Automatic Alignment of pre and intraoperative Data using Anatomical Landmarks for Augmented Laparoscopic Liver Surgery

International audienceEach year in Europe 50,000 new liver cancer cases are diagnosed for which hepatic surgery combined to chemotherapy is the most common treatment. In particular the number of laparoscopic liver surgeries has increased significantly over the past years. This type of minimally invasive procedure which presents many benefits for the patient is challenging for the surgeons due to the limited field of view. Recently new augmented reality techniques which merge preoperative data and intraoperative images and permit to visualize internal structures have been proposed to help surgeons during this type of surgery. One of the difficulties is to align preoperative data with the intraoperative images. We propose in this paper a semi-automatic approach for solving the ill-posed problem of initial alignment for Augmented Reality systems during liver surgery. Our registration method relies on anatomical landmarks extracted from both the laparoscopic images and three-dimensional model, using an image-based soft-tissue reconstruction technique and an atlas-based approach, respectively. The registration evolves automatically from a quasi-rigid to a non-rigid registration. Furthermore, the surface-driven deformation is induced in the volume via a patient specific biomechanical model. The experiments conducted on both synthetic and in vivo data show promising results with a registration error of 2 mm when dealing with a visible surface of 30% of the whole liver

Aortic valve stenosis-multimodality assessment with PET/CT and PET/MRI

Aortic valve disease is the most common form of heart valve disease in developed countries and a growing healthcare burden with an ageing population. Transthoracic and transoesophageal echocardiography remains central to the diagnosis and surveillance of patients with aortic stenosis, providing gold standard assessments of valve haemodynamics and myocardial performance. However, other multimodality imaging techniques are being explored for the assessment of aortic stenosis, including combined PET/CT and PET/MR. Both approaches provide unique information with respect to disease activity in the valve alongside more conventional anatomic assessments of the valve and myocardium in this condition. This review investigates the emerging use of PET/CT and PET/MR to assess patients with aortic stenosis, examining how the complementary data provided by each modality may be used for research applications and potentially in future clinical practice

Prediction of infarct localization from myocardial deformation

International audienceWe propose a novel framework to predict the location of a myocardial infarct from local wall deformation data. Non-linear dimensionality reduction is used to estimate the Euclidean space of coordinates encoding deformation patterns. The infarct location of a new subject is inferred by two consecutive interpolations, formulated as multiscale kernel regressions. They consist in (i) finding the low-dimensional coordinates associated to the measured deformation pattern, and (ii) estimating the possible infarct location associated to these coordinates. These concepts were tested on a database of 500 synthetic cases generated from a realistic electromechanical model of the two ventricles. The database consisted of infarcts of random extent, shape, and location overlapping the whole left-anterior-descending coronary territory. We demonstrate that our method is accurate and significantly overcomes the limitations of the clinically-used thresholding of the deformation patterns (average area under the ROC curve of 0.992±0.011 vs. 0.812±0.124, p<0.001)

Fast porous visco-hyperelastic soft tissue model for surgery simulation: Application to liver surgery

International audienceUnderstanding and modeling liver biomechanics represents a significant challenge due to its complex nature. In this paper, we tackle this issue in the context of real time surgery simulation where a compromise between biomechanical accuracy and computational efficiency must be found. We describe a realistic liver model including hyperelasticity, porosity and viscosity that is implemented within an implicit time integration scheme. To optimize its computation, we introduce the Multiplicative Jacobian Energy Decomposition (MJED) method for discretizing hyperelastic materials on linear tetrahedral meshes which leads to faster matrix assembly than the standard Finite Element Method. Viscohyperelasticity is modeled by Prony series while the mechanical eff ect of liver perfusion is represented with a linear Darcy law. Dynamic mechanical analysis has been performed on 60 porcine liver samples in order to identify some visco-elastic parameters. Finally, we show that liver deformation can be simulated in real-time on a coarse mesh and study the relative eff ects of the hyperelastic, viscous and porous components on the liver biomechanics

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

International audienceIn computer-aided interventions, biomechanical models reconstructed from the pre-operative data are used via augmented reality to facilitate the intra-operative navigation. The predictive power of such models highly depends on the knowledge of boundary conditions. However , in the context of patient-specific modeling, neither the pre-operative nor the intra-operative modalities provide a reliable information about the location and mechanical properties of the organ attachments. We present a novel image-driven method for fast identification of boundary conditions which are modelled as stochastic parameters. The method employs the reduced-order unscented Kalman filter to transform in real-time the probability distributions of the parameters, given observations extracted from intra-operative images. The method is evaluated using synthetic, phantom and real data acquired in vivo on a porcine liver. A quantitative assessment is presented and it is shown that the method significantly increases the predictive power of the biomechanical model

Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view

International audienceThe short-axis view defined such that a series of slices are perpendicular to the long-axis of the left ventricle (LV) is one of the most important views in cardiovascular imaging. Raw trans-axial Computed Tomography (CT) images must be often reformatted prior to diagnostic interpretation in short-axis view. The clinical importance of this refor-matting requires the process to be accurate and reproducible. It is often performed after manual localization of landmarks on the image (e.g. LV apex, centre of the mitral valve, etc.) being slower and not fully reproducible as compared to automatic approaches. We propose a fast, automatic and reproducible method to reformat CT images from original trans-axial orientation to short-axis view. A deep learning based seg-mentation method is used to automatically segment the LV endocardium and wall, and the right ventricle epicardium. Surface meshes are then obtained from the corresponding masks and used to automatically detect the shape features needed to find the transformation that locates the cardiac chambers on their standard, mathematically defined, short-axis position. 25 datasets with available manual reformatting performed by experienced cardiac radiologists are used to show that our reformatted images are of equivalent quality

Hybrid PET/CT and PET/MRI imaging of vulnerable coronary plaque and myocardial scar tissue in acute myocardial infarction

BACKGROUND: Following an acute coronary syndrome, combined CT and PET with 18F-NaF can identify coronary atherosclerotic plaques that have ruptured or eroded. However, the processes behind 18F-NaF uptake in vulnerable plaques remain unclear. METHODS AND RESULTS: Ten patients with STEMI were scanned after 18F-NaF injection, for 75 minutes in a Siemens PET/MR scanner using delayed enhancement (LGE). They were then scanned in a Siemens PET/CT scanner for 10 minutes. Tissue-to-background ratio (TBR) was compared between the culprit lesion in the IRA and remote non-culprit lesions in an effort to independently validate prior studies. Additionally, we performed a proof-of-principle study comparing TBR in scar tissue and remote myocardium using LGE images and PET/MR or PET/CT data. From the 33 coronary lesions detected on PET/CT, TBRs for culprit lesions were higher than for non-culprit lesions (TBR = 2.11 ± 0.45 vs 1.46 ± 0.48; P < 0.001). Interestingly, the TBR measured on the PET/CT was higher for infarcted myocardium than for remote myocardium (TBR = 0.81 ± 0.10 vs 0.71 ± 0.05; P = 0.003). These results were confirmed using the PET/MR data (TBR = 0.81 ± 0.10 for scar, TBR = 0.71 ± 0.06 for healthy myocardium, P = 0.03). CONCLUSIONS: We confirmed the potential of 18F-NaF PET/CT imaging to detect vulnerable coronary lesions. Moreover, we demonstrated proof-of-principle that 18F-NaF concurrently detects myocardial scar tissue

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

The clinical utility of hybrid imaging for the identification of vulnerable plaque and vulnerable patients

Despite decades of research and major innovations in technology, cardiovascular disease remains the leading cause of death globally. Our understanding of major cardiovascular events and their prevention is centred around the atherosclerotic plaque and the processes that ultimately lead to acute plaque rupture. Recent advances in hybrid imaging technology allow the combination of high spatial resolution and anatomical detail with molecular assessments of disease activity. This provides the ability to identify vulnerable plaque characteristics and differentiate active and quiescent disease, with the potential to improve patient risk stratification. Combined positron emission tomography and computed tomography is the prototypical non-invasive hybrid imaging technique for coronary artery plaque assessment. In this review we discuss the current state of play in the field of hybrid coronary atherosclerosis imaging