SYSTEM AND METHOD FOR ESTIMATING AN INDICATOR OF THE TISSUE ACTIVITY OF AN ORGAN

Abstract

The invention relates to a system and method for quantifying a novel biomarker of the tissue activity of a human or animal organ. By way of preferred application, such a biomarker describes the diffusivity of biological fluids in living tissues in the form of a novel indicator of the diffusion of water molecules in living tissues on the basis of diffusion data resulting from the acquisition of a sequence of images of one or more parts of the body of an animal or human patient. Particularly resistant and stable with respect to noise present in the medical imaging signals from which the experimental data stem, the novel biomarker is relevant in a large number of applications including, inexhaustively, the analysis and/or monitoring of cancers, or the assessment of strokes.

Claims

1. A method for quantifying a biomarker of an elementary volume, called “voxel”, of an organ, said method being implemented by a processing unit of a diffusion MRI imaging analysis system, and comprising a step for generating the value of said biomarker, hereinafter denoted “tissue activity indicator” or TAI, on the basis of experimental data S(b), wherein said step for generating the value of said biomarker TAI comprises, over a delimited interval b.sub.min to b.sub.max of values of an acquisition parameter b corresponding to the intensity of the diffusion gradient, the calculation TAI=∫.sub.b.sub.min.sup.b.sup.maxL(b)−Γ.sub.S [S(b)] db, L(b) being a function of said acquisition parameter b and Γ.sub.S[S(b)] a bijective transformation of said experimental data S(b).

2. The method according to claim 1, wherein said function and bijective transformation are mutually determined, so that L(b) is greater than or equal to Γ.sub.S[S(b)] over all the values of the acquisition parameter b between b.sub.min and b.sub.max.

3. The method according to claim 1, wherein said function and bijective transformation are mutually determined, such that L(b.sub.min)=Γ.sub.S[S(b.sub.min)] and/or L(b.sub.max)=Γ.sub.S[S(b.sub.max)].

4. The method according to claim 1, the imaging analysis system comprising an output human-machine interface of the quantified biomarker (TAI) to a user of said system, said output human-machine interface cooperating with the processing unit, said method comprising a subsequent step for triggering an output of said quantified biomarker (TAI) in a suitable format.

5. The method according to claim 1, the imaging analysis system comprising an input human-machine interface cooperating with the processing unit, said method comprising a step of determining the function L(b) of said acquisition parameter b and the bijective transformation Γ.sub.S[S(b)] of said experimental data S(b) on the basis of input data of a user of said input human-machine interface.

6. The method according to claim 1, wherein the step for generating the value of said biomarker is implemented by successive iterations for a plurality of voxels in question, said biomarker (TAI) being quantified per voxel.

7. The method according to claim 4, wherein the step for generating the value of said biomarker is implemented by successive iterations for a plurality of voxels in question, said biomarker (TAI) being quantified per voxel, and the step for triggering an output of said quantified biomarker (TAI) comprises generating an image in the form of a parametric map the pixels of which respectively encode the values of said quantified biomarker for the voxels in question.

8. An imaging analysis system comprising a processing unit, an interface for communicating with the outside world and storage media, wherein: the communication interface is arranged to receive from the outside world experimental data S(b) of an elementary volume of an organ; and the storage media contains instructions, the interpretation or execution of which by said processing unit causes the implementation of a method for quantifying a biomarker of an elementary volume of said organ according to claim 1.

9. A imaging analysis system according to claim 8, wherein: the experimental data S(b) of an elementary volume of an organ are data resulting from an acquisition of a signal by diffusion MRI imaging; and the quantified biomarker is an indicator of the diffusion of water molecules (TAI) in an elementary volume of said organ.

10. The system according to claim 8, wherein the communication interface is arranged to transmit a graphical content, associated with said quantified biomarker by the implementation of a step of for triggering an output of said quantified biomarker (TAI) in a suitable format, to an output human-machine interface.

11. The system according to claim 8, wherein the communication interface is arranged to collect input data transmitted by an input human-machine interface of said system, said input data making it possible to determine a function L(b) of the acquisition parameter b and a bijective transformation Γ.sub.S[S(b)] of said experimental data S(b).

12. A non-transitory computer-readable medium storing a program comprising one or more instructions that can be interpreted or executed by a processing unit of an imaging analysis system, wherein the interpretation or execution of said instructions by said processing unit causes the implementation of a method for quantifying a biomarker (TAI) of an elementary volume according to claim 1.

Description

[0045] Other characteristics and advantages will become more clearly apparent on reading the following description and on examination of the accompanying figures among which:

[0046] FIG. 1, already described, shows a simplified description of a system for the analysis of images obtained by nuclear magnetic resonance;

[0047] FIG. 2, already described, shows a simplified description of a variant of a system for the analysis of images obtained by nuclear magnetic resonance;

[0048] FIG. 3, already described, shows an example of an intensity attenuation curve of an item of experimental data as a function of an acquisition parameter;

[0049] FIG. 4, already described, shows an example of logarithmic representation of intensity attenuation of an item of experimental data as a function of an acquisition parameter;

[0050] FIG. 5 shows a simplified description of a method according to the invention for quantifying a biomarker of an organ;

[0051] FIGS. 6A, 6B and 6C show variants of a quantification of a biomarker according to the invention on the basis of experimental data of a voxel of an organ;

[0052] FIG. 7A shows an anatomic image comprising a region of interest, in this case a prostate, obtained by a T2 sequence;

[0053] FIG. 7B shows, in the form of an image, the graphical rendering of a quantified biomarker for each voxel of the aforementioned prostate with reference to FIG. 7A, on the basis of experimental diffusion data, following a method according to the invention;

[0054] FIG. 7C shows, in the form of an image, the graphical rendering of an estimated biomarker for each voxel of the aforementioned prostate with reference to FIG. 7A, on the basis of experimental diffusion data and a mono-exponential model following a method according to the prior art;

[0055] FIG. 7D shows, in the form of an image, the graphical rendering of an estimated biomarker for each voxel of the aforementioned prostate with reference to FIG. 7A, on the basis of experimental diffusion data and a bi-exponential model of the IVIM (intravoxel incoherent motion) type following a method according to the prior art;

[0056] FIG. 8A shows in the form of an image, the determination of four regions on the basis of a graphical representation such as that described with reference to FIG. 7B, obtained by the quantification of a biomarker according to the invention, said four regions describing a region of interest focused on the prostate and three other regions adjacent to the aforementioned;

[0057] FIG. 8B shows, in the form of histograms, the “contrast-to-noise” ratios during the distinguishing of said region of interest with respect to each of the three other aforementioned regions, when the source image is that obtained by the quantification of a biomarker (cf. FIG. 7B) according to the invention or according to the prior art, on the basis of a mono-exponential (cf. FIG. 7C) or bi-exponential (cf. FIG. 7D) model.

[0058] There will now be described, with reference to FIGS. 5 and 6A, a preferred but non-limitative embodiment example of a method according to the invention for quantifying a novel biomarker TAI, said biomarker being similar to a tissue activity indicator, obtained on the basis of experimental diffusion data of an organ, for example a prostate, in a human being. The invention is not to be considered limited to these examples of acquisition methods, organs, alone and could be applied to an animal.

[0059] Such a method 100 is intended to be implemented by a processing unit of a medical imaging analysis system such as that shown above with reference to FIG. 1 or FIG. 2.

[0060] It mainly comprises a step 130 for generating a biomarker TAI on the basis of experimental data S(b) for each voxel of interest of an organ. Such experimental data can be generated in a prior step 120 on the basis of an acquisition of signals by diffusion MRI imaging, according to an acquisition parameter b, in this case the intensity of the diffusion gradient, commonly called “parameter b”. Such a step 130 can be implemented iteratively in order to quantify such a biomarker TAI for a set of voxels of interest.

[0061] A method 100 according to the invention also comprises, like estimation methods for other biomarkers originating from the prior art, a step 140 for encoding the value or the values of the quantified biomarker TAI for one or a plurality of voxels in the form of a graphical content, for example in the form of a parametric map. Such a parametric map can be presented in the form of a table of pixels, or commonly called “image” like the example of the parametric map PCb shown in FIG. 7B. Each pixel of said parametric map PCb advantageously encodes a triplet of integer values comprised between zero and two hundred and fifty-five according to the RGB (abbreviation for “red green blue”) colour coding. Such a computerized colour coding is the most used by the available material. In general, computer screens reconstitute a colour by additive synthesis on the basis of three primary colours, a red, a green and a blue, forming on the screen a mosaic that is generally too small to be distinguished by a human eye. The RGB coding gives a value for each of these primary colours. Such a value is generally coded on one byte and thus belongs to an integer value interval comprised between zero and two hundred and fifty-five. The step 140 can thus consist of advantageously encoding the value of the biomarker TAI quantified for a voxel of interest on the basis of a colour gradient, for example from blue to yellow for encoding said biomarker from the lowest value to the highest. In this way, when a plurality of voxels of an organ are the subject of interest, each pixel of the parametric map PCb is associated with the corresponding voxel so as to show graphically, in two dimensions, the respective values of the quantified biomarker, for said plurality of voxels. The pixels encoding yellow describe the voxels for which the biomarker TAI demonstrates a high tissue activity, unlike those encoding green, demonstrating a medium tissue activity, or also those encoding blue, demonstrating a very low tissue activity. Any other graphical encoding could be implemented in step 140, in addition or in a variant, of the aforementioned gradient.

[0062] When the imaging analysis system implementing the method 100 comprises an output human-machine interface, such as a computer screen 5 as shown by the example in FIG. 1 or 2, the step 140 also consists of triggering an output in the advantageous form of a display or any outputting mode intelligible by a human being, of said indicator of quantified tissue activity TAI in a parametric map PCb, as mentioned above or in any other suitable format. In this way, a user 6 of said analysis system can consult the results of said quantification of the biomarker and benefit from a decision-making aid for a treatment act, for establishing a diagnosis or also for confirming or invalidating a clinical test.

[0063] To describe an example implementation of the step 130 of quantification of a biomarker TAI according to the invention, the experimental data S(b) should be considered, resulting from a step 120 of said method 100, for generating said experimental data on the basis of an acquisition of a signal by diffusion imaging in which the acquisition parameter b is the intensity of the diffusion gradient.

The step 130 therefore consists of the calculation

[00006]$\mathrm{TAI}=\underset{b_{\min }}{\overset{b_{\max }}{\int }}L\left(b\right)={\Gamma }_S\left[S\left(b\right)\right]\mathrm{db}$

where L(b) is a function of said acquisition parameter b and Γ.sub.S[S(b)] bijective transformation of said experimental data S(b). According to this example, said function and bijective transformation can be mutually determined, so that L(b) is greater than or equal to Γ.sub.S[S(b)] over all the values of the acquisition parameter b comprised between b.sub.min and b.sub.max. In this way, the value of the biomarker TAI remains positive between b.sub.min and b.sub.max. The invention is not to be considered limited by this advantageous choice.

[0064] According to a first embodiment, the bijective transformation Γ.sub.S[S(b)] is the identity function. It is therefore possible to write: Γ.sub.S[S(b)]=S(b).

[0065] The function L(b) can then be chosen as the straight line that links S(b.sub.min) and S(b.sub.max), determined according to the following relationship:

[00007]$L\left(b\right)=\frac{S\left(b_{\max }\right)-S\left(b_{\min }\right)}{b_{\max }-b_{\min }}.Math.b+\frac{b_{\max }.Math.S\left(b_{\min }\right)-b_{\min }.Math.S\left(b_{\max }\right)}{b_{\max }-b_{\min }}$

[0066] A first embodiment of a quantification of the biomarker TAI, for a voxel of interest, is shown in FIG. 6A. The acquisition parameter, in this case the intensity of the diffusion gradient b, is comprised between the values b.sub.min and b.sub.max, respectively equal to 0 and 1000 s/mm.sup.2.

[0067] The experimental data S(b) are symbolized by points on a dotted line. The affine function L(b) describes a straight line marked on FIG. 6A in the form of an interrupted line. The quantification of the biomarker TAI corresponds to the subtraction, or the difference, between the area below the curve of the function L(b) and the area below the curve of the experimental data S(b). The area resulting from this subtraction is shown hatched in FIG. 6A and corresponds to the quantified value of the biomarker TAI in the form of a tissue activity indicator. It should be noted that advantageously the affine function L(b) was chosen such that L(b.sub.min)=Γ.sub.S[S(b.sub.min)]=S(b.sub.min) and L(b.sub.max)=Γ.sub.S[S(b.sub.max)]=S(b.sub.max).

[0068] FIGS. 6B and 6C show two variants of a quantification of the biomarker TAI. According to these latter, the experimental data S(b) are identical to those utilized within the framework of the quantification according to FIG. 6A. On the other hand, the function L(b) is chosen, i.e. pre-established, calculated or parameterized, such that the latter describes a constant over the value interval b.sub.min to b.sub.max. Thus, said function L(b) according to FIG. 6B is such that L(b) is constant and is equal to a value that is predetermined or deduced from the experimental data S(b). In this case, according to FIG. 6B,

[00008]$L\left(b\right)=\underset{b}{\max }S\left(b\right)$

over the value interval b.sub.min to b.sub.max, i.e. L(b) adopts the maximum value of the experimental data S(b) over said b-value interval. Like in FIG. 6A, the quantification of the biomarker TAI corresponds to the subtraction, or the difference, between the area below the curve of the function L(b) and the area below the curve of the experimental data S(b). The area resulting from this subtraction is shown hatched in FIG. 6B and corresponds to the quantified value of the biomarker TAI in the form of a tissue activity indicator. Such a constant can be determined or predetermined separately. Thus, as shown in FIG. 6C, the function L(b) can be chosen such that

[00009]$L\left(b\right)=\frac{1}{N_b}.Math.{.Math.}_{b}S\left(b\right)$

over the value interval from values b.sub.min to b.sub.max, where N.sub.b describes the number of samples or experimental data in question. According to this example shown in FIG. 6C, the quantification of the biomarker TAI can be done in a signed manner, in fact, the curves determined by the function L(b) and the experimental data S(b) are secant about a value of b close to 200 s/mm.sup.2. The difference between the areas below each curve L(b) and S(b) can be sometimes negative, sometimes positive, as shown in FIG. 6C for which the symbols “+” and “−” show these situations.

[0069] According to another technique, such a quantification of the biomarker TAI can be implemented by choosing a polynomial function L(b) of a higher order than or equal to two such that L(b)=αb.sup.2+βb+γ for which:

[00010]$\alpha =-\frac{S\left(b_{\max }\right)-S\left(b_{\min }\right)}{{\left(b_{\max }-b_{\min }\right)}^2}$$\beta =\frac{S\left(b_{\max }\right)-S\left(b_{\min }\right)}{b_{\max }-b_{\min }}-\alpha \left(b_{\max }+b_{\min }\right)$$\gamma =S\left(b_{\min }\right)-{\alpha b}_{\min }^2-{\beta b}_{\min }$

[0070] In a variant, the bijective transformation Γ.sub.s[S(b)] can be logarithmic in nature. The step 130 of the method 100 can generate the value of the biomarker TAI on the basis of the transformation Γ.sub.s[S(b)] such that

[00011]${\Gamma }_s\left[S\left(b\right)\right]=\ln \left[\frac{S\left(b\right)}{S\left(b_{\min }\right)}\right].$

According to this variant, the function L(b) can be defined as a decreasing straight line passing through the two points Γ.sub.s[S(b.sub.min)] and Γ.sub.s[S(b.sub.max)]. The invention is not to be considered limited by such choices or parameterizations of the function and bijective transformation. In a variant, any other combination suitable for the nature of the experimental data S(b) could be utilized, provided that L(b) remains a function of an acquisition parameter b and Γ.sub.s[S(b)] a bijective transformation of said experimental data S(b).

[0071] In order to parameterize the implementation of the step 130, the invention provides that a method 100 can comprise a step 110 for determining jointly the function L(b) and the bijective transformation Γ.sub.s[S(b)]. When the imaging analysis system comprises an input human-machine interface, such as the interface 8 described with reference to FIG. 1 or FIG. 2, said input human-machine interface cooperating with the processing unit implementing said method 100, an operator 6 can express a gesture command or more generally an instruction, via said input human-machine interface 8, in order to choose, for example, from a predetermined database, input and/or adapt said function L(b) and bijective transformation Γ.sub.s[S(b)] according to their customary usage, their hardware, the organ examined, etc. The joint determination of the function L(b) of said acquisition parameter b and of the bijective transformation Γ.sub.s[S(b)] of said experimental data S(b) is thus done on the basis of input data of said user 6 of said input human-machine interface 8. The operator 6 can thus easily optimize the implementation of the method 100, and, consequently, the quantification and/or the output of the biomarker TAI indicating the tissue activity of the organ examined.

[0072] FIGS. 7A to 7D make it possible to highlight the benefit conferred by the invention with respect to the known methods, through a preferred application example. FIG. 7A shows a high-resolution anatomic image AI, obtained by a T2 sequence, and comprising a region of interest ROI, in this case a prostate. Said region of interest ROI is represented by a circle in an interrupted white line.

[0073] FIG. 7B shows, in the form of a parametric map PCb, the graphical rendering resulting from a quantification of a biomarker TAI, according to the invention, associated with a plurality of voxels of interest. For each voxel of interest, said biomarker TAI has been quantified by the implementation of a method, such as the method 100 described above, on the basis of experimental diffusion data S(b) resulting from an acquisition of a diffusion imaging signal. The PCb image obtained expresses said biomarker TAI in a colour gradient ranging from dark blue to yellow, according to whether the biomarker comprises a low or high value. It can be noted that the region of interest ROI, the prostate—represented by an interrupted white circle in FIG. 7B—is clearly distinguished with respect to the rest of the body of the patient. The biomarker TAI expresses pixels mainly describing a high tissue activity.

[0074] FIG. 7C shows, in the form of a parametric map PCc, an estimated biomarker, in this case an apparent diffusion coefficient ADC, for each of the same voxels of interest as for FIG. 7B, on the basis of similar experimental diffusion data S(b) but from a mono-exponential model following a method according to the prior art. One and the same colour gradient (from blue to yellow) applied to the values of the biomarker ADC makes it possible to distinguish with greater difficulty the region of interest ROI, in this case the prostate, from the rest of the body. Within said region of interest ROI, the graphical information associated with the biomarker ADC is also more diffuse than in the case of the biomarker TAI.

[0075] FIG. 7D shows, in the form of a parametric map PCd, an estimated biomarker, in this case a pseudodiffusion coefficient D IVIM, for each of the same voxels of interest as for FIG. 7B, on the basis of similar experimental diffusion data S(b) but from a bi-exponential model of the IVIM (intravoxel incoherent motion) type following a method according to the prior art. Like in FIG. 7C, one and the same colour gradient (from blue to yellow) applied to the values of the biomarker D IVIM makes it possible to distinguish with greater difficulty the region of interest ROI, in the case the prostate, from the rest of the body. Within said region of interest ROI, the graphical information associated with the D IVIM biomarker is also more diffuse than in the case of the biomarker TAI and very sensitive to noise, as is demonstrated by the presence of numerous high-value artefacts.

[0076] FIGS. 8A and 8B make it possible to measure even better the benefit conferred by the invention with respect to the known methods. In fact, said FIGS. 8A and 8B show a comparison of the performance on the basis of a contrast-to-noise criterion (also known by the abbreviation CNR). In fact, as shown in FIGS. 7B, 7C and 7D, the TAI (tissue activity indicator) biomarkers according to the invention, ADC (apparent diffusion coefficient) biomarkers of the mono-exponential model or D IVIM (pseudodiffusion coefficient) biomarkers of the bi-exponential model were estimated on the basis of experimental data S(b) taken from the multi-b diffusion imaging sequence without noise reduction process, the acquisition parameter b being comprised between 0 and 1000 s/mm.sup.2. It is usual to compare performance on the basis of the CNR criteria, criteria prevalent in clinical imaging, making it possible to evaluate the detectability of a region of interest with respect to regions adjacent thereto. Such a CNR criterion is thus evaluated according to the following relationship:

[00012]$\mathrm{CNR}=\frac{.Math.{\mu }_{\mathrm{ROI}}-{\mu }_{\mathrm{BKG}}.Math.}{{\sigma }_{\mathrm{BKG}}}$

where μ.sub.ROI and μ.sub.BKG respectively describe average values of the region of interest ROI and of a region BKG adjacent to the aforementioned, σ.sub.BKG being the value of the standard deviation in said adjacent region BKG.

[0077] FIG. 8A is a partial view of a parametric map PCb describing the biomarker TAI and previously mentioned with reference to FIG. 7B making it possible to distinguish a prostate from the rest of the body of a patient. In FIG. 8A, a region of interest ROI is encircled artificially, to ensure understanding of the subject, by a white line. It is focused on the prostate more precisely than the region of interest ROI previously delimited by a circle in an interrupted line in FIGS. 7A to 7D. Three other adjacent regions, referenced respectively BKG1, BKG2 and BKG3, of said region of interest ROI have also been encircled by a white line in said FIG. 8A.

[0078] In order to compare the respective performance of the biomarkers TAI, ADC and D IVIM, a contrast-to-noise ratio CNR was calculated, for each biomarker, between one and the same region of interest ROI and different adjacent regions BKG1, BKG2 and BKG3, that are identical for the three biomarkers. In order to perform this comparison of performance, such a CNR was calculated on the basis of parametric maps PCb, as shown in FIG. 8A, but also of parametric maps PCc and PCd already described with reference to FIGS. 7B and 7D. Thus, for each biomarker, three CNRs were calculated: ROI vs BKG1, ROI vs BKG2 and ROI vs BKG3.

[0079] FIG. 8B shows the CNRs calculated for the three biomarkers TAI, ADC and D IVIM. It is clear that the quantification of the biomarker TAI according to the invention allows a better detectability of the prostate, as evidenced in FIG. 8B. The CNRs calculated on the basis of a parametric map PCb associated with the biomarker TAI are more than twice as high as those obtained on the basis of parametric maps PCc and PCd associated with the known ADC and D IVIM biomarkers, in particular as a result of the level of noise sensitivity that is greater for these two latter biomarkers.

[0080] This comparison makes it possible to emphasize that the invention achieves a method for the rapid quantification of a novel biomarker of tissue activity that is particularly resistant and stable with respect to the noises present in the medical imaging signals with respect to the other biomarkers that are applicable in this context. The invention thus provides a novel biomarker that can be utilized and is pertinent to a large number of applications among which there may be mentioned, non-limitatively, the analysis and/or monitoring of cancers, the evaluation of cerebral vascular accidents.