Abstract: Stent marker detection is automatically performed. Stent markers in fluoroscopic images or other markers in other types of imaging are detected using a machine-learnt classifier. Hierarchal classification may be used, such as detecting individual markers with one classifier and then detecting groups of markers (e.g., a pair) with a joint classifier. The detection may be performed in a single image and without user indication of a location.

Abstract: Real-time marker detection in medical imaging of a stent may be provided. A plurality of frames of image data may be obtained. A plurality of candidate markers for the stent may be determined in the plurality of frames of image data. One or more markers from the plurality of candidate markers may be detected. The detecting may be based on automatic initialization using a subset of frames of image data from the plurality of frames of image data. The detecting may be performed in real-time with the obtaining.

Abstract: A method and system for real time stent enhancement on a live 2D fluoroscopic scene is disclosed. A motion compensated stent enhancement image is generated from a first set of frames in a fluoroscopic image sequence. A weighting field is generated based on the motion compensated stent enhancement image. For each new frame in the fluoroscopic image sequence that is received, the stent is enhanced in the new frame by compounding the new frame with the motion compensated stent enhancement image using the weighting field.

Abstract: Real-time marker detection in medical imaging of a stent may be provided. A plurality of frames of image data may be obtained. A plurality of candidate markers for the stent may be determined in the plurality of frames of image data. One or more markers from the plurality of candidate markers may be detected. The detecting may be based on automatic initialization using a subset of frames of image data from the plurality of frames of image data. The detecting may be performed in real-time with the obtaining.

Abstract: A method and system for real time stent enhancement on a live 2D fluoroscopic scene is disclosed. A motion compensated stent enhancement image is generated from a first set of frames in a fluoroscopic image sequence. A weighting field is generated based on the motion compensated stent enhancement image. For each new frame in the fluoroscopic image sequence that is received, the stent is enhanced in the new frame by compounding the new frame with the motion compensated stent enhancement image using the weighting field.

Abstract: A method for enhancing stent visibility in digital medical images includes providing a time series of 2-dimensional (2D) images of a stent in a vessel, estimating motion of the stent in a subset of images of the time series of images, estimating motion of clutter in the subset of images, where clutter comprises anatomical structures other than the stent, estimating a clutter layer in the subset of images from the estimated clutter motion, estimating a stent layer in the subset of images from the clutter layer and the estimated clutter motion, and minimizing a functional of the estimated stent motion, the estimated stent layer, the estimated clutter motion, and the estimated clutter layer to in calculate a refined stent layer image, where the refined stent layer image has enhanced visibility of the stent.

Abstract: A method and system for tracking a guidewire in a fluoroscopic image sequence is disclosed. In order to track a guidewire in a fluoroscopic image sequence, guidewire segments are detected in each frame of the fluoroscopic image sequence. The guidewire in each frame of the fluoroscopic image sequence is then detected by rigidly tracking the guidewire from a previous frame of the fluoroscopic image sequence based on the detected guidewire segments in the current frame. The guidewire is then non-rigidly deformed in each frame based on the guidewire position in the previous frame.

Abstract: A virtual map of vessels of interest in medical procedures, such as coronary angioplasty is created so that doses of contrasting agent given to a patient may be reduced. A position of a coronary guidewire is determined and locations of vessel boundaries are found. When the contrast agent has dissipated, virtual maps of the vessels are created as new images. The locations of the determined vessel boundaries are imported to a mapping system and an image obtained without using a contrast agent is modified based on the imported locations of vessel boundaries. This creates a virtual map of the vessels.

Abstract: A method for temporally filtering medical images during a fluoroscopy guided intervention procedure includes providing a mask image, a fluoroscopy intervention image acquired at a current time during a medical intervention procedure, forming a subtraction image by subtracting the mask image from the intervention image, calculating a motion image of a moving structure in the subtraction image, forming a residual image by subtracting the motion image from the subtraction image, temporally filtering the residual image with a filtered image from a previous time, and adding the motion image to the temporally filtered residual image.

Abstract: Stent viewing is provided in medical imaging. Stent images are provided with minimal or no user input of spatial locations. Images showing contrast agent are distinguished from other images in a sequence. After aligning non-contrast images, the images are compounded to enhance the stent. The contrast agent images are used to identify the vessel. A contrast agent image is aligned with the enhanced stent or other image to determine the relative vessel location. An indication of the vessel wall may be displayed in an image also showing the stent. A preview images may be output. A guide wire may be used to detect the center line for vessel identification. Various detections are performed using a machine-trained classifier or classifiers.

Abstract: A method and system for intelligent digital subtraction is disclosed. The method and system for intelligent digital subtraction can be used in a roadmap application for a coronary intervention. A mask image is obtained with vessels highlighted by contrast media. A guide wire is inserted into the vessels, and a guide wire image is obtained. A direct subtraction image is generated from the guide wire image and the mask image. A reduced noise subtraction image is generated based on mutual image information between the subtraction image and the guide wire image and mutual image information between the subtraction image and the mask image.

Abstract: The invention relates to a method for presentation of medical images by a reproduction facility of a diagnostic device with suppression of the noise with the following steps: one-off calibration of the signal-dependent noise; separation of the signal and noise components in the image; adaptation of the two components according to set parameters; and composition of the signals.

Abstract: A method and system for vessel segmentation in fluoroscopic images is disclosed. Hierarchical learning-based detection is used to perform the vessel segmentation. A boundary classifier is trained and used to detect boundary pixels of a vessel in a fluoroscopic image. A cross-segment classifier is trained and used to detect cross-segments connecting the boundary pixels. A quadrilateral classifier is trained and used to detect quadrilaterals connecting the cross segments. Dynamic programming is then used to combine the quadrilaterals to generate a tubular structure representing the vessel.

Abstract: A method and system for image quality assessment is disclosed. The image quality assessment method is a no-reference method for objectively assessing the quality of medical images. This method is guided by the human vision model in order to accurately reflect human perception. A region of interest (ROI) of medical image is divided into non-overlapping blocks of equal size. Each of the blocks is categorized as a smooth block, a texture block, or an edge block. A perceptual sharpness measure, which is weighted by local contrast, is calculated for each of the edge blocks. A perceptual noise level measure, which is weighted by background luminance, is calculated for each of the smooth blocks. A sharpness quality index is determined based on the perceptual sharpness measures of all of the edge blocks, and a noise level quality index is determined based on the perceptual noise level measures of all of the smooth blocks.

Abstract: A method and system for correcting butting artifacts in x-ray images is disclosed. In order to correct a butting artifact in an x-ray image, a butting artifact region in the x-ray image is normalized. Multiple intensity shift estimators are calculated for each pixel of each line of the butting artifact. Confidence intervals are calculated for each intensity shift estimator. A multiple hypothesis hidden Markov model (MH-HMM) is formulated based on the intensity shift operators and confidence measures subject to a smoothness constraint, and the MH-HMM is solved to determine intensity shift values for each pixel. A corrected image is generated by adjusting the intensity of each pixel of the butting artifact based on the intensity shift value for that pixel.

Abstract: A method for online optimization of guidewire visibility in fluoroscopic images includes providing an digitized image acquired from a fluoroscopic imaging system, the image comprising an array of intensities corresponding to a 2-dimensional grid of pixels, detecting a guidewire in the fluoroscopic image, enhancing the visibility of the guidewire in the fluoroscopic image, calculating a visibility measure of the guidewire in the fluoroscopic image, and readjusting acquisition parameters of the fluoroscopic imaging system wherein the guidewire visibility is improved.

Abstract: A method for downsampling fluoroscopic images and enhancing guidewire visibility during coronary angioplasty includes providing a first digitized image, filtering the image with one or more steerable filters of different angular orientations, assigning a weight W and orientation O for each pixel based on the filter response for each pixel, wherein each pixel weight is assigned to a function of a maximum filter response magnitude and the pixel orientation is calculated from the angle producing the maximum filter response if the magnitude is greater than zero, wherein guidewire pixels have a higher weight than non-guidewire pixels, and downsampling the orientation and weights to calculate a second image of half the resolution of the first image, wherein the downsampling accounts for the orientation and higher weight assigned to the guidewire pixels.

Abstract: Stent marker detection is automatically performed. Stent markers in fluoroscopic images or other markers in other types of imaging are detected using a machine-learnt classifier. Hierarchal classification may be used, such as detecting individual markers with one classifier and then detecting groups of markers (e.g., a pair) with a joint classifier. The detection may be performed in a single image and without user indication of a location.

Abstract: Stent viewing is provided in medical imaging. Stent images are provided with minimal or no user input of spatial locations. Images showing contrast agent are distinguished from other images in a sequence. After aligning non-contrast images, the images are compounded to enhance the stent. The contrast agent images are used to identify the vessel. A contrast agent image is aligned with the enhanced stent or other image to determine the relative vessel location. An indication of the vessel wall may be displayed in an image also showing the stent. A preview images may be output. A guide wire may be used to detect the center line for vessel identification. Various detections are performed using a machine-trained classifier or classifiers.

Abstract: A method for enhancing stent visibility in digital medical images includes providing a time series of 2-dimensional (2D) images of a stent in a vessel, estimating motion of the stent in a subset of images of the time series of images, estimating motion of clutter in the subset of images, where clutter comprises anatomical structures other than the stent, estimating a clutter layer in the subset of images from the estimated clutter motion, estimating a stent layer in the subset of images from the clutter layer and the estimated clutter motion, and minimizing a functional of the estimated stent motion, the estimated stent layer, the estimated clutter motion, and the estimated clutter layer to in calculate a refined stent layer image, where the refined stent layer image has enhanced visibility of the stent.