99,180 research outputs found
Weighted Diffeomorphic Density Matching with Applications to Thoracic Image Registration
In this article we study the problem of thoracic image registration, in
particular the estimation of complex anatomical deformations associated with
the breathing cycle. Using the intimate link between the Riemannian geometry of
the space of diffeomorphisms and the space of densities, we develop an image
registration framework that incorporates both the fundamental law of
conservation of mass as well as spatially varying tissue compressibility
properties. By exploiting the geometrical structure, the resulting algorithm is
computationally efficient, yet widely general.Comment: Accepted in Proceedings of the 5th MICCAI workshop on Mathematical
Foundations of Computational Anatomy, Munich, Germany, 2015
(http://www-sop.inria.fr/asclepios/events/MFCA15/
Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm.
The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping
Diffeomorphic density registration
In this book chapter we study the Riemannian Geometry of the density
registration problem: Given two densities (not necessarily probability
densities) defined on a smooth finite dimensional manifold find a
diffeomorphism which transforms one to the other. This problem is motivated by
the medical imaging application of tracking organ motion due to respiration in
Thoracic CT imaging where the fundamental physical property of conservation of
mass naturally leads to modeling CT attenuation as a density. We will study the
intimate link between the Riemannian metrics on the space of diffeomorphisms
and those on the space of densities. We finally develop novel computationally
efficient algorithms and demonstrate there applicability for registering RCCT
thoracic imaging.Comment: 23 pages, 6 Figures, Chapter for a Book on Medical Image Analysi
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients
In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes
Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review
Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519
A comparative study of breast surface reconstruction for aesthetic outcome assessment
Breast cancer is the most prevalent cancer type in women, and while its
survival rate is generally high the aesthetic outcome is an increasingly
important factor when evaluating different treatment alternatives. 3D scanning
and reconstruction techniques offer a flexible tool for building detailed and
accurate 3D breast models that can be used both pre-operatively for surgical
planning and post-operatively for aesthetic evaluation. This paper aims at
comparing the accuracy of low-cost 3D scanning technologies with the
significantly more expensive state-of-the-art 3D commercial scanners in the
context of breast 3D reconstruction. We present results from 28 synthetic and
clinical RGBD sequences, including 12 unique patients and an anthropomorphic
phantom demonstrating the applicability of low-cost RGBD sensors to real
clinical cases. Body deformation and homogeneous skin texture pose challenges
to the studied reconstruction systems. Although these should be addressed
appropriately if higher model quality is warranted, we observe that low-cost
sensors are able to obtain valuable reconstructions comparable to the
state-of-the-art within an error margin of 3 mm.Comment: This paper has been accepted to MICCAI201
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