrasound images that are corrected for refractive and attenuative effects. The resulting techniques were applied to simulated numerical phantom data, physical phantom data and in-vivo breast data obtained with an experimental ring transducer prototype. Additionally, the ring transducer was customized to test compatibility with an existing ultrasound workstation. We were able to obtain independently recorded radio-frequency (RF) data for individual transmit-receive pair combinations for all 128 transducers. The signal data was then successfully reconstructed into reflection data using the Kirchhoff migration techniques. The results from the use of sound speed and attenuation corrections lead to significant improvements in image quality, particularly in dense tissues where the refractive and scattering effects are the greatest. The procedure was applied to a variety of breast densities and masses of different natures. The resulting reflection images successfully resolved boundaries and textures. The reflection characteristics of tomographic ultrasound maintain an indispensible position in the quantification of proper mass identification. The results of this project indicate the clinical significance of the invocation of properly compensated Kirchhoff based reconstruction method with the use of sound speed and attenuation parameters for the visualization and classification of masses and tissue

Model-Based Iterative Reconstruction in Cone-Beam Computed Tomography: Advanced Models of Imaging Physics and Prior Information

Cone-beam computed tomography (CBCT) represents a rapidly developing imaging modality that provides three-dimensional (3D) volumetric images with sub-millimeter spatial resolution and soft-tissue visibility from a single gantry rotation. CBCT tends to have small footprint, mechanical simplicity, open geometry, and low cost compared to conventional diagnostic CT. Because of these features, CBCT has been used in a variety of specialty diagnostic applications, image-guided radiation therapy (on-board CBCT), and surgical guidance (e.g., C-arm based CBCT). However, the current generation of CBCT systems face major challenges in low-contrast, soft-tissue image quality – for example, in the detection of acute intracranial hemorrhage (ICH), which requires a fairly high level of image uniformity, spatial resolution, and contrast resolution. Moreover, conventional approaches in both diagnostic and image-guided interventions that involve a series of imaging studies fail to leverage the wealth of patient-specific anatomical information available from previous scans. Leveraging the knowledge gained from prior images holds the potential for major gains in image quality and dose reduction. Model-based iterative reconstruction (MBIR) attempts to make more efficient use of the measurement data by incorporating a forward model of physical detection processes. Moreover, MBIR allows incorporation of various forms of prior information into image reconstruction, ranging from image smoothness and sharpness to patient-specific anatomical information. By leveraging such advantages, MBIR has demonstrated improved tradeoffs between image quality and radiation dose in multi-detector CT in the past decade and has recently shown similar promise in CBCT. However, the full potential of MBIR in CBCT is yet to be realized. This dissertation explores the capabilities of MBIR in improving image quality (especially low-contrast, soft-tissue image quality) and reducing radiation dose in CBCT. The presented work encompasses new MBIR methods that: i) modify the noise model in MBIR to compensate for noise amplification from artifact correction; ii) design regularization by explicitly incorporating task-based imaging performance as the objective; iii) mitigate truncation effects in a computationally efficient manner; iv) leverage a wealth of patient-specific anatomical information from a previously acquired image; and v) prospectively estimate the optimal amount of prior image information for accurate admission of specific anatomical changes. Specific clinical challenges are investigated in the detection of acute ICH and surveillance of lung nodules. The results show that MBIR can substantially improve low-contrast, soft-tissue image quality in CBCT and enable dose reduction techniques in sequential imaging studies. The thesis demonstrates that novel MBIR methods hold strong potential to overcome conventional barriers to CBCT image quality and open new clinical applications that would benefit from high-quality 3D imaging

High Performance Optical Computed Tomography for Accurate Three-Dimensional Radiation Dosimetry

Optical computed tomography (CT) imaging of radiochromic gel dosimeters is a method for truly three-dimensional radiation dosimetry. Although optical CT dosimetry is not widely used currently due to previous concerns with speed and accuracy, the complexity of modern radiotherapy is increasing the need for a true 3D dosimeter. This thesis reports technical improvements that bring the performance of optical CT to a clinically useful level. New scanner designs and improved scanning and reconstruction techniques are described. First, we designed and implemented a new light source for a cone-beam optical CT system which reduced the scatter to primary contribution in CT projection images of gel dosimeters from approximately 25% to approximately 4%. This design, which has been commercially implemented, enables accurate and fast dosimetry. Second, we designed and constructed a new, single-ray, single-detector parallel-beam optical CT scanner. This system was able to very accurately image both absorbing and scattering objects in large volumes (15 cm diameter), agreeing within ∼1% with independent measurements. It has become a reference standard for evaluation of optical CT geometries and dosimeter formulations. Third, we implemented and characterized an iterative reconstruction algorithm for optical CT imaging of gel dosimeters. This improved image quality in optical CT by suppressing the effects of noise and artifacts by a factor of up to 5. Fourth, we applied a fiducial-based ray path measurement scheme, combined with an iterative reconstruction algorithm, to enable optical CT reconstruction in the case of refractive index mismatch between different media in the scanner’s imaged volume. This improved the practicality of optical CT, as time-consuming mixing of liquids can be avoided. Finally, we applied the new laser scanner to the difficult dosimetry task of small-field measurement. We were able to obtain beam profiles and depth dose curves for 4 fields (3x3 cm2 and below) using one 15 cm diameter dosimeter, within 2 hours. Our gel dosimetry depth-dose curves agreed within ∼1.5% with Monte Carlo simulations. In conclusion, the developments reported here have brought optical CT dosimetry to a clinically useful level. Our techniques will be used to assist future research in gel dosimetry and radiotherapy treatment techniques