Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <+/-3%; dose <+/-20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.Comment: Accepted in Medical Image Analysi

Microwave Imaging to Improve Breast Cancer Diagnosis

Breast cancer is the most prevalent type of cancer worldwide. The correct diagnosis of Axillary Lymph Nodes (ALNs) is important for an accurate staging of breast cancer. The performance of current imaging modalities for both breast cancer detection and staging is still unsatisfactory. Microwave Imaging (MWI) has been studied to aid breast cancer diagnosis. This thesis addresses several novel aspects of the development of air-operated MWI systems for both breast cancer detection and staging. Firstly, refraction effects in air-operated setups are evaluated to understand whether refraction calculation should be included in image reconstruction algorithms. Then, the research completed towards the development of a MWI system to detect the ALNs is presented. Anthropomorphic numerical phantoms of the axillary region are created, and the dielectric properties of ALNs are estimated from Magnetic Resonance Imaging exams. The first pre-clinical MWI setup tailored to detect ALNs is numerically and experimentally tested. To complement MWI results, the feasibility of using machine learning algorithms to classify healthy and metastasised ALNs using microwave signals is analysed. Finally, an additional study towards breast cancer detection is presented by proposing a prototype which uses a focal system to focus the energy into the breast and decrease the coupling between antennas. The results show refraction calculation may be neglected in low to moderate permittivity media. Moreover, MWI has the potential as an imaging technique to assess ALN diagnosis as estimation of dielectric properties indicate there is sufficient contrast between healthy and metastasised ALNs, and the imaging results obtained in this thesis are promising for ALN detection. The performance of classification models shows these models may potentially give complementary information to imaging results. The proposed breast imaging prototype also shows promising results for breast cancer detection

Models of breast lesions based on three-dimensional X-ray breast images

This paper presents a method for creation of computational models of breast lesions with irregular shapes from patient Digital Breast Tomosynthesis (DBT) images or breast cadavers and whole-body Computed Tomography (CT) images. The approach includes six basic steps: (a) normalization of the intensity of the tomographic images; (b) image noise reduction; (c) binarization of the lesion area, (d) application of morphological operations to further decrease the level of artefacts; (e) application of a region growing technique to segment the lesion; and (f) creation of a final 3D lesion model. The algorithm is semi-automatic as the initial selection of the region of the lesion and the seeds for the region growing are done interactively. A software tool, performing all of the required steps, was developed in MATLAB. The method was tested and evaluated by analysing anonymized sets of DBT patient images diagnosed with lesions. Experienced radiologists evaluated the segmentation of the tumours in the slices and the obtained 3D lesion shapes. They concluded for a quite satisfactory delineation of the lesions. In addition, for three DBT cases, a delineation of the tumours was performed independently by the radiologists. In all cases the abnormality volumes segmented by the proposed algorithm were smaller than those outlined by the experts. The calculated Dice similarity coefficients for algorithm-radiologist and radiologist-radiologist showed similar values. Another selected tumour case was introduced into a computational breast model to recursively assess the algorithm. The relative volume difference between the ground-truth tumour volume and the one obtained by applying the algorithm on the synthetic volume from the virtual DBT study is 5% which demonstrates the satisfactory performance of the proposed segmentation algorithm. The software tool we developed was used to create models of different breast abnormalities, which were then stored in a database for use by researchers working in this field

Effect of denoising on the quality of reconstructed images in digital breast tomosynthesis

Individual projection images in Digital Breast Tomosynthesis (DBT) must be acquired with low levels of radiation,\ud which significantly increases image noise. This work investigates the influence of a denoising algorithm and the\ud Anscombe transformation on the reduction of quantum noise in DBT images. The Anscombe transformation is a\ud variance-stabilizing transformation that converts the signal-dependent quantum noise to an approximately signalindependent\ud Gaussian additive noise. Thus, this transformation allows for the use of conventional denoising algorithms,\ud designed for additive Gaussian noise, on the reduction of quantum noise, by working on the image in the Anscombe\ud domain. In this work, denoising was performed by an adaptive Wiener filter, previously developed for 2D\ud mammography, which was applied to a set of synthetic DBT images generated using a 3D anthropomorphic software\ud breast phantom. Ideal images without noise were also generated in order to provide a ground-truth reference. Denoising\ud was applied separately to DBT projections and to the reconstructed slices. The relative improvement in image quality\ud was assessed using objective image quality metrics, such as peak signal-to-noise ratio (PSNR) and mean structural\ud similarity index (SSIM). Results suggest that denoising works better for tomosynthesis when using the Anscombe\ud transformation and when denoising was applied to each projection image before reconstruction; in this case, an average\ud increase of 9.1 dB in PSNR and 58.3% in SSIM measurements was observed. No significant improvement was observed\ud by using the Anscombe transformation when denoising was applied to reconstructed images, suggesting that the\ud reconstruction algorithm modifies the noise properties of the DBT images.FAPESPCNP

Reducing the Radiation Dose to Women Receiving Cardiac CT Scans

This thesis aims to quantify the reduction in radiation dose deposited in glandular breast tissue achieved by using tilted gantry acquisition during cardiac CT scans. Previous work by Halpern et al. suggested using tilted acquisition parallel to the long axis of the patient’s heart. However, for a larger portion of the population this is not feasible due to the design of current scanners (which are limited to maximum tilt angles of 30 degrees). This study investigated the potential dose reduction and image quality effects at commercially available tilt angles between 0-30 degrees through simulation and experimental studies. Upon IRB approval, datasets from 10 female patients from Froedtert Hospital (Milwaukee, WI) were used to create voxelized phantom models for the computer simulation. Experimental measurements were performed with an anthropomorphic phantom on a clinical CT scanner (Discovery CT750HD, GE Healthcare, Chalfont St. Gilates, England). For both simulation and experimental studies, radiation dose to the breast and reconstructed image signal-to-noise ratio (SNR) was quantified for tilt angles between 0-30 degrees in five degree increments. The simulated and experimental results demonstrate that tilted gantry acquisition reduces the glandular breast dose from cardiac CT scans when compared to conventional (non-tilted) axial scans. Maximum reductions of 33%-81% (mean, 55%) were achieved with a 30-degree gantry tilt. However, a decrease in image quality by approximately 15% when compared to non-tilted images is seen in the simulated results. The image quality is found to remain equivalent, on average, up to a 15 degree tilt

Generation of polychromatic projection for dedicated breast computed tomography simulation using anthropomorphic numerical phantom.

Numerical simulations are fundamental to the development of medical imaging systems because they can save time and effort in research and development. In this study, we developed a method of creating the virtual projection images that are necessary to study dedicated breast computed tomography (BCT) systems. Anthropomorphic software breast phantoms of the conventional compression type were synthesized and redesigned to meet the requirements of dedicated BCT systems. The internal structure of the breast was randomly constructed to develop the proposed phantom, enabling the internal structure of a naturally distributed real breast to be simulated. When using the existing monochromatic photon incidence assumption for projection-image generation, it is not possible to simulate various artifacts caused by the X-ray spectrum, such as the beam hardening effect. Consequently, the system performance could be overestimated. Therefore, we considered the polychromatic spectrum in the projection image generation process and verified the results. The proposed method is expected to be useful for the development and optimization of BCT systems.ope

Design, Fabrication, and Validation of 3D Printed, Patient-Specific Compensators for Postmastectomy Radiation Therapy

The purpose of this study was to use 3D printed, patient-specific tissue compensators to overcome the 3D planning limitations for postmastectomy radiation therapy (PMRT). Tissue compensators can be used to reduce dose heterogeneity, hot and cold spots at field junctions, and treatment complexity, but are currently seldom used due to the difficulty in designing, fabricating, and validating them. To produce compensators using 3D printing technology, suitable materials had to be found and characterized. Several materials were found to be promising, but previously unreported material uncertainties were also discovered that must be carefully controlled for in 3D printing studies. A new algorithm was also created to optimally design the compensator shape to conform the dose to the desired region, while maintaining acceptable geometric considerations for 3D printing. Patients’ dose distributions calculated using this algorithm were superior to dose distributions calculated in those same patients using more conventional matched field plans. To validate the idealized dose distributions, a new technique was developed to 3D print patient-specific, large scale radiotherapy phantoms with dosimeters throughout that can accurately reflect patients’ anatomy better than generalized phantoms. Six of these phantoms were created for a sample of patients with a range of body vi sizes. A sample of compensators was designed and printed for these novel phantoms, and radiation doses were measured and compared to planned dose distributions. Measured doses agreed well with planned doses. This study demonstrates that 3D printed, patient-specific compensators can be used to simplify treatments, and improve dose distributions in PMRT patients relative to their conventional 3D plans. Additionally, the algorithm could be applied to calculate compensators for different treatment sites in the future, and the phantoms developed could be used to perform pseudo in vivo dosimetry measurements for a wide range of radiotherapy experiments

Automatic synthesis of anthropomorphic pulmonary CT phantoms

The great density and structural complexity of pulmonary vessels and airways impose limitations on the generation of accurate reference standards, which are critical in training and in the validation of image processing methods for features such as pulmonary vessel segmentation or artery–vein (AV) separations. The design of synthetic computed tomography (CT) images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image is differentiated unequivocally. This work demonstrates a complete framework to generate computational anthropomorphic CT phantoms of the human lung automatically. Starting from biological and image-based knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. A dataset of 24 labeled anthropomorphic pulmonary CT phantoms were synthesized with the proposed system. Visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems show good correspondence between real and synthetic lungs (p > 0.05 with low Cohen’s d effect size and AUC values), supporting the potentiality of the tool and the usefulness of the generated phantoms in the biomedical image processing field

Modelos de observador aplicados a la detectabilidad de bajo contraste en tomografía computarizada

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, leída el 15/01/2016. Tesis formato europeo (compendio de artículos)Introduction. Medical imaging has become one of the comerstones in modem healthcare. Computed tomography (CT) is a widely used imaging modality in radiology worldwide. This technique allows to obtain three-dimensional volume reconstructions ofdifferent parts of the patient with isotropic spatial resolution. Also, to acquire sharp images of moving organs, such as the heart orthe lungs, without artifacts. The spectrum ofindications which can be tackled with this technique is wide, and it comprises brain perfusion, cardiology, oncology, vascular radiology, interventionism and traumatology, amongst others. CT is a very popular imaging technique, widely implanted in healthcare services worldwide. The amount of CT scans performed per year has been continuously growing in the past decades, which has led to a great benefit for the patients. At the same time, CT exams represent the highest contribution to the collective radiation dose. Patient dose in CT is one order ofmagnitude higher than in conventional X-ray studies. Regarding patient dose in X-ray imaging the ALARA criteria is universally accepted. It states that patient images should be obtained using adose as low as reasonably achievable and compatible with the diagnostic task. Sorne cases ofpatients' radiation overexposure, most ofthem in brain perfusion procedures have come to the public eye and hada great impact in the USA media. These cases, together with the increasing number ofCT scans performed per year, have raised a red flag about the patient imparted doses in CT. Several guidelines and recommendation for dose optimization in CT have been published by different organizations, which have been included in European and National regulations and adopted by CT manufacturers. In CT, the X-ray tube is rotating around the patient, emitting photons in beams from different angles or projections. These photons interact with the tissues in the patient, depending on their energy and the tissue composition and density. A fraction of these photons deposit all or part of their energy inside the patient, resulting in organs absorbed dose. The images are generated using the data from the projections ofthe X-ray beam that reach the detectors after passing through the patient. Each proj ection represents the total integrated attenuation of the X-ray beam along its path. A CT protocol is defined as a collection of settings which can be selected in the CT console and affect the image quality outcome and the patient dose. They can be acquisition parameters such as beam collimation, tube current, rotation time, kV, pitch, or reconstruction parameters such as the slice thickness and spacing, reconstruction filter and method (filtered back projection (FBP) or iterative algorithms). All main CT manufacturers offer default protocols for different indications, depending on the anatomical region. The user can frequently set the protocol parameters selecting amongst a range of values to adapt them to the clinical indication and patient characteristics, such as size or age. The selected settings in the protocol affect greatly image quality and dose. Many combinations ofsean parameters can render an appropriate image quality for a particular study. Protocol optimization is a complex task in CT because most sean protocol parameters are intertwined and affect image quality and patient dose...Introducción. La imagen médica se ha convertido en uno de los pilares en la atención sanitaria actual. La tomografía computarizada (TC) es una modalidad de imagen ampliamente extendida en radiología en todo el mundo. Esta técnica permite adquirir imágenes de órganos en movimiento, como el corazón o los pulmones, sin artefactos. También permite obtener reconstrucciones de volúmenes tridimensionales de distintas partes del cuerpo de los pacientes. El abanico de indicaciones que pueden abordarse con esta técnica es amplio, e incluye la perfusión cerebral, cardiología, oncología, radiología vascular, intervencionismo y traumatología, entre otras. La TC es una técnica de imagen muy popular, ampliamente implantada en los servicios de salud de hospitales de todo el mundo. El número de estudios de TC hechos anualmente ha crecido de manera continua en las últimas décadas, lo que ha supuesto un gran beneficio para los pacientes. A la vez, los exámenes de TC representan la contribución más alta a la dosis de radiación colectiva en la actualidad. La dosis que reciben los pacientes en un estudio de TC es un orden de magnitud más alta que en exámenes de radiología convencional. En relación con la dosis a pacientes en radiodiagnóstico, el criterio ALARA es aceptado universalmente. Expone que las imágenes de los pacientes deberían obtenerse utilizando una dosis tan baja como sea razonablemente posible y compatible con el objetivo diagnóstico de la prueba. Algunos casos de sobreexposición de pacientes a la radiación, la mayoría en exámenes de perfusión cerebral, se han hecho públicos, lo que ha tenido un gran impacto en los medios de comunicación de EEUU. Estos accidentes, junto con el creciente número de exámenes TC anuales, han hecho aumentar la preocupación sobre las dosis de radiación impartidas a los pacientes en TC. V arias guías y recomendaciones para la optimización de la dosis en TC han sido publicadas por distintas organizaciones, y han sido incluidas en normas europeas y nacionales y adoptadas parcialmente por los fabricantes de equipos de TC. En TC, el tubo de rayos-X rota en tomo al paciente, emitiendo fotones en haces desde distintos ángulos o proyecciones. Estos fotones interactúan con los tejidos en el paciente, en función de su energía y de la composición y densidad del tejido. Una fracción de estos fotones depositan parte o toda su energía dentro del paciente, dando lugar a la dosis absorbida en los órganos. Las imágenes se generan usando los datos de las proyecciones del haz de rayos-X que alcanzan los detectores tras atravesar al paciente. Cada proyección representa la atenuación total del haz de rayos-X integrada a lo largo de su trayectoria. Un protocolo de TC se define como una colección de opciones que pueden seleccionarse en la consola del equipo y que afectan a la calidad de las imágenes y a la dosis que recibe el paciente. Pueden ser parámetros de adquisición, tales como la colimación del haz, la intensidad de corriente, el tiempo de rotación, el kV, el factor de paso parámetros de reconstrucción como el espesor y espaciado de corte, el filtro y el método de reconstrucción (retroproyección filtrada (FBP) o algoritmos iterativos). Los principales fabricantes de equipos de TC ofrecen protocolos recomendados para distintas indicaciones, dependiendo de la región anatómica. El usuario con frecuencia fija los parámetros del protocolo eligiendo entre un rango de valores disponibles, para adaptarlo a la indicación clínica y a las características del paciente, tales como su tamaño o edad. Las condiciones seleccionadas en el protocolo tienen un gran impacto en la calidad de imagen y la dosis. Múltiples combinaciones de los parámetros pueden dar lugar a un nivel de calidad de imagen apropiado para un estudio en concreto. La optimización de los protocolos es una tarea compleja en TC, ya que la mayoría de los parámetros del protocolo están relacionados entre sí y afectan a la calidad de imagen y a la dosis que recibe el paciente...Depto. de Radiología, Rehabilitación y FisioterapiaFac. de MedicinaTRUEunpu