다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

A survey on deep learning in medical image analysis

Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Virtually all large systems which evaluate images require the registration of images, or a closely related operation, as an intermediate step. Specific examples of systems where image registration is a significant component include matching a target with a real-time image of a scene for target recognition, monitoring global land usage using satellite images, matching stereo images to recover shape for autonomous navigation, and aligning images from different medical modalities for diagnosis.Over the years, a broad range of techniques has been developed for various types of data and problems. These techniques have been independently studied for several different applications, resulting in a large body of research. This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied. Three major types of variations are distinguished. The first type are the variations due to the differences in acquisition which cause the images to be misaligned. To register images, a spatial transformation is found which will remove these variations. The class of transformations which must be searched to find the optimal transformation is determined by knowledge about the variations of this type. The transformation class in turn influences the general technique that should be taken. The second type of variations are those which are also due to differences in acquisition, but cannot be modeled easily such as lighting and atmospheric conditions. This type usually effects intensity values, but they may also be spatial, such as perspective distortions. The third type of variations are differences in the images that are of interest such as object movements, growths, or other scene changes. Variations of the second and third type are not directly removed by registration, but they make registration more difficult since an exact match is no longer possible. In particular, it is critical that variations of the third type are not removed. Knowledge about the characteristics of each type of variation effect the choice of feature space, similarity measure, search space, and search strategy which will make up the final technique. All registration techniques can be viewed as different combinations of these choices. This framework is useful for understanding the merits and relationships between the wide variety of existing techniques and for assisting in the selection of the most suitable technique for a specific problem.

A survey of image registration techniques

https://www.semthorcardiovascsurg.com/article/S1043-0679(04)00060-7/pdf

Advanced imaging including PET/CT for cardiothoracic surgery.

Women with breast cancer, whether screen detected or symptomatic, have both mammography and ultrasound for initial imaging assessment Unlike X-ray or magnetic resonance, which produce an image of the whole breast, ultrasound provides comparatively limited 2D or 3D views located around the lesions Combining different modalities is an essential task for accurate diagnosis and simulating ultrasound images based on whole breast data could be a way toward correlating different information about the same lesion Very few studies have dealt with such a simulation framework since the breast undergoes large scale deformation between the prone position of magnetic resonance imaging and the largely supine or lateral position of ultrasound We present a framework for the realistic simulation of 3D ultrasound images based on prone magnetic resonance images from which a supine position is generated using a biomechanical model The simulation parameters are derived from a real clinical infrastructure and from transducers that are used for routine scans, leading to highly realistic ultrasound images of any region of the breast

3D ultrasound simulation based on a biomechanical model of prone MRI in breast cancer imaging

Following recent developments, most brain imaging modalities (MR, CT, SPECT, PET) can nowadays be registered and integrated in a manner almost simple enough for routine use. By design though, these modalities are still not able to match the principles and near real-time capabilities of the much simpler (but of lower spatial resolution) EEG, thus the need to integrate it as well, along with - for some patients - the more accurate invasive electrophysiology measurements taken directly in contact with brain structures. A standard control CT (or MR) is routinely performed after the implantation of invasive electrodes. After registration with the other modalities, the initial estimates of the electrodes' locations extracted from the CT (or MR) are iteratively improved by using a geometrical model of the electrodes' arrangement (grids, strips, etc.) And other optional constraints (morphology, etc.). Unlike the direct 3D pointing of each electrode in the surgical suite - which can still act as a complementary approach - this technique estimates the most likely location of the electrodes during monitoring and can also deal with non cortical arrangements (internal strips, depth electrodes, etc.). Although not always applicable to normal volunteers because of its invasive components, this integration further opens the door towards an improved understanding of a very complex biological system.

Model-based multiconstrained integration of invasive electrophysiology with other modalities

The Internet of Things (IoT) is beginning to have a pronounced impact on the digitalised food manufacturing chain. Whilst the food chain is becoming at once more complex and more critical, the application of IoT has begun to be recognised as a key component of this transformation. However, exactly what the IoT is, or is not, is both evolving and provoking a lively debate. New clarifiers are being added to the term such as the Internet of Industrial Things and the Autonomous Internet of Things to ostensibly help clarify the terminology. For the purposes of the Internet of Food Things Network Plus, our conceptual and physical platform is predicated on the idea of a secure and trustworthy network based on open standards, where-possible commodity devices, and underpinned by societally ratified regulatory arrangements.

The Internet of food things: Adding value to the digitalisation of the UK food supply chain

This chapter is intended to provide an insight into what is currently available to the medical imaging community for visualizing and processing digital images acquired with spiral CT. While even more sophisticated functions may also become available in the near future, most of these are still seldom implemented in current clinical practice.

Luc Bidaut

Papers

Advanced imaging including PET/CT for cardiothoracic surgery.

3D ultrasound simulation based on a biomechanical model of prone MRI in breast cancer imaging

Model-based multiconstrained integration of invasive electrophysiology with other modalities

The Internet of food things: Adding value to the digitalisation of the UK food supply chain

Data/Image Processing