Method For Detecting The Presence Of Abnormal Tissue

Abstract

A computer implemented method is usable to detect the presence of abnormal tissue through analysis of magnetic resonance relaxation times T1 and T2. The relaxation times T1 and T2 are determined from a data set obtained from a magnetic resonance apparatus. The method includes: loading the data set from at least one tissue into a computing device; determining a region of interest; determining an average value of the free induction decay signal within the region of interest on each of the scans separately; detecting scans with outlier data in each data series; and, if a scan with outlier data is detected, identifying the scan in the data series; determining the relaxation time within the region of interest based on scans from the corresponding data series that are not identified as having outlier data; classifying the tissue as normal or abnormal based on predefined values, which are determined depending on the type of tissue analyzed.

Claims

1. A method for detecting a presence of abnormal tissue using T1 and T2 relaxation times, wherein the T1 and T2 relaxation times are determined in a computing device from analysis of a data set obtained through operation of a magnetic resonance apparatus on at least one tissue, wherein the data set comprises data series that includes scans corresponding to successive moments in time containing information about an intensity of a free induction decay signal, comprising: (a) loading into the computing device the data set from the at least one tissue, wherein the data set includes at least one data series describing longitudinal relaxation and at least one data series describing transverse relaxation, (b) determination of a region of interest (ROI), wherein the region of interest does not change between successive scans in each at least one data series, (c) determining an average value of the free induction decay signal within the region of interest in each of the scans separately, (d) detecting scans with outlier data in each data series, wherein detection of scans with outlier data is determined through analysis of the average value of the intensity of the free induction fading signal within the region of interest for each scan in the respective data series, (e) responsive at least in part to detecting a scan with outlier data in (d), identifying the scan with outlier data in the at least one data series, (f) determining the relaxation time in the region of interest based on scans in the corresponding at least one data series which have no outlier data, wherein relaxation time T1 is determined from the longitudinal relaxation data series and relaxation time T2 is determined from the transverse relaxation data series, (g) classifying tissue as normal or abnormal on the basis of predefined values which are determined according to the type of tissue examined.

2. The method according to claim 1, wherein in step (d) the isolation forest algorithm is used for detecting scans with outlier data.

3. The method according to claim 1, wherein a parameter or an algorithm which is a coefficient describing an abnormality of a particular average value in the analyzed data series, is no more than 0.2, preferably no more than 0.1.

4. The method according to claim 1, wherein after step (e) in the region of interest (ROI), uncorrected relaxation times are determined based on all scans of the respective data series without excluding scans with outlier data, wherein relaxation time T1 is determined from the data series describing longitudinal relaxation and relaxation time T2 is determined from the data series describing transverse relaxation.

5. The method according to claim 1 and prior to (d), further comprising: verification of the times of echo (TE) and times of repetition (TR) recorded in the data series, wherein when a data series contains a constant time of echo (TE) and a variable time of repetition (TR) a determination is made that the respective data series is a valid data series describing longitudinal relaxation, and wherein when a data series contains a constant time of repetition (TR) and a variable time of echo (TE) a determination is made that the respective data series is a valid data series describing transverse relaxation, and wherein in case of different correlations the analysis is interrupted.

6. The method according to claim 1 wherein the determination of the relaxation time in (f) comprises: based on the mean values of the free induction decay signal within the region of interest (ROI) determined in (c), a characteristic of the changes in the intensity of the free induction decay signal over time is generated, where each time point corresponds to a separate scan, a relaxation curve is determined, being an approximation curve corresponding to a predefined mathematical model, and further comprising: for the determined relaxation curve, determination of the relaxation time, which is a parameter of the curve, calculation of the mean square error as a measure of the fit of the individual models to the data, and classification of the tissue as normal or abnormal on the basis of predefined values which are determined according to the type of tissue examined.

7. The method according to claim 6, wherein the determined relaxation times, relaxation curves, characteristics of changes in the intensity of the free induction decay signal over time, and measures of the fit of the individual models are stored in a results database.

8. The method according to claim 6, wherein the predefined mathematical model of the relaxation curve is an exponential model, an exponential model with a shift, or a bi-exponential model.

9. The method according to claim 1, wherein at least one of the following algorithms in (f) is used for tissue classification: naive Bayes classifier, neural network, support vector machine, random forest and decision tree.

10. The method according to claim 1, wherein at least one tissue to which the data set relates is a post-operative breast tumour sample, potentially cancerous.

11. The method according to claim 1, and further comprising: subsequent to (g) providing the classification to an expert system that includes clinical data on a patient associated with the at least one tissue, calculating a predicted survival time of the patient through operation of the expert system responsive at least in part to the classification and the clinical data.

12. At least one computer readable medium bearing non-transitory computer program instructions that when executed by a computing device are operative to configure the computing device to carry out the method steps recited in claim 1.

Description

BRIEF DESCRIPTIONS OF DRAWINGS

[0056] FIG. 1 is a schematic view of an exemplary magnetic resonance measuring system.

[0057] FIG. 2 is a schematic representation of the steps carried out in connection with an exemplary method.

[0058] FIG. 3A shows an exemplary relaxation curve without the removal of outlier data, including the calculated value of the mean square error of the fitted curve.

[0059] FIG. 3B shows the relaxation curve data of FIG. 3A after the removal of outlier data, and the calculated value of the mean square error of the fitted curve.

[0060] FIG. 4A illustrates a determined region of interest.

[0061] FIG. 4B shows a corresponding illustrative T1 map that corresponds to the region of interest in FIG. 4A.

[0062] FIG. 5 shows an example of fitting in exemplary relaxation curve according to different mathematical models and the determined values of relaxation time.

[0063] FIG. 6 is a table of terminology that includes terms in the Polish language that are utilized in the Figures, and their corresponding English terminology.

DETAILED DESCRIPTION

[0064] Referring now to the drawings and particularly to FIG. 1, there is shown therein a magnetic resonance apparatus generally indicated 1. The magnetic resonance apparatus is in operative connection with at least one computing device 2. The exemplary at least one computing device 2 is in operative connection with at least one data store 3 which includes at least one data set obtained through operation of the magnetic imaging apparatus from at least one tissue sample, which is alternatively referred to herein as a tissue.

[0065] In exemplary arrangements the at least one computing device 2 includes one or more circuits that are operative to communicate electrical signals. The one or more circuits include at least one processor and at least one data store. In exemplary arrangements the at least one processor may include a processor suitable for carrying out computer program instructions that are stored in the one or more associated data stores. The at least one processor includes or is in operative connection with a nonvolatile storage medium including instructions that include a basic input/output system (BIOS) or other interfaces. For example, processors may correspond to one or more of the combination of a CPU, FPGA, ASIC or any other integrated circuit or other type of circuit that is capable of processing data and program instructions. Exemplary data stores may correspond to one or more of volatile or nonvolatile memories such as random access memory, flash memory, magnetic memory, optical memory, solid-state memory or other mediums that are operative to store computer program instructions and data. Computer program instructions may include instructions in any of a plurality of programming languages and formats including, without limitation, routines, subroutines, programs, threads of execution, objects, methodologies, scripts and functions which carry out the actions such as those described herein. Structures for processors may include, corresponds to and utilize the principles described in the textbook entitled Microprocessor Architecture, Programming and Applications with the 8085 by Ramesh S. Gaonker, Sixth Edition (Penram International Publishing 2013) which is incorporated herein by reference in its entirety.

[0066] The exemplary data stores used in connection with exemplary arrangements may include one or more types of mediums suitable for storing non-transitory computer program instructions and data. Such mediums may include, for example, magnetic media, optical media, solid-state media or other types of media such as RAM, ROM, PROMs, flash memory, solid-state memory, computer hard drives or any other medium suitable for holding data and computer program instructions.

[0067] The method according to the exemplary arrangements and carried out with at least one computer device is shown schematically in FIG. 2. Detection of the presence of abnormal tissues in at least one tissue by means of relaxation times T1 and T2 includes making determinations through operation of the computing device 2 on the basis of analysis of a data set in the at least one data store 3 obtained from the magnetic resonance apparatus 1 from the at least one tissue. The data set comprises a series of data which corresponds to scans corresponding to successive moments in time and containing information about the intensity of the free induction decay signal.

[0068] The in vitro tissue samples analyzed in the exemplary arrangements are examined at a constant temperature, that is cooled to 5 degrees Celsius. The tissues are cooled for transport and their temperature is kept constant during testing. The examination of the tissues is carried out at a magnetic field strength of 1.5 T. The method in the exemplary arrangements includes the following steps:

[0069] 100—entering the identifier for the tissue subject to analysis, layer and mode (T1 or T2). This information is stored in the at least one data store along with the associated data and the determined information,

[0070] 200—loading a data set derived from at least one tissue into the computing device 2, wherein the data set comprises at least one data series describing longitudinal relaxation (T1) values and at least one data series describing transverse relaxation (T2) values,

[0071] 400—determining an area of interest, wherein the area of interest (ROI) does not change between successive scans in each data series,

[0072] 401—determining the average value of the free induction decay signal within the region of interest in each of the scans separately,

[0073] 402—detecting scans with outlier data in each data series, wherein the determined average value of the intensity of the free induction fading signal within the region of interest for each scan in the data series is analyzed,

[0074] 403—if a scan with outlier data is detected, determination of such a scan in the data series,

[0075] 404—determining the relaxation time in the region of interest based on scans from the corresponding data series determined and flagged as non-outliers, with relaxation time T1 determined from the data series describing longitudinal relaxation, and relaxation time T2 determined from the data series describing transverse relaxation

[0076] 500—classifying the tissue as normal or abnormal based on predefined values that are determined according to the type of tissue under investigation.

[0077] In the first aspect of this exemplary method, when detecting scans 402 with outlier data, an isolation forest algorithm is used for making the determination and the algorithm parameter contamination factor is 0.1.

[0078] In a second aspect of this exemplary method, after the step 403 in the region of interest (ROI), uncorrected relaxation times are determined based on all scans in the respective data series without excluding scans with outlier data, wherein the relaxation time T1 is determined based on the data series describing longitudinal relaxation and the relaxation time T2 is determined based on the data series describing transverse relaxation.

[0079] In a third aspect of this exemplary method, prior to the step of detecting scans 402 with outlier data, a verification of the times of echo (TE) and times of repetition (TR) recorded in the data series is performed, wherein when the data series includes a constant time of echo (TE) and a variable time of repetition (TR) it is determined to be a valid data series describing longitudinal relaxation, and when the data series includes a constant time of repetition (TR) and a variable time of echo (TE) it is determined to be a valid data series describing transverse relaxation. In case of different relationships, the data series is determined as not valid and the analysis is terminated.

[0080] In a fourth aspect of the exemplary method, determining the relaxation time 404 comprises: [0081] based on the mean values of the free induction decay signal within the region of interest (ROI) determined in step (c), a characteristic of the changes in the intensity of the free induction decay signal over time is generated, where each time point corresponds to a separate scan, a relaxation curve is determined, which is an approximation curve corresponding to a predefined mathematical model, then [0082] for the determined relaxation curve the relaxation time is determined, which is a parameter of this curve, [0083] the fit measures of the individual models to the data are calculated, [0084] the tissue is then classified as normal or abnormal on the basis of predefined values which are determined according to the type of tissue under investigation, [0085] the determined relaxation times, relaxation curves, and characteristics of changes in the intensity of the free induction decay signal over time are stored in at least one data store in a results database. [0086] In exemplary arrangements based on the measurements with a 95% probability (confidence level) of the values, [0087] T1 values are in the range 251.85 ms-301.94 ms [0088] T2 values are in the range of 114.19 ms-125.65 ms.

[0089] In the fifth aspect of the exemplary arrangement, the predefined mathematical model of the relaxation curve is an exponential model, an exponential model with shift, or a bi-exponential model, these models having different forms for longitudinal and transverse relaxation.

[0090] Based on the longitudinal relaxation process, the time T1 is determined. As the signal intensity increases over time, the T1 time is determined when the signal reaches 63% of its final value.

[0091] The basic exponential model used in an exemplary arrangement is based on Bloch's equations and the equation of the curve is as follows:

M=M.sub.0[1−exp(−t/T.sub.1)]  (1)

[0092] In the above equation, M is the resultant value of the longitudinal magnetisation vector, M0 its final value (return to equilibrium after an earlier deflection by the radio signal), t is the time and T1 is the longitudinal relaxation time.

[0093] The shifted exponential model in an exemplary arrangement is a modification of the basic model by an additional factor k:

M=M.sub.0[1−k(exp(−t/T.sub.1))],  (2)

[0094] For example k=2

M=M.sub.0[1−2(exp(−t/T.sub.1))]  (3)

[0095] The bi-exponential model used in an exemplary arrangement in which the weights w.sub.s and w.sub.L of the so-called short and long components of the relaxation time are defined, and T1s and T1L are the values of these two components. In this model the determined relaxation time consists of two numbers.

M=M.sub.0w.sub.s[1−exp(−t/T.sub.1s)]+M.sub.0w.sub.L[1−exp(−t/T.sub.1L)]  (4)

[0096] Transverse relaxation processes are used to calculate the T2 time. There are several fundamental difficulties associated with recording a fading signal. One of them is the need to compensate for the presence of a baseline or to deal with measurement noise. Although it is possible to find studies in which the performance of an exponential model has been found to be satisfactory, it is more common to find opinions that only more complex models are able to adequately represent the relaxation processes.

[0097] The basic exponential model describes the occurring phenomenon in some approximation:

M=M.sub.0[exp(−t/T.sub.2)]  (5)

[0098] The shifted exponential model is much more faithful, and a fit with less error can be obtained by adding a constant b to the basic model. This is approximately equal to the baseline signal level (i.e. the minimum recorded after some time t>>T2):

M=M.sub.0[exp(−t/T.sub.2)]+b  (6)

[0099] The bi-exponential model allows the relaxation curve to be fitted with even less error than previous methods. At the same time, it is more susceptible to distortions caused by the large scatter of the measured intensity values.

M=M.sub.s[exp(−n(t/T.sub.2s))]+M.sub.L[exp(−n(t/T.sub.2L))]+s  (7)

[0100] As for time T1 the relaxation time T2 determined in this model consists of two numbers.

[0101] In the sixth aspect of the exemplary arrangement of the tissue classification, the naive Bayes classifier algorithm and a random forest algorithm are used. The result is whether the sample described by the determined relaxation times is a normal tissue or a diseased tissue.

[0102] In a seventh aspect of the exemplary arrangement, the at least one tissue to which the data set relates is a post-operative breast tumour sample, potentially cancerous.

[0103] In a further aspect of the exemplary arrangement a computer program product comprises at least one medium bearing non-transitory computer program instructions and is characterized in that, when run on a computing device, it performs the steps of the method defined in the first arrangement and is therefore not repeated herein.

[0104] In a further aspect of the exemplary arrangement, the method according to the exemplary arrangement is applied to an expert system that uses clinical data on a patient contained in a database to support diagnostic decisions, characterised in that a predicted survival time is determined based on the clinical data and the classification results obtained according to the method.

[0105] The exemplary arrangements may also be used in diagnostic devices, providing a method for determining T1 and T2 relaxation times that is robust and tolerant to interference and missing data.

[0106] Thus, the exemplary arrangements achieve improved capabilities, eliminate difficulties encountered in the use of prior methods and approaches, and attain the useful results described herein.

[0107] In the foregoing description certain terms have been used for brevity, clarity and understanding. However, no unnecessary limitations are to be implied therefrom because such terms are used for descriptive purposes and are intended to be broadly construed. Moreover the descriptions and illustrations herein are by way of examples and the new and useful aspects of the arrangements are not limited only to the exact features that have been shown and described.

[0108] Having described the features, discoveries and principles of the exemplary arrangements, the manner in which they are utilized and operated, and the advantages and useful results attained, the new and useful features, methodologies, elements, arrangements, devices, parts, combinations, systems, operations, processes and relationships are set forth in the appended claims.