SYSTEM AND METHOD FOR ESTIMATING A PHYSIOLOGICAL PARAMETER OF AN ELEMENTARY VOLUME
Patent Information
- Application Number
- FR2015055656
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2015-06-19
- Publication Date
- 2025-03-21
- Estimated Expiration
- 2035-06-19
Smart Images
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Figure 00000048_0000 
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Abstract
Description
first and second experimental signals respectively from first and second signal acquisitions. According to a preferred embodiment, the invention provides that the step for estimating a first physiological parameter may consist of an estimation step by a Bayesian method, consisting of estimating said first physiological parameter by calculating its marginalized posterior distribution. Similarly, preferably, the invention provides that the step to estimate a second physiological parameter consists of an estimation step by a Bayesian method, consisting of estimating said second physiological parameter by calculating its marginalized posterior distribution. Advantageously, when the Magnetic Resonance Imaging analysis system includes means for providing feedback to a user of said system, said means for providing feedback cooperating with the processing unit, a method according to the invention may include a subsequent step for triggering feedback of the estimated physiological parameter and / or the first, second physiological parameters. To improve the quality of experimental signals obtained and acquired by Magnetic Resonance, a method according to the invention may further include a preliminary preprocessing step of the first and / or second experimental signals obtained respectively from the first and / or second acquisitions by Magnetic Resonance, said step being arranged to correct said first and / or second experimental signals. According to a second object, the invention relates to a method for producing an estimate of a physiological parameter of a region of interest, said region comprising at least one voxel. According to the invention, said physiological parameter is estimated per voxel by means of a method conforming to the first object of the invention. Advantageously, when the Magnetic Resonance Imaging analysis system includes means for providing feedback to a user of said system, said feedback means cooperating with the processing unit, a method according to the invention may further include a subsequent step for triggering the feedback of the physiological parameter estimate, of the first and / or second physiological parameters for each voxel of the region of interest in the form of a map describing a physiological parameter. In addition or alternatively, when the Magnetic Resonance Imaging analysis system includes means of restitution to a user of said system, said restitution means cooperating with the processing unit, a method according to the invention may further include a subsequent step to generate a weighted image from the values produced of the estimated physiological parameter, the first and second physiological parameters for a predetermined acquisition sequence. According to a third object, the invention relates to a processing unit comprising means for communicating with the outside world and processing means, cooperating with storage means. advantageously, the means for communication are arranged to receive from the outside world first and second experimental signals from the first and / or second acquisitions of signals by Magnetic Resonance and the means for storage include instructions executable or interpretable by the processing means, the interpretation or execution of said instructions by said processing means causes the implementation of a process in accordance with the first object of the invention. To assist a practitioner seeking to establish a diagnosis and make a quick decision, the means for communicating from a processing unit according to the invention can deliver an estimated physiological parameter in a format suitable for rendering means capable of rendering it to a user. According to a fourth object, the invention relates to a Magnetic Resonance Imaging analysis system comprising a processing unit according to the invention and means of restitution capable of returning to a user a physiological parameter according to a process according to the first object of the invention and implemented by said processing unit. Finally, according to a fifth object, the invention relates to a computer program product comprising one or more instructions interpretable or executable by the processing means of a processing unit according to the invention. Said processing unit further comprises storage means, or cooperating with such storage means, said program being loadable into said storage means. Said instructions by said processing means are such that their interpretation or execution causes the implementation of a process conforming to the first object of the invention. Other features and advantages will become clearer upon reading the following description and examining the accompanying figures, including: - Figures 1 and 2, described previously, present two variants of the realization of a medical imaging analysis system, possibly by Magnetic Resonance; - Figures 3A, 3B and 3C, described above, present three examples of maps or weighted images obtained by a Nuclear Magnetic Resonance imaging device according to the State of the Art; - Figure 4, described previously, presents a simplified flowchart of a process according to the State of the Art; - Figure 5 schematically describes a simplified flowchart of a process according to the invention; - Figures 6A, 6B and 6C present three examples of physiological parameter maps, estimated according to a method in accordance with the invention; - Figures 7A, 7B and 7C present three examples of weighted images generated and rendered according to a process in accordance with the invention. Figure 5 schematically illustrates a method 200 for estimating a physiological parameter of an elementary volume—called a voxel—of an organ. As a reminder, a "voxel" is defined as any pixel with a thickness. As previously stated, such a method 200 is advantageously implemented by a processing unit of a Magnetic Resonance Imaging analysis system, such as, by way of non-limiting example, those described in connection with Figures 1 and 2. A method 200 according to the invention advantageously includes a step 230 for estimating said physiological parameter. An example of the implementation of such a process 200 will be advantageously but not exhaustively described later in the document. A method 200 according to the invention also includes a step 221 for estimating a first physiological parameter from first experimental signals obtained from a first signal acquisition. Furthermore, the method also includes a step 222 for estimating a second physiological parameter from second experimental signals obtained from a second signal acquisition. As described previously, such first and second experimental signals can be directly downloaded from a server advantageously arranged to store said first and second signals. Alternatively or in addition, the method 200 according to the invention may include steps 211, 212 for producing first and second experimental signals respectively from first and second signal acquisitions. Thus, such a step 211 may advantageously consist of implementing a The first acquisition of signals from a first acquisition sequence determined to estimate a first physiological parameter. Similarly, a step 212 may advantageously consist of implementing a second acquisition of signals from a second acquisition sequence determined to estimate a second physiological parameter. The selection of the first and / or second acquisition sequences can be performed automatically or manually, during a preliminary step of configuring the implementation of a method 200 according to the invention, for example via the parameters 16 described previously in connection with Figures 1 and 2. According to a preferred but non-limiting embodiment of the invention, such first and second physiological parameters to be estimated may be the physiological parameters T1 or T2, respectively. Furthermore, according to the same embodiment, preferably but not limitingly, the sequences of the first and second signal acquisitions may advantageously consist of two respective sequences of arbitrary T1 and T2 mapping, or even T2* mapping in the case of a gradient echo acquisition sequence. Therefore, by way of non-limiting example, step 211 for producing initial experimental signals from a first signal acquisition may include the use of a gradient echo sequence with different Ot flip angles to estimate the first physiological parameter Tl. Such a sequence is particularly valued because it is very fast and available for any which Magnetic Resonance Imaging analysis system. Thus, step 221 for estimating the first physiological parameter may consist of a calculation step by linearizing an equation relating said first physiological parameter to the first experimental signals S. Alternatively, and preferably but not exclusively, step 221 for estimating the first physiological parameter T1 may consist of an estimation step using a Bayesian method. By way of non-limiting example, said Bayesian method is described in document WO2012049421 filed by OLEA MEDICAL or in document WO2010139895A1, also filed by OLEA MEDICAL. These Bayesian methods may consist of estimating the first physiological parameter by calculating its marginalized posterior distribution. Thus, such Bayesian methods notably increase the accuracy of the estimates and reduce sensitivity to noise. Similarly, by way of non-limiting example, step 212 for producing second experimental signals from a second signal acquisition may include the use of a spin echo sequence with multiple echo values to estimate the second physiological parameter Tl. Like the first signal acquisition sequence, such a second sequence is particularly valued because it is very fast and available for any Magnetic Resonance Imaging analysis system. Furthermore, step 222 for estimating the second physiological parameter may consist of a linearization calculation step of an equation relating said second physiological parameter to the second experimental signals S. Alternatively, and preferably but not exclusively, step 222 for estimating the second physiological parameter T1 may consist of an estimation step using a Bayesian method. Said Bayesian methods may consist of estimating the second physiological parameter by calculating its marginalized posterior distribution. Such a Bayesian method notably increases the accuracy of the estimates and reduces noise sensitivity. Thus, the second acquisition sequence can then be shorter in time while obtaining results qualitatively identical to those obtained with a longer sequence. Alternatively, thanks to the use of a Bayesian method, the spatial resolution of the images, and therefore the noise level, can be significantly improved without degrading the estimates. Furthermore, step 230 for estimating the physiological parameter of a process 200 according to the invention consists of producing the estimated value of said physiological parameter from the respective estimates of the first and second physiological parameters. Preferably, but not limitingly, when said first and second estimated physiological parameters are respectively the physiological parameters T1 or T1, the physiological parameter to be estimated may be the physiological parameter PD. As described in connection with Figures 1 and 2, the Magnetic Resonance Imaging analysis system may include means for providing feedback 5 to a user 6, said means for providing feedback 5 cooperating advantageously with processing unit 4. Such rendering means allow for advantageous graphical, auditory, or other output and may include, for example, a screen or loudspeakers. In this case, a method 200 according to the invention may also include a subsequent step to trigger a rendering of the estimated physiological parameter and / or the first and second physiological parameters in a suitable format.According to the preferred application example in which the first and second physiological parameters are respectively the physiological parameters T1 and T2, the estimated physiological parameter is the physiological parameter PD, such a restitution may consist of a graphical representation in the form of maps of the first, second physiological parameters T1 and T2 and / or the physiological parameter PD or one or more estimated values of the first, second physiological parameters T1 and T2 and / or the physiological parameter PD. Advantageously, but not limitingly, a method 200 according to the invention may also include one or more preprocessing steps for the first and / or second experimental signals obtained respectively from the first and / or second Magnetic Resonance signal acquisitions, said step(s) consisting of correcting said first and / or second experimental signals, in particular by correcting artifacts or applying any other corrective filter. By way of non-limiting examples, such steps may consist of: -Movement correction if the patient does not remain sufficiently still during the acquisition of a sequence. For example, a rigid or non-rigid registration algorithm can be chosen; - co-registration or re-adjustment between the first and second acquisition sequences if the field of view of the latter is modified, or if the The patient moved between the first and second sequences. Such co-registration can advantageously translates into a rigid co-registration algorithm (rigid or non-rigid co-registration) – a noise reduction step in the acquisition of both sequences. For example, such a noise reduction step could advantageously translates into an image convolution smoothing algorithm with a Gaussian kernel; -a step to correct the inhomogeneities of magnetic fields B1 applied within the Magnetic Resonance imaging device which commonly affect experimental Magnetic Resonance signals. The invention further relates to a method 300 for producing an estimate of a physiological parameter of a region of interest. A "region of interest" is understood to mean any region containing at least one voxel. However, a region of interest cannot be limited to a single voxel, but may comprise a plurality of voxels, advantageously selected manually or automatically. According to the invention, said physiological parameter can be estimated for each voxel by means of a method 200 according to the invention, as described above, in particular in relation to figure 5, said method being implemented iteratively for each voxel by the processing means of the processing unit 4. Similar to a method 200 for estimating a physiological parameter of a voxel, a method 200 according to the invention may also include a subsequent step 350 for triggering a restitution of the estimated physiological parameter and / or the first, second physiological parameters in an appropriate format, when the Magnetic Resonance Imaging analysis system includes restitution means 5 to a user 6, said restitution means 5 cooperating advantageously with the processing unit 4.According to the preferred application example in which the first and second physiological parameters are respectively the physiological parameters T1 and T2, and the physiological parameter PD, such a rendering may consist of displaying or printing a graphical representation in the form of maps of the first and second physiological parameters T1 and T2 and / or the physiological parameter PD, or one or more estimated values of the first and second physiological parameters T1 and T2 and / or the physiological parameter PD. Examples of such parameter maps will be described later in connection with Figures 6A, 6B, and 6C. Alternatively or in addition, a method 300 according to the invention for estimating a hemodynamic parameter of a region of interest may further include a subsequent step 340 to generate a weighted image from the values produced of the estimated physiological parameter, the first and second physiological parameters for a predetermined acquisition sequence, when the Magnetic Resonance Imaging analysis system includes restitution means 5 of said system, said restitution means 5 cooperating with the processing unit 4. Such a step 340 makes it possible in particular to obtain valuable information on the physiological parameters and to generate one or more weighted images from any type of acquisition sequence chosen, without requiring the implementation of a new examination, and therefore a new acquisition, which is extremely costly in time and money.By way of non-limiting examples, the first and second physiological parameters and the estimated physiological parameter may advantageously and non-limitingly be the physiological parameters T1, T2, and PD, respectively. Advantageously, the method 300 may include a configuration step (not shown in Figure 5), prior to step 340 for generating a weighted image, to select an acquisition sequence and the associated acquisition parameters, such as, by way of non-limiting examples, the parameters TR, TE, and T1. Such sequence and parameter selection may be performed manually by a user or implemented automatically. Examples of such weighted images will be described later in connection with Figures 7A, 7B, and 7C. Alternatively or in addition, a method 300 according to the invention may advantageously include a step (not shown in Figure 5) for selectively segmenting a tissue based on known theoretical values of said tissue. For example, suppose the T1 and T2 values of white matter are known and are 560130 ms and 7715 ms, respectively. Segmentation based on thresholds of the estimated T1 and T2 values allows the extraction of white matter voxels according to the following equation: whitematter=^voxeli\Tli e [530 ms, 590ms] uT, e[72ms,82ms]| Alternatively or in addition, another example of using said weighted images for segmentation purposes would be to use the estimated values of T1, T2 and PD as input data for a partitioning algorithm such as the k-means algorithm (k-means according to Anglo-Saxon terminology). Let us now describe an example of implementation of a method 200 according to the invention and of which an example is described in Figure 5 for estimating respectively the physiological parameters Tl, T2 and PD and then generating an image or a weighted map from the estimates of the physiological parameters Tl, T2 and PD for a predetermined acquisition sequence of spin echo type or inversion recovery type. Let us first describe steps 212 and 222 of such a process 200 for estimating the second physiological parameter T2. As previously mentioned, step 212 can advantageously consist of the implementation a second signal acquisition from a second acquisition sequence determined to estimate a second physiological parameter. Furthermore, as described previously, such a second acquisition sequence can advantageously be a T2 mapping sequence. Preferably, but not limitingly, said T1 mapping sequence, implemented by processing means of a processing unit 4 of a Magnetic Resonance Imaging analysis system, can advantageously be a multi-echo spin echo sequence. When using such a multi-echo spin echo sequence, the experimental signal at each voxel can be calculated using a decreasing exponential function, such as: 3TE)=^e^T2 with: $ oc PD[1 - e ^1] In principle, step 222 for estimating the second physiological parameter Tl consists of a substep of calculation by linearizing the previous equation using the logarithm of the experimental signal, combined with a substep of linear regression. However, the use of such substeps is not satisfactory, since they present large calculation uncertainties. Preferably, but not exclusively, step 222 for estimating the second physiological parameter Tl may consist of a Bayesian estimation, such as, as previously specified, that described in the document WO2012049421 or that described in document WO2010139895A1. In principle, a model is predefined manually or automatically. Bayes' theorem can then be applied, producing an equation linking the posterior distribution of the parameters of said predefined model to the prior distributions of these same parameters (m - a) - and to the likelihood function, the likelihood function being defined as the probability distribution of the data given the parameters, such that: P(T2, |D) x P(D| P )• P(T2)-P(S,)-P(o) where a is the standard deviation of the noise affecting the data D in a voxel of interest. In our application framework, the data D correspond to the second experimental signals obtained by acquiring a second sequence. Classically, the estimation of any parameter of interest is performed using the marginalized posterior distribution estimation of said parameter of interest. For example, the estimation of the marginalized posterior distribution of the second physiological parameter Tl can be calculated for a voxel of interest by evaluating the relationship: So, by way of non-limiting examples, an estimate of the second physiological parameter Tl can finally be calculated in the form of the maximum a posteriori ( T_ ) = arg max P(T, | D) or the mean of the posterior distribution \T2.P(T2\D)dT2 2 \P^\D)dT2 The said calculations are advantageously implemented by the processing means of a processing unit 4 of a Magnetic Resonance analysis system according to the invention. Previously, in order to estimate the marginalized posterior distribution of the second physiological parameter T2, the process includes sub-steps to calculate, estimate and / or select the prior distributions of these same parameters P^), P(p) and the likelihood function En In the absence of additional information on noise, by applying the Maximum Entropy Theorem, a Gaussian distribution can be chosen to allow the calculation of the likelihood function. Such a choice can be made automatically or manually, during a preliminary configuration step of the implementation of a method 200 according to the invention, for example via the parameters 16 described previously in connection with Figures 1 and 2. The likelihood function in 222 is therefore calculated: nr zq2 / =1 P(D\ T2,^,c )xcNe where N is the number of echo times used to perform the acquisition. The a priori distributions of the parameters, meanwhile, can be chosen, manually or automatically, also during a preliminary step of configuration of the implementation of a process 200 according to the invention, for example via the parameters 16 described above in connection with Figures 1 and 2, such as by way of non-limiting examples: P(T2)^T2 Once the prior distributions of the same parameters pçv, a$) of the model and the likelihood function have been respectively selected and / or chosen, the marginalized posterior distribution of the second physiological parameter Tl for a given voxel can then be produced, such that: wi 2 P(T2\D)X where the sums are calculated over the different TE echo times of the acquisition. From this posterior distribution, an estimate of the second physiological parameter Tl of the voxel of interest can be calculated. Finally, the parameters and o at the voxel of interest can be produced analytically, such as: Thanks to this analytical calculation, the estimation of the parameters and T2 is then optimal and much less sensitive to measurement noise than the methods classically used. Figure 6A presents an estimation map of the second physiological parameter Tl, resulting from an iterative implementation of the 200 process for a plurality of voxels. Let us now describe steps 211 and 221 of a method 200 according to the invention for estimating the first physiological parameter Tl. Several acquisition sequences can be used to estimate the first physiological parameter Tl. As previously stated, step 211 can advantageously consist of implementing a first signal acquisition from a first acquisition sequence determined to estimate a first physiological parameter. Furthermore, as previously described, such a first acquisition sequence can advantageously be a Tl mapping sequence. By way of non-limiting examples, said Tl mapping sequence, implemented by processing means of a processing unit 4 of a Magnetic Resonance Imaging analysis system, can advantageously be an inversion-recovery sequence, a look- locker, or a variable flip-angle sequence. Preferably, but not exclusively, a variable flip-angle sequence (also known by the English abbreviation "VFA") can also be used. This sequence is indeed the fastest of the sequences compared to the previous ones. When using such a variable flip-angle sequence, the experimental signal at each voxel of interest can be expressed as a proportionality relationship, such as: TFT sina 1-e T' Sx Mo jr- 1 - cosa • e T' with Mo oc PDe 75 A classic approach to estimating the parameters Tl and Mo involves performing a linearization calculation of the previous proportionality relation. Noting TR ^10 = ® 1 > 1 ' step consisting of calculating the relation of The following proportionality can then be implemented: Its) sina ■0^é+M0.(l-El0) tan a Such an equation can be solved by the method of least squares, in order to quickly estimate Tl and Mo in each voxel. However, this method of estimating the first physiological parameter Tl is very sensitive to noise. Therefore, preferably, but not exclusively, step 221 for estimating the first physiological parameter Tl may consist of advantageously in a Bayesian estimation, such as, as previously specified, that described in document WO2012049421 or that described in document WO2010139895A1. In principle, a model is predefined manually or automatically. Bayes' theorem can then be applied, producing an equation linking the posterior distribution of the parameters P(M,|D) of said predefined model to the prior distributions of these same parameters P(T,) , P(M0) , P(e) and to the likelihood function M0,<5 ), the likelihood function being defined as the probability distribution of the data given the parameters, such that: P(Tt, M„a\D) x P(D\T„ )• PÇT,)-P(a) where a is the standard deviation of the noise affecting the data D in a voxel of interest. In our application framework, the data D corresponds to the first experimental signals obtained by acquiring said first sequence. Classically, as described previously, the estimation of any parameter of interest is performed using the marginalized posterior distribution estimation of said parameter of interest. The estimation of the marginalized posterior distribution of the first physiological parameter Tl can be advantageously calculated for a voxel of interest such as: PÇT^D) oc j] P(Tp M0,e | D)dM0 of So, by way of non-limiting examples, an estimate of the first physiological parameter Tl can be calculated, for example, in the form of the maximum a posteriori ( = argmaxPOWD) or the average value of the posterior distribution 11 ^T^dT, The said calculations are advantageously implemented by the processing means of a processing unit 4 of a Magnetic Resonance analysis system according to the invention. Previously, in order to estimate the marginalized posterior distribution of the second physiological parameter Tl, the process, more specifically step 221, advantageously includes substeps to calculate, estimate, and / or select the prior distributions of these same parameters (PCTi), P(M0), P(v), and the likelihood function (0). In the absence of additional information on noise, by applying the Maximum Entropy Theorem, a Gaussian distribution can be chosen to allow the calculation of the likelihood function. Such a choice can be made automatically or manually during a preliminary step configuring the implementation of a process 200 according to the invention, for example, via the parameters 16 described previously in connection with Figures 1 and 2. The likelihood function in 221 is therefore calculated: 1-coscc-e 71 where N is the number of tilt angles used to perform the acquisition. The a priori distributions of the model parameters, for their part, can be chosen, manually or automatically, also during a prior configuration step of the implementation of a process 200 according to the invention, for example via the parameters 16 described previously in connection with Figures 1 and 2, such as by way of non-limiting examples: PCM^l P(G) X CT 1 Note: TR sina 1 - e T' 7 (a, T ) = ÿp- 1 - cosa • e T' Once the prior distributions of these same parameters P(T,) , P(M0) , P(g) and the function of likelihood M0,ct) respectively selected and / or chosen, the marginalized posterior distribution of the first physiological parameter Tl for a voxel given can then be produced, such as: Wl 2 (£[f(a,T,)2 ]).£$«)=) where the sums are calculated over the different tilt angles of the acquisition Tl. From this posterior distribution, an estimate of the first physiological parameter The tl of the voxel of interest can be calculated. Finally, the parameters Mo and a at the voxel of interest can be calculated analytically, such as: (Z[%W)])' (nwnfJ-gaW) Thanks to this analytical calculation, the estimation of the parameters Mo and Tl is then optimal and much less sensitive to measurement noise than the methods classically used. Figure 6B presents an estimation map of the first physiological parameter Tl, resulting from an iterative implementation of the 200 process for a plurality of voxels. Thirdly, let us describe step 230 to estimate the physiological parameter PD. In our example, the parameter was estimated using step 222 to estimate the second physiological parameter T2 and the T2 mapping sequence. Such a parameter depends on the parameter The physiological PD parameter is weighted by Tl. Therefore, the parameter can be calculated according to the following proportionality relationship: oc PD 77? 1-e 71 Having an estimate of but also of the first physiological parameter Tl thanks to the Tl sequence Using mapping, the physiological parameter PD can be estimated in 230 at the voxel of interest as follows: {PD) 1 - e where the acquisition parameter TR corresponds to the repetition time of the Tl mapping sequence. The calculations used in step 230 to estimate the physiological parameter PD are advantageously implemented by the processing means of a processing unit 4 of a Magnetic Resonance Imaging (MRI) system according to the invention. Such an estimation of the physiological parameter PD is relative and proportional to the true value of the estimated physiological parameter PD. However, the proportionality factor between the estimate and the true value of PD depends solely on the properties of the MRI device. Thus, such indeterminacy, in the form of a relative value, does not pose a problem for the generation of a weighted image or synthetic MRI map. Figure 6C presents an estimation map of the physiological parameter PD, resulting from an iterative implementation of process 200 for a plurality of voxels. Finally, let us describe the step for generating a weighted image from the estimates of the first and second physiological parameters, T1, T2, and PD, respectively, for a particular acquisition sequence. For the purposes of this invention, a "weighted image" means any T1-weighted image, T2-weighted image, or any inversion- recovery: the invention cannot be limited to the term "weighted". By cleverly combining the acquisition parameters of a Nuclear Magnetic Resonance (NMR) imaging device with pre-estimated physiological parameters, a user of said device can instruct the device to generate T1-, T2-, and PD-weighted images, maps, or image sequences, or to occult and / or mask certain types of tissue from a selected acquisition sequence and associated acquisition parameters. Alternatively, the invention provides that such T1-, T2-, and PD-weighted images, maps, or image sequences can be automatically generated and rendered by an NMR imaging analysis system advantageously comprising a processing unit 4 and rendering means 5 that advantageously cooperate with said processing unit 4. The acquisition sequence and associated acquisition parameters can thus be automatically selected. As non-limiting examples, let us now cite, advantageously but not exhaustively, several sequences that the Magnetic Resonance Imaging analysis system can calculate. According to a first embodiment, for a so-called "classical" spin echo acquisition sequence, the process for estimating and generating may include a step for calculating in each voxe1 i: < 77?^ TE S = PD:- 1 - e 71 / I e Tli where PDi ' ' St ^2 / are the parameter estimates physiological Tl, Tl, PD at voxel i previously produced for each voxel i. From this sequence, the Magnetic Resonance imaging analysis system can generate and reproduce synthetic Tl or T2 weighted images using its processing unit and its reproduction means. Similarly, alternatively, according to a second embodiment, for an inversion-retrieval type acquisition sequence, the process for estimating and generating may include a step for calculating in each voxel i: r _2L St = PDi- l-2e71 / 7R> TE TT + e 11 e 21 The process may then include a step to generate an inversion recovery image. This type of sequence allows for the removal of certain types of tissue, such as fluids. Alternatively, according to a third embodiment, for a saturation-recovery type acquisition sequence, the process for estimating and generating may include a step for calculating in each voxel i: < 77?^ ^ = PD: • l-e71' j Figures 7A, 7B, and 7C present three examples of weighted images generated according to a method of the invention. Respectively, said Figures 7A, 7B, and 7C show synthetic images weighted in T2, weighted in T1, and in inversion recovery, said inversion recovery image highlighting the removal of water. Figure 7A Figure 7A presents a T1-weighted image from a spin echo sequence with an echo time of 120 milliseconds and a repetition time of 1,500 milliseconds. Similarly, Figure 7A presents a T1-weighted image from a spin echo sequence with an echo time of 30 milliseconds and a repetition time of 5,000 milliseconds. Finally, Figure 7C presents a weighted image from an inversion recovery sequence with an echo time of 50 milliseconds, a repetition time of 20,000 milliseconds, and an inversion time of 1,700 milliseconds. Thanks to the new estimates and / or maps presented above, the invention makes it possible to provide a practitioner with a comprehensive set of relevant and consistent information, readily available through the use of a method according to the invention. This availability is made possible by adapting the processing unit 4 as shown in Figures 1 or 2, in that the processing means implement a method for estimating a physiological parameter of a voxel or region of interest, comprising generating the estimated value of said physiological parameter from the respective estimates of the first and second physiological parameters. This implementation is advantageously made possible by loading or storing, within storage means cooperating with said processing means, a product computer program. The latter comprises instructions that can be interpreted and / or executed by said processing means. The interpretation or execution of said instructions triggers the implementation of a process 200 or 300 according to the invention. The means for communicating with the outside world of said processing unit can deliver a physiological parameter, namely the estimated parameters 14, in a format suitable for rendering means capable of rendering it to a user 6, said parameter physiological estimate that could be advantageous presented, for example, in the form of maps or weighted images as illustrated in Figures 6A to 6C and 7A to 7C. Thanks to the invention, the information provided is more numerous, consistent, reproducible, and accurate. The information available to the practitioner is thus likely to increase their confidence and speed in determining a diagnosis and making a decision.
Claims
CLAIMS 1. Method (200) for producing an estimate of a physiological parameter of an elementary volume - called voxel - of an organ, said method (200) being implemented by processing means of a processing unit (4) of a Magnetic Resonance imaging analysis system, and comprising a step (230) for estimating said physiological parameter, said method comprising: - a step (221) for estimating a first physiological parameter from first experimental signals resulting from a first acquisition of signals by Magnetic Resonance, - a step (222) for estimating a second physiological parameter from second experimental signals resulting from a second acquisition of signals by Magnetic Resonance, - - said step (230) for estimating the physiological parameter consisting of producing the estimated value of said physiological parameter from the respective estimates of the first and second physiological parameters, said method (200) being characterized in that the step (221) for estimating a first physiological parameter consists of a step of estimation by a Bayesian method, consisting of estimating said first physiological parameter by calculating its marginalized a posteriori distribution.
2. Method (200) according to the preceding claim, further comprising steps (211, 212) for producing first and second experimental signals respectively from first and second signal acquisitions.
3. Method (200) according to any one of the preceding claims, for which the step (222) for estimating a second physiological parameter consists of a step of estimation by a Bayesian method, consisting of estimating said second physiological parameter by calculating its marginalized a posteriori distribution.
4. Method (200) according to any one of the preceding claims, the Magnetic Resonance imaging analysis system comprising means (5) for restoring the estimated parameter to a user (6) of said system, said restoring means (5) cooperating with the processing unit (4), said method comprising a subsequent step for triggering a restoration of the estimated physiological parameter and / or the first, second physiological parameters.
5. Method (200) according to any one of the preceding claims, further comprising a prior step of preprocessing the first and / or second experimental signals obtained respectively from the first and / or second acquisitions by Magnetic Resonance, said step being arranged to correct said first and / or second experimental signals.
6. Method (300) for producing an estimate of a physiological parameter of a region of interest, said region comprising at least one voxel, said physiological parameter being estimated per voxel by means of a method (200) according to any one of claims 1 to 5.
7. Method (300) according to the preceding claim, when the Magnetic Resonance imaging analysis system comprises means (5) for restitution to a user (6) of said system, said restitution means (5) cooperating with the processing unit (4), further comprising a subsequent step for triggering the restitution of the estimation of the physiological parameter, of the first and / or second physiological parameters for each voxel of the region of interest in the form of a map describing a physiological parameter.
8. Method (300) according to any one of claims 6 or 7, when the Magnetic Resonance imaging analysis system comprises means (5) for restoring to a user (6) of said system, said means of restoring (5) cooperating with the processing unit (4), further comprising a subsequent step (340) for generating an image weighted from the produced values of the estimated physiological parameter, the first and second physiological parameters for a predetermined acquisition sequence. 5 9. Processing unit (4) comprising means for communicating with the outside world and processing means, cooperating with storage means and characterized in that: 10 - the means of communication are arranged for receive signals from the outside world from the first and / or second acquisitions of signals by Magnetic Resonance; - the storage means include 15 instructions executable or interpretable by the processing means whose interpretation or execution of said instructions by said processing means causes the implementation of a method (200,300) according to any one of the 20 claims. 1 to 8.
10. Processing unit (4) according to the preceding claim, for which the means for communicating deliver an estimated physiological parameter according to a format appropriate to restitution means (5). able to return it to a user (6).
11. Magnetic Resonance Imaging Analysis System comprising a processing unit (4) 30 according to any one of claims 9 and 10 and restitution means (5) capable of restoring to a user (6) a physiological parameter according to a method (200,300) in accordance with any one of claims 1 to 8 and implemented by said processing unit (4).
12. Computer program product comprising one or more instructions interpretable or executable by the processing means of a processing unit (4) according to any one of claims 9 or 10, said processing unit (4) further comprising storage means or cooperating with such storage means, said program being loadable into said storage means, characterized in that the interpretation or execution of said instructions by said processing means causes the implementation of a method (200, 300) according to any one of claims 1 to 8.