Dismiss
InnovationQ will be updated on Sunday, Oct. 22, from 10am ET - noon. You may experience brief service interruptions during that time.
Browse Prior Art Database

METHODS FOR IDENTIFYING AND CHARACTERIZING THE DIFFERENT PHASES OF CONTRAST PASSAGE IN DYNAMIC IMAGING

IP.com Disclosure Number: IPCOM000237018D
Publication Date: 2014-May-27
Document File: 9 page(s) / 828K

Publishing Venue

The IP.com Prior Art Database

Abstract

The invention relates to dynamic imaging using both intrinsic and extrinsic contrast. The invention proposes a technique for automatically determining and labeling several transition phases of contrast passage in 4D data. The technique is based on using bolus phase histogram data and related metrics to achieve the same. The technique enables determining pre-bolus arrival, bolus pick-up and peak phase and bolus quiescent phase, post the peak / maximal phase, using normalized metrics and without explicit knowledge of data acquisition method.

This text was extracted from a Microsoft Word document.
At least one non-text object (such as an image or picture) has been suppressed.
This is the abbreviated version, containing approximately 38% of the total text.

METHODS FOR IDENTIFYING AND CHARACTERIZING THE DIFFERENT PHASES OF CONTRAST PASSAGE IN DYNAMIC IMAGING

 

BRIEF ABSTRACT

The invention relates to dynamic imaging using both intrinsic and extrinsic contrast. The invention proposes a technique for automatically determining and labeling several transition phases of contrast passage in 4D data. The technique is based on using bolus phase histogram data and related metrics to achieve the same. The technique enables determining pre-bolus arrival, bolus pick-up and peak phase and bolus quiescent phase, post the peak / maximal phase, using normalized metrics and without explicit knowledge of data acquisition method.

KEYWORDS

Contrast passage, dynamic imaging, 4D dataset, transition phases


DETAILED DESCRIPTION

4D data is generated using methods available with magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) or ultrasound imaging. This information is important for several processing steps of dynamic imaging data. For example, the processing steps include identifying baseline and peak phase for subtraction angiography based methods, determining maximal contrast phase for automated / manual/assisted detection of vascular inputs and determining fixed and moving images for image registration requirements, such as, motion/distortion correction, among others.

Another requirement includes image registration. Image registration is utilized for various requirements in dynamic imaging. For example, image registration is used to correct or freeze organ motion or correct for distortions arising due to system imperfections /acquisitions. Image registration methods typically rely on identifying a reference image ("fixed" image), to which a distorted image ("moving" image) is warped.

Conventional techniques rely on user inputs to identify various transition phases to accomplish registration of dynamic data. For example, in a conventional technique, analysis of 4D data utilizes global curve for identifying base-line and bolus transition phases. Global curve is obtained by averaging all time-curve voxels in a given 4D dataset. It is required that peak of global curve does not necessarily coincide with peak bolus phase and is not used per se. However, data analysis using global curve is primarily based on derivatives to identify change and is therefore very susceptible to noise and where global curve shows very gradual rise FIGURE 1 depicts gradual rise in global curve.

Further, AlF detection is accomplished by using a base-line phase and a "peak phase". The base-line phase and the peak phase are identified using max-slope point of the global curve. However, the technique is not completely optimal in case global curve is noisy or when global curve demonstrates gradual rise. However, the conventional technique allows easy identification of "peak-point" using a standard normalized threshold value and is applicable even when the global curve is noisy or has a gradual rise.

 For i...