Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/60—Analysis of geometric attributes
  • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume

Definitions

• a database representing temporal image slices of the heart is first generated.
• the database may be described as a temporal sequence of slices of the heart, or a stack of slices at temporal intervals, covering at least one whole cycle of the heart.
• An attempt is then made to delineate the cardiac contours in each heart phase from the stack of slices.
• Current methods of accomplishing this task are manual or semi-manual using visualization software. For example, a clinician marks boundary points of a heart chamber in each slice of the stack of slices at each temporal interval.
• the shape (volume) of the heart chamber is determined in each of the temporal intervals, at least for the temporal intervals corresponding to the cardiac phases of interest. From the estimated shapes, functional parameters such as end-diastolic and end-systolic left ventricular volumes, stroke volumes and ejection fraction are computed. With a typical sample of 200 slices, manually delineating the cardiac chambers is time-consuming and error prone. Methods of accomplishing the above-described procedure automatically are currently being tried in the research art. One automated procedure is to fit to the image slices a mathematical model of the heart. This model is based on some prior knowledge on the shape of the myocardium, and possibly a statistical description of the typical shape variations from patient to patient.
• the automatic segmentation technique generally consists in optimizing an energy functional made of two terms: the first one describes the fit of the model to the data, while the second one penalizes too strong deformations with respect to some reference configuration.
• the result of this procedure is one optimal segmentation with respect to the mathematical model used to represent the myocardium shape and the mathematical form of the energy used for the optimization.
• MRI scans are noisy, contain artifacts and only a few slices are acquired, with possible gaps between the slices.
• the image information does not permit delineating the cardiac chambers without ambiguity. Due to these problems and the like, the segmentation problem is characterized by an inherent uncertainty. This aspect is well known in clinical practice, where it is commonly accepted that the marked boundary points vary from clinician to clinician in manual techniques.
• a method of stochastic model-based segmentation of diagnostic images of a subject, from samples of the subject is provided.
• the samples are generated according to a Bayesian stochastic model describing the conditional probability distribution of the organ shape given the images and functional parameters are derived for each of the respective samples. An uncertainty value is estimated for each derived parameter .
• a diagnostic imaging apparatus is provided.
• the apparatus includes a means for generating the diagnostic images and a processor programmed to perform the above-described method.
• an apparatus for generating stochastic model-based segmentation of diagnostic images of a subject is provided.
• the apparatus includes a diagnostic imaging scanner configured to perform a scan of a volume region of interest of a subject.
• a processing system is provided to process scanned data of the volume region of interest from the diagnostic imaging scanner and a provided reconstruction module receives the scanning data and generate a stack of slice images of the region of interest stored in the processing system.
• a shape module computes shape samples representing multiple solutions of the segmentation of the slice images according to a probability distribution described by a Bayesian model, and a function module derives functional parameters for each of the respective samples and estimates a probability value for each derived parameter.
• a display module displays the functional parameters and respective probability values on a provided display device.
• a diagnostic imaging apparatus performs stochastic model-based segmentation of diagnostic images of a subject.
• a computing means computes a plurality of shape samples according to a probability distribution described by a Bayesian model.
• a function means derives at least one functional parameter for each of the respective samples and estimates a probability value for each derived parameter.
• One advantage resides in an improved robustness of the segmentation and the tracking of the heart chambers.
• Another advantage resides in the precise information provided to the clinician regarding the accuracy of the functional parameters generated by the automated process.
• Yet another advantage resides in the means for an improved diagnosis of cardiac function, with an improved level of confidence.
• FIGURE 1 diagrammatically shows a magnetic resonance imaging system employing automated estimating of cardiac functional parameters according to concepts of the present invention.
• a diagnostic imaging scanner 10 such as a magnetic resonance scanner, includes a housing 12 defining a generally cylindrical scanner bore 14 inside of which an associated imaging subject 16 is disposed. Details of the diagnostic imaging scanner are not shown because magnetic resonance, CT, SPECT, PET, and other suitable scanners are well known in the art. It suffices to say that the diagnostic imaging scanner 10 performs a cardiac scan and communicates scan data to a reconstruction module 18 resident in a processing system 20.
• the processing system 20 presented herein is not inherently related to any particular computer or other apparatus. In particular, various general-purpose machines may be used with program modules in accordance with the teachings herein, or it may prove more convenient to construct more specialized apparatus to perform the required method steps.
• the processing system 20 may be a single system or an interconnected distributed system of processors.
• the reconstruction module 18 generates a series of volume images at temporal intervals during the cardiac cycle.
• Each of the temporally offset volume images includes a stack of slice images 22. Due to possible poor contrast between the cardiac tissues and the surroundings of the myocardium, image noise, discrete pixel sizes, and the like, the boundary of the cardiac chamber is not without ambiguity.
• a shape module or means 24 computes multiple solutions of the heart shape rather than the single optimized solution of prior-art methods.
• the Bayesian probability distribution can be represented by a finite number of samples, for example 500.
• the multiple samples are stored in a myocardium shape sample memory 26.
• These multiple solutions can be displayed to the clinician for example in the form of an animated shape, possibly superposed on the image slices, so that a qualitative representation of the set of solutions is given to the clinician.
• a function module 28 then accesses the shape samples 26 and computes cardiac functional parameters and statistical parameters 30 from the multiple shapes 26, for example ejection fraction, end-diastolic volume, end-systolic volume, stroke volume, wall thickness, and the like.
• the exemplary functional parameter is the ejection fraction 32.
• the present invention computes a plurality of ejection fractions from the plurality of shapes in the shape memory 26 and statistical parameters from this plurality of the ejection fractions.
• a histogram is computed, having a relative frequency or probability 34 for each of the computed ejection fractions 32.
• the functional parameters and statistical parameters 30 are then presented via a display module 36 on a display device 38 for analysis by a clinician or other user of the diagnostic imaging system 10.
• the display can be in graphical form as a histogram, numerical form as a median value and standard deviation, or the like. In this manner, a clinician is presented along with the solutions, a measure of the degree of confidence in the solutions.
• an animation module 39 displays the plurality of solutions in the aforementioned form of an animated shape, possibly superposed on the images, so that a qualitative representation of the set of solutions is given to the clinician.
• the animation sequence may be ordered according to a predetermined criteria such as, for example, ordered from low probability to high probability, most far from a reference shape to the most close to the reference shape, etc.
• the animation may also be superposed on the most probable shape.
• One approach that the shape module 24 uses in computing the shape samples 26 involves the use of Markov chain algorithms as described by W.R. Gilks, S. Richardson and D.J. Spiegelhalter in Markov Chain Monte Carlo in Practice, Chapman and Hall, 1966. To deal with the temporal component of the problem, sequential approaches as described by M. Isard and A. Blake in Condensation — conditional density propagation for visual tracking, International Journal of Computer Vision, 1998.
• the general approach is to construct a Bayesian probability distribution describing the space of solutions for the segmentation problem, and to generate samples of this probability distribution.
• the prior model describes the prior knowledge about the shape; it is a probability distribution on the parameters used to describe the shape (for example the coordinates of the nodes of a mesh representing the organ shape, or the coefficients of the decomposition of the surface based on surface harmonics).
• the likelihood model describes the structure of the images (for example the statistical distribution of the grey values) for a fixed shape.
• Monte Carlo integration allows one to compute this integral straightforwardly by generating a sample ..., z n ) of the distribution ⁇ (z/y) and to use the approximation:
• Computing statistics on the functional parameters requires, therefore, generating a finite number of samples of the Bayesian probability distribution ⁇ (z/y). This can be done, for example, by generating a Markov chain which has the Bayesian probability distribution as a stationary distribution, see Markov Chain Monte Carlo in Practice for details.
• the samples span the range of possible solutions. If a temporal sequence of slices is to be segmented, the segmentation consists then in a temporal series of shapes. Such a sample can be sequentially generated, using sequential Monte Carlo techniques.
• a finite number of shapes representing volumes are generated.
• a motion model is used to predict the next set of shapes.
• a simple motion model assumes that the myocardium shape contracts by a constant factor at each time step.
• a weight of each sample which depends on the likelihood value of the sampled shape for the new time step, is then computed and used in the Monte Carlo integration.
• the sequential Monte Carlo method is well known in the arts, and used in other applications such as, for example, financial mathematics, tracking, etc.
• the method also called particle filtering, is useful for following in time objects in motion.

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• Engineering & Computer Science (AREA)
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• General Physics & Mathematics (AREA)
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• Measuring And Recording Apparatus For Diagnosis (AREA)
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• 2005
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