A Computational Framework for Ultrastructural Mapping of Neural Circuitry

Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists

Improving workflows of neuro-interventional procedures with autostereoscopic 3D visualization of multi-modality imaging in hybrid interventional suites

Recent developments in interventional neuroradiology techniques, medical imaging modalities, endovascular stenting and embolization materials lead to an increasing number of patients with cerebral aneurysms and arteriovenous malformations that are eligible for endovascular treatment and have opened new perspectives for novel ways for patient treatment in general. In this paper, we describe a software tool for 3D image fusion of multi-modal acquisitions to assist endovascular treatment of cerebral malformations. The software and an autostereoscopic 3D display were implemented and tested in clinical applications in a hybrid interventional suite that is used for radio-interventional as well as neurosurgical procedures. Our hypothesis is that fusion of image data acquired prior to intervention procedures with images acquired during those procedures should allow better visualizing and navigating through complex cerebral vasculature. This should also improve workflows of neuro-interventional procedures

CMRSegTools: an Osirix plugin for myocardial infarct sizing on DE-CMR images

International audienc

Console-integrated stereoscopic OsiriX 3D volume-rendered images for da Vinci colorectal robotic surgery

The increased distance between surgeon and surgical field is a significant problem in laparoscopic surgery. Robotic surgery, although providing advantages for the operator, increases this gap by completely removing force feedback. Enhancement with visual tools can therefore be beneficial. The goal of this preliminary work was to create a custom plugin for OsiriX to display volume-rendered images in the da Vinci surgeon's console. The TilePro multi-input display made the generated stereoscopic pairs appear to have depth. Tumor position, vascular supply, spatial location, and relationship between organs appear directly within the surgeon's field of view. This study presents a case of totally robotic right colectomy for cancer using this new technology. Sight diversion was no longer necessary. Depth perception was subjectively perceived as profitable. Total immersion in the operative field helped compensate for the lack of tactile feedback specific to robotic intervention. This innovative tool is a step forward toward augmented-reality robot-assisted surgery

Stereoscopic augmented reality for da Vincii™ robotic biliary surgery

New laparoscopic techniques put distance between the surgeon and his patient

A multicentric IT platform for storage and sharing of imaging-based radiation dosimetric data

International audiencePurpose The MEDIRAD project is about the effects of low radiation dose in the context of medical procedures. The goal of the work is to develop an informatics service that will provide the researchers of the MEDIRAD project with a platform to share acquired images, along with the associated dosimetric data pertaining to the radiation resulting from the procedure. Methods The authors designed a system architecture to manage image data and dosimetric data in an integrated way. DICOM and non-DICOM data are stored in separated repositories, and the link between the two is provided through a semantic database, i.e., a database whose information schema in aligned with an ontology. Results The system currently supports CT, PET, SPECT, and NM images as well as dose reports. Currently, two workflows for non-DICOM data generated from dosimetric calculations have been taken into account, one concerning Monte Carlo-based calculation of organ doses in Chest CT, and the other estimation of doses in nontarget organs in I-131 targeted radionuclide therapy of the thyroid. Conclusion The system is currently deployed, thus providing access to image and related dosimetric data to all MEDIRAD users. The software was designed in such a way that it can be reused to support similar needs in other projects

Augmented reality to the rescue of the minimally invasive surgeon. The usefulness of the interposition of stereoscopic images in the Da Vinci™ robotic console

Computerized management of medical information and 3D imaging has become the norm in everyday medical practice. Surgeons exploit these emerging technologies and bring information previously confined to the radiology rooms into the operating theatre. The paper reports the authors' experience with integrated stereoscopic 3D-rendered images in the da Vinci surgeon console