Simulation of a Virtual Iron-Overload Model and R2* estimation using Multispectral Fat-Water Models for GRE and UTE Acquisitions using MRI

Iron overload is an excessive accumulation of iron in the body and can be either inherited or acquired through chronic blood transfusions. Assessment of hepatic iron concentration (HIC) is important in the management and monitoring of iron overload. Despite liver biopsy being the gold standard method for assessing HIC, it is invasive, painful, unsuitable for repeated measurements, and carries the risk of bleeding and infection. Magnetic Resonance Imaging (MRI) methods based on transverse relaxation rate (R2*) have emerged as a non-invasive alternative to liver biopsy for assessing HIC. Multispectral fat-water-R2* modeling techniques, such as the non-linear square (NLSQ) fitting and autoregressive moving average (ARMA) models, have been proposed to provide more accurate assessments of iron overload by accounting for the presence of fat, which can otherwise confound R2*-based HIC measurements in conditions of co-existing iron overload and steatosis. However, the R2* estimation by these multispectral models has not been systematically investigated for various acquisition methods like the multiecho gradient echo (GRE) and ultrashort echo time (UTE) across the full clinically relevant range of HICs. To address this challenge, a Monte Carlo-based iron overload model based on true iron morphometry and histological data was constructed, and MRI signals were synthesized at 1.5 T and 3 T field strengths. This study compared the accuracy and precision of multispectral NLSQ and ARMA models against the monoexponential model and published in vivo R2*-HIC calibrations in estimating R2*. The results showed that, for GRE acquisitions, ARMA and NLSQ models produced higher slopes compared to the monoexponential model and published in vivo R2*-HIC calibrations. However, for UTE acquisitions for shorter echo spacing (≤ 0.5 ms) and longer maximum echo time, TEmax (≥ 6 ms), both multispectral and monoexponential signal models produced similar R2*-HIC slopes and precision values across the full clinical spectrum of HICs at both 1.5 T and 3 T. The results from the simulation studies were validated using phantoms and patient data. Future work should investigate the performance of multispectral models by simulating liver models in coexisting conditions of iron overload and steatosis to investigate simultaneous and accurate quantification of both R2* and fat

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

Sugar beet

Sugar beet has entered the age of liberalism with the abolition of production quotas in Europe. It finds itself on the world market and on an equal footing with sugar cane. France has benefited from the “AKER - Sugar beet 2020, a competitive innovation” Investments for the Future Programme, which aims to double the annual growth rate of the sugar yield per hectare of beet. It has made a scientific breakthrough by researching all of the genetic diversity available worldwide, and by carrying out genotyping before phenotyping. It is developing new genetic material, available for introduction into future sugar beet varieties. It also offers innovative tools and methods in the fields of genotyping and phenotyping, supporting players in the sector - beet growers and sugar manufacturers - in their imperative improvement in competitiveness. This book is mainly intended for scientists and professionals, and all those interested in research, development and training in the plant sector. It has just completed eight years of multidisciplinary work bringing together a hundred scientists. The AKER programme puts for a long time sugar beet in the top tier of cultivated species and helps to provide the consumer with quality sugar produced locally and under environmentally friendly conditions