3D reconstruction for plastic surgery simulation based on statistical shape models

This thesis has been accomplished in Crisalix in collaboration with the Universitat Pompeu Fabra within the program of Doctorats Industrials. Crisalix has the mission of enhancing the communication between professionals of plastic surgery and patients by providing a solution to the most common question during the surgery planning process of ``How will I look after the surgery?''. The solution proposed by Crisalix is based in 3D imaging technology. This technology generates the 3D reconstruction that accurately represents the area of the patient that is going to be operated. This is followed by the possibility of creating multiple simulations of the plastic procedure, which results in the representation of the possible outcomes of the surgery. This thesis presents a framework capable to reconstruct 3D shapes of faces and breasts of plastic surgery patients from 2D images and 3D scans. The 3D reconstruction of an object is a challenging problem with many inherent ambiguities. Statistical model based methods are a powerful approach to overcome some of these ambiguities. We follow the intuition of maximizing the use of available prior information by introducing it into statistical model based methods to enhance their properties. First, we explore Active Shape Models (ASM) which are a well known method to perform 2D shapes alignment. However, it is challenging to maintain prior information (e.g. small set of given landmarks) unchanged once the statistical model constraints are applied. We propose a new weighted regularized projection into the parameter space which allows us to obtain shapes that at the same time fulfill the imposed shape constraints and are plausible according to the statistical model. Second, we extend this methodology to be applied to 3D Morphable Models (3DMM), which are a widespread method to perform 3D reconstruction. However, existing methods present some limitations. Some of them are based in non-linear optimizations computationally expensive that can get stuck in local minima. Another limitation is that not all the methods provide enough resolution to represent accurately the anatomy details needed for this application. Given the medical use of the application, the accuracy and robustness of the method, are important factors to take into consideration. We show how 3DMM initialization and 3DMM fitting can be improved using our weighted regularized projection. Finally, we present a framework capable to reconstruct 3D shapes of plastic surgery patients from two possible inputs: 2D images and 3D scans. Our method is used in different stages of the 3D reconstruction pipeline: shape alignment; 3DMM initialization and 3DMM fitting. The developed methods have been integrated in the production environment of Crisalix, proving their validity.Aquesta tesi ha estat realitzada a Crisalix amb la col·laboració de la Universitat Pompeu Fabra sota el pla de Doctorats Industrials. Crisalix té com a objectiu la millora de la comunicació entre els professionals de la cirurgia plàstica i els pacients, proporcionant una solució a la pregunta que sorgeix més freqüentment durant el procés de planificació d'una operació quirúrgica ``Com em veuré després de la cirurgia?''. La solució proposada per Crisalix està basada en la tecnologia d'imatge 3D. Aquesta tecnologia genera la reconstrucció 3D de la zona del pacient operada, seguit de la possibilitat de crear múltiples simulacions obtenint la representació dels possibles resultats de la cirurgia. Aquesta tesi presenta un sistema capaç de reconstruir cares i pits de pacients de cirurgia plàstica a partir de fotos 2D i escanegis. La reconstrucció en 3D d'un objecte és un problema complicat degut a la presència d'ambigüitats. Els mètodes basats en models estadístics son adequats per mitigar-les. En aquest treball, hem seguit la intuïció de maximitzar l'ús d'informació prèvia, introduint-la al model estadístic per millorar les seves propietats. En primer lloc, explorem els Active Shape Models (ASM) que són un conegut mètode fet servir per alinear contorns d'objectes 2D. No obstant, un cop aplicades les correccions de forma del model estadístic, es difícil de mantenir informació de la que es disposava a priori (per exemple, un petit conjunt de punts donat) inalterada. Proposem una nova projecció ponderada amb un terme de regularització, que permet obtenir formes que compleixen les restriccions de forma imposades i alhora són plausibles en concordança amb el model estadístic. En segon lloc, ampliem la metodologia per aplicar-la als anomenats 3D Morphable Models (3DMM) que són un mètode extensivament utilitzat per fer reconstrucció 3D. No obstant, els mètodes de 3DMM existents presenten algunes limitacions. Alguns estan basats en optimitzacions no lineals, computacionalment costoses i que poden quedar atrapades en mínims locals. Una altra limitació, és que no tots el mètodes proporcionen la resolució adequada per representar amb precisió els detalls de l'anatomia. Donat l'ús mèdic de l'aplicació, la precisió i la robustesa són factors molt importants a tenir en compte. Mostrem com la inicialització i l'ajustament de 3DMM poden ser millorats fent servir la projecció ponderada amb regularització proposada. Finalment, es presenta un sistema capaç de reconstruir models 3D de pacients de cirurgia plàstica a partir de dos possibles tipus de dades: imatges 2D i escaneigs en 3D. El nostre mètode es fa servir en diverses etapes del procés de reconstrucció: alineament de formes en imatge, la inicialització i l'ajustament de 3DMM. Els mètodes desenvolupats han estat integrats a l'entorn de producció de Crisalix provant la seva validesa

EIT Reconstruction Algorithms: Pitfalls, Challenges and Recent Developments

We review developments, issues and challenges in Electrical Impedance Tomography (EIT), for the 4th Workshop on Biomedical Applications of EIT, Manchester 2003. We focus on the necessity for three dimensional data collection and reconstruction, efficient solution of the forward problem and present and future reconstruction algorithms. We also suggest common pitfalls or ``inverse crimes'' to avoid.Comment: A review paper for the 4th Workshop on Biomedical Applications of EIT, Manchester, UK, 200

Selectiveâ diffusion regularization for enhancement of microcalcifications in digital breast tomosynthesis reconstruction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134765/1/mp5851.pd

Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images

We present an information-theoretic approach to the registration of images with directional information, and especially for diffusion-Weighted Images (DWI), with explicit optimization over the directional scale. We call it Locally Orderless Registration with Directions (LORD). We focus on normalized mutual information as a robust information-theoretic similarity measure for DWI. The framework is an extension of the LOR-DWI density-based hierarchical scale-space model that varies and optimizes the integration, spatial, directional, and intensity scales. As affine transformations are insufficient for inter-subject registration, we extend the model to non-rigid deformations. We illustrate that the proposed model deforms orientation distribution functions (ODFs) correctly and is capable of handling the classic complex challenges in DWI-registrations, such as the registration of fiber-crossings along with kissing, fanning, and interleaving fibers. Our experimental results clearly illustrate a novel promising regularizing effect, that comes from the nonlinear orientation-based cost function. We show the properties of the different image scales and, we show that including orientational information in our model makes the model better at retrieving deformations in contrast to standard scalar-based registration.Comment: 16 pages, 19 figure

System Characterizations and Optimized Reconstruction Methods for Novel X-ray Imaging

In the past decade there have been many new emerging X-ray based imaging technologies developed for different diagnostic purposes or imaging tasks. However, there exist one or more specific problems that prevent them from being effectively or efficiently employed. In this dissertation, four different novel X-ray based imaging technologies are discussed, including propagation-based phase-contrast (PB-XPC) tomosynthesis, differential X-ray phase-contrast tomography (D-XPCT), projection-based dual-energy computed radiography (DECR), and tetrahedron beam computed tomography (TBCT). System characteristics are analyzed or optimized reconstruction methods are proposed for these imaging modalities. In the first part, we investigated the unique properties of propagation-based phase-contrast imaging technique when combined with the X-ray tomosynthesis. Fourier slice theorem implies that the high frequency components collected in the tomosynthesis data can be more reliably reconstructed. It is observed that the fringes or boundary enhancement introduced by the phase-contrast effects can serve as an accurate indicator of the true depth position in the tomosynthesis in-plane image. In the second part, we derived a sub-space framework to reconstruct images from few-view D-XPCT data set. By introducing a proper mask, the high frequency contents of the image can be theoretically preserved in a certain region of interest. A two-step reconstruction strategy is developed to mitigate the risk of subtle structures being oversmoothed when the commonly used total-variation regularization is employed in the conventional iterative framework. In the thirt part, we proposed a practical method to improve the quantitative accuracy of the projection-based dual-energy material decomposition. It is demonstrated that applying a total-projection-length constraint along with the dual-energy measurements can achieve a stabilized numerical solution of the decomposition problem, thus overcoming the disadvantages of the conventional approach that was extremely sensitive to noise corruption. In the final part, we described the modified filtered backprojection and iterative image reconstruction algorithms specifically developed for TBCT. Special parallelization strategies are designed to facilitate the use of GPU computing, showing demonstrated capability of producing high quality reconstructed volumetric images with a super fast computational speed. For all the investigations mentioned above, both simulation and experimental studies have been conducted to demonstrate the feasibility and effectiveness of the proposed methodologies

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

Research Status and Prospect for CT Imaging

Computed tomography (CT) is a very valuable imaging method and plays an important role in clinical diagnosis. As people pay more and more attention to radiation doses these years, decreasing CT radiation dose without affecting image quality is a hot direction for research of medical imaging in recent years. This chapter introduces the research status of low-dose technology from following aspects: low-dose scan implementation, reconstruction methods and image processing methods. Furthermore, other technologies related to the development tendency of CT, such as automatic tube current modulation technology, rapid peak kilovoltage (kVp) switching technology, dual-source CT technology and Nano-CT, are also summarized. Finally, the future research prospect are discussed and analyzed