Patent Application
20080049999
A more complete appreciation of the present disclosure and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:
In describing the exemplary embodiments of the present disclosure illustrated in the drawings, specific terminology is employed for sake of clarity. However, the present disclosure is not intended to be limited to the specific terminology so selected, and it is to be understood that each specific element includes all technical equivalents which operate in a similar manner.
Modern medical imaging techniques, for example CT scans and MR scans, generally involve sets of multiple scans taken at different times and/or from different positions. Moreover, even a single scan, for example, an MR scan, may include multiple data sequences.
In MR imagery, the structure to be imaged is subjected to a strong electro-magnetic (EM) field. The EM field elevates the magnetic moment of the atomic nuclei of the scanned matter to an excited state and the rate in which the magnetic moments return to equilibrium (relax), a characteristic that is highly indicative of the particular form of matter being scanned, is closely monitored. By analyzing these relaxation times, the forms of matter within the scanned structure may be imaged.
There are several relaxation times that are relevant to MR imaging. For example, T1, also known as spin-lattice relaxation time, is defined as the component of relaxation which occurs in the direction of the ambient magnetic field. This generally comes about by interactions between the nucleus of interest and unexcited nuclei in the environment and ambient electric fields. T1 is measured as the time required for the magnetization vector to be restored to 63% of its original magnitude.
T2, also known as spin-spin relaxation time, is defined as the component of relaxation which occurs perpendicular to the ambient magnetic field. This relaxation is dominated by interactions between spinning nuclei that are already excited. T2 is measured as the time required for the transverse magnetization vector to drop to 37% of its original magnitude after its initial excitation.
T2* is the characteristic time constant that describes the decay of transverse magnetization, taking into account the inhomogeneity in static magnetic fields and the spin-spin relaxation in the human body. T2* is thus influenced by magnetic field gradient irregularities. T2* is increased with iron deposition.
The observation of each set of relaxation times constitutes a separate data sequence. For example, spin-lattice relaxation time (T1) may constitute a first data sequence while transverse magnetization decay time (T2*) may constitute a second data sequence.
Other exemplary data sequences include the Short Tau Inversion Recovery (STIR) and Half-Fourier Single-Shot Turbo Spin-Echo (HASTE).
Moreover, multiple scans may be taken in sequence. For example, a patient may undergo a first MR scan, followed by a second MR scan a short time later. For example, it is common for the same structure to be imaged several times, in overlapping image sections such as a head image, torso image, pelvis image, etc. Each scan may involve a different resolution and/or other scan parameters may be varied. Subtle and large movements may also affect the scan. For example, the patient may shift position between scans and/or internal organs such as the heart and lungs may be imaged in varying stages of contraction.
In each scan and/or data sequence, different information may be captured. Accordingly, a bone marrow lesion that may be hidden in one scan/data sequence may be observable from another scan/data sequence. Moreover, difference scans/data sequences may provide different insights into a particular lesion. For example, a first scan/data sequence may provide the best indication of the presence of a lesion, but characterization of the lesion may require reference to one or more other scans/data sequence. In fact, the information revealed about a lesion from the first scan/data sequence may dictate which other scans/data sequences may be most useful to refer to in establishing or confirming a diagnosis.
Accordingly, exemplary embodiments of the present invention utilize multiple scans and/or multiple data sequences to identify and/or classify a lesion and/or other region of interest. Detection of a lesion and/or other region of interest may thus be performed automatically by reference to multiple scans and/or data sequences.
For example, different data sequences such as a T1 data sequence, a T2 data sequence, and a T2* data sequence may each comprise an image data set. For example, different scan orientations such as a coronal scan, a saggital scan, a head scan, a torso scan, a pelvis scan, and a high-resolution spine image scan may each comprise an image data set.
The image data may be obtained, for example, from a medical image device such as an MR scanner or a CT scanner. Alternatively, the image data may be obtained from a medical image database.
Once obtained, intensity inhomogeneity correction may be performed for all image data under consideration (Step S11). Intensity inhomogeneity may appear as a sudden shift in pixel intensities within a medical image. Intensity inhomogeneity may be caused, for example, when different magnetic coils of an MRI provide different magnetic field strengths. Intensity inhomogeneity correction may thus be performed to reduce or eliminate the shifts in pixel intensity that are caused by factors other than the internal structure of the patient being imaged.
At this point, the region of interest, for example, one or more bones, has been imaged in multiple sets of image data. If the image data are well aligned (Yes, Step S12) then the region of interest, for example, one or more bones, may be segmented in one set of image data, for example, the set of image data that includes the greatest portion of the region of interest and this image segmentation may be applied to each of the other sets of image data that are well aligned (Step S13). Alternatively, if the image data are not well aligned (No, Step S12) then the region of interest, for example, one or more bones, may be segmented individually for each set of image data (Step S14). Some combination of these various alternatives may be used. For example, if there are some image data sets that are well aligned and other image data sets that are not well aligned, then those image data sets that are well aligned may be segmented with respect to one of the image data sets while those image data sets that are not well aligned may be individually segmented.
Segmentation of the region of interest may involve, for example, isolation of the image data involving the region of interest and removal of data not within the region of interest. The result of segmentation may be an image of only the region of interest, for example, the one or more bones of interest.
Where multiple image data sets are overlapping, segmentation may be performed either before or after the overlapping multiple image data are combined.
Where the image data are not well aligned (No, Step S12) an image alignment step may be performed (Step S15) after segmentation (Step S14). Image alignment may be performed based on the region of interest. For example, where the region of interest is one or more bones, image alignment may be performed with reference to the one or more bones of interest.
Alternatively, a general image alignment may be performed without regard to any particular region of interest.
Detection may then be performed on each image data set individually to identify regions of suspicion, for example, lesion candidates (Step S16). The region of suspicion or lesion candidate may be, for example, a bone marrow metastases candidate. Where the region of interest is one or more bones, a region of suspicion may be an area identified as being a potential lesion. Because detection is performed individually for each image data set, data from individual scans and data from individual data sequences may be separately processed for detection of regions of suspicion.
Detection may be automatically performed for each image data set either in series (e.g. one image data set at a time) or in parallel (e.g. multiple image data sets are processed at the same time) however, regardless of whether data sets are processed in series or in parallel, each data set is processed individually.
Where one or more regions of suspicion are detected in a particular image data set (Yes, Step S17) the location of the one or more regions of suspicion are identified in the other image data sets (Step S18). Where no regions of suspicion are detected (No, Step S17) the process may end.
After detecting the presence of regions of suspicion from all image data sets, the locations of the discovered regions of suspicion are added to a list of discovered regions of suspicion (Step S19). Image information at the location of each of the discovered regions of suspicion are taken from each of the image data sets (Step S20) so that a database is formed including the location of each discovered regions of suspicion and the image information found at that location in each image data set.
Selection and classification is them performed for each regions of suspicion based on the image information from each image data set at the location of the regions of suspicion (Step S21). Selection and classification is an automated process by which each region of suspicion is examined to determine whether it is an abnormality (for example, a lesion) or a false positive, and if it is an abnormality, what sort of abnormality it is.
Selection and classification may be performed either from the information of each data set individually, or a decision tree may be used to perform and confirm a diagnosis. For example, a decision tree may be used to generate a list of possible diagnoses for a given lesion candidate and may then be used to determine which image data set should be referred to in order to confirm or rule out particular possible diagnoses.
Results of selection and classification may then be outputted (Step S22) for the benefit of a medical practitioner to render a diagnosis.
For example, the following image data sets may be obtained: a T1 data sequence, a T2 data sequence, a T2* data sequence, a coronal scan, a saggital scan, a head scan, a torso scan, a pelvis scan, and a high-resolution spine image. Then, inhomogeneity correction may be performed on each of the above-named image data sets. In this example, the region of interest is the spinal cord. Here, the image data sets are not well aligned and the spinal cord is separately segmented in each image data set.
Thus information may be obtained for each region of suspicion. In performing this step, the region of suspicion may be segmented from each scan. Then, from the segmented region of suspicion, volume, shape, spherecity, spikiness and/or texture may be calculated. Then, because the same region of suspicion is separately segmented for each image, a consensus for the volume, shape and location of the region of suspicion may be calculated, for example, using averaging, information fusion, and/or robust estimation. Information fusion is a way of calculating a weighted average for the region of suspicion where weight represents a degree of confidence or uncertainty for each value from each scan.
As an alternative to separately segmenting the region of suspicion from each scan, a consensus segmentation may be calculated across each scan. Then, volume, shape, sphericity, spikiness and/or texture of the region of suspicion may be calculated from the consensus segmentation. Calculating the consensus segmentation may include calculating union, intersection, weighed summation, order-statistical filtering, or thresholding of the region of suspicion from each data set.
Image alignment is performed so that each image data set is registered against the same coordinate system so that a particular object found in one image data set may be easily found in all of the other image data sets. Here, image alignment is performed with respect to the object of interest, the spinal cord.
Lesion candidate detection may then be performed individually in each of the above-named image data sets. It is possible that a lesion observable in one of the image data sets may not be observable in other of the image data sets. Accordingly, detection may be performed individually for each image data set.
Selection and classification is then performed for each lesion candidate to determine whether there is a lesion present and if so, whether the lesion is a bone metastases or another identifiable lesion classification. Finally, a listing of all detected lesions and corresponding classifications is outputted.
Exemplary embodiments of the present invention may also include one or more other optional features. For example, automatic lesion measurement may be performed for each detected lesion so that measurement information such as lesion volume, maximum diameter (in both two and three dimensions), etc. These measurements may then be used during the selection and classification step.
Automatic lesion counting may be performed, either within the patient's whole body or within a particular region, for example, the patient's spine.
Moreover, lesion measurements may be combined across multiple scans. For example, measurements of the same lesion taken from multiple scans such as a coronal scan and a sagittal scan, and/or different pulse sequences, may be combined to provide a more complete set of measurements.
Where multiple scans encompass scans taken at different points in time, for example, two scans taken a month apart in time, change analysis may be performed on the multiple scans to follow the change in the lesion's characteristics as time progresses and/or after treatment.
The computer system referred to generally as system 1000 may include, for example, a central processing unit (CPU) 1010, random access memory (RAM) 1020, a graphical processing unit (GPU) 1030 connected to a display unit 1040, a network adapter 1070 connected to a network 1080, for example an intranet or the Internet, an internal bus 1005, and one or more input devices 1050, for example, a keyboard, mouse etc. As shown, the system 1000 may be connected to a data storage device 1060, for example, a hard disk.
The CPU 1010 may access and/or receive image data from an image acquisition station 1100 and/or a database 1090, for example, via the network 1080. The image acquisition station 1100 may include an MR scanner, a CT scanner or any other form of medical imaging device. The database 1090 may include previously acquired image data, for example, MR datasets and/or CT data sets.
The above specific exemplary embodiments are illustrative, and many variations can be introduced on these embodiments without departing from the spirit of the disclosure or from the scope of the appended claims. For example, elements and/or features of different exemplary embodiments may be combined with each other and/or substituted for each other within the scope of this disclosure and appended claims.
The present application is based on provisional application Ser. No. 60/840,620, filed Aug. 28, 2006, the entire contents of which are herein incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 60840620 | Aug 2006 | US |
