Method for determining a biological state of a subject based on mr images and associated methods and devices

Abstract

The invention concerns hypoxia, which is a critical medical condition that can have severe health consequences. Early and accurate detection of hypoxia is thus desirable to prevent irreversible tissue damage and improve clinical outcomes. Traditional methods for detecting hypoxia, such as arterial blood gas measurements, use of oxygen sensors or misonidazole positron-emitting tomography, have limitations in terms of accuracy, invasiness, irradiation and cost. That is why the inventors have developed a method to determined a biological state linked to hypoxia, said method being without irradiation.

Description

[0001] METHOD FOR DETERMINING A BIOLOGICAL STATE OF A SUBJECT BASED ON MR IMAGES AND ASSOCIATED METHODS AND DEVICES FIELD OF THE INVENTION

[0002] The present invention is relative to a method for determining a biological state of a subject based on images of a region of interest of a subject. The invention concerns a method for predicting that a subject is at risk of suffering from an hypoxia-induced or hypoxia-inducing disease. The invention also relates to a method for diagnosing an hypoxia-induced or hypoxia-inducing disease. The invention also concerns a method for identifying a therapeutic target for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease. The invention also relates to a method for identifying a biomarker, the biomarker being a diagnostic biomarker of an hypoxia-induced or hypoxia-inducing disease, a prognostic biomarker of an hypoxia-induced or hypoxia-inducing disease or a predictive biomarker in response to the treatment of an hypoxia-induced or hypoxia-inducing disease. The invention also concerns a method for screening a compound useful as a medicine, the compound having an effect on a known therapeutical target, for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease. The invention also concerns a method for monitoring patients enrolled in a clinical trial and a method for controlling a therapy of a subject suffering from an hypoxia-induced or hypoxia-inducing disease.

[0003] The invention also relates to the associated computer program products and a computer readable medium.

[0004] BACKGROUND OF THE INVENTION

[0005] Hypoxia, defined as a reduction in oxygen supply in tissues, is a critical medical condition that can have severe health consequences. It is often associated with various pathologies, including cardiovascular diseases, respiratory disorders, and certain types of cancer. Early and accurate detection of hypoxia is thus desirable to prevent irreversible tissue damage and improve clinical outcomes.

[0006] In the medical context, hypoxia can manifest acutely or chronically, with each form requiring specific diagnostic and therapeutic approaches. Traditional methods for detecting hypoxia, such as arterial blood gas measurements, use of oxygen sensors or misonidazole positron-emitting tomography, have limitations in terms of accuracy, invasiness, irradiation and cost.SUMMARY OF THE INVENTION

[0007] There is therefore a need for a method for determining a biological state of a subject, notably a biological state linked to hypoxia, which is accurate, less invasive and without irradiation.

[0008] To this end, the specification describes a method for determining a biological state of a subject, said method comprising:

[0009] - a phase of obtaining images of a region of interest of the subject, the phase of obtaining comprising a step of:

[0010] - executing instructions read from a computer readable memory with a processor, the processor being in communication with an input device to receive images of a region of interest of the subject, the images being acquired with a magnetic resonance imaging technique,

[0011] - a phase of determining a multi-parametric characterization of the region of interest, to obtain a determined multi-parametric characterization, the phase of determining comprising a step of:

[0012] - executing instructions read from the computer readable memory with the processor, the processor being in communication with the input device, to carry out a first processing of the received images to obtain the relaxation time,

[0013] - executing instructions read from the computer readable memory with the processor, the processor being in communication with the input device, to carry out a second processing of the received images to obtain the transverse relaxation time,

[0014] - executing instructions read from the computer readable memory with the processor, the processor being in communication with the input device, to carry out a third processing of the received images to obtain the proton density fat fraction,

[0015] - executing instructions read from the computer readable memory with the processor, the processor being in communication with the input device, to carry out a fourth processing of the received images to obtain at least one parameter of the intravoxel incoherent motion model,

[0016] - executing instructions read from the computer readable memory with the processor, the processor being in communication with the input device, to carry out a fifth processing of the received images to obtain a map of the quantitative magnetic susceptibility,

[0017] - executing instructions read from the computer readable memory with the processor, the processor being in communication with the input device, to carry out a sixth processing of the received images to obtain the relative oxygen extraction fraction,- a phase of assessing the biological state of the subject, the phase of assessing comprising a step of:

[0018] - executing instructions read from the computer readable memory with the processor to determine the biological state of the subject based on the multi-parametric characterization, and

[0019] - executing instructions read from the computer readable memory with the processor, the processor being in communication with an output device, to output the determined biological state.

[0020] According to further aspects of the method for obtaining, which are advantageous but not compulsory, the method for determining might incorporate one or several of the following features, taken in any technically admissible combination:

[0021] - the biological state of the subject is a tumor stage.

[0022] - the magnetic resonance imaging technique comprises applying a 3 dimensional chemical-shift encoding multi-gradient-echo sequence, 2 dimensional multi-spin-echo sequence and a multi-b-value diffusion-weighted sequence.

[0023] - carrying out the fourth processing of the received images enables to obtain each parameter among the intravoxel incoherent motion model.

[0024] - the phase of determining comprises a step of executing instructions read from the computer readable memory with the processor, the processor being in communication with the input device, to carry out additional processing of the received images to obtain:

[0025] • a map of the external macroscopic magnetic field,

[0026] • internal magnetic field,

[0027] • external magnetic field,

[0028] • apparent diffusion coefficient,

[0029] • blood volume, and

[0030] • venous oxygen saturations.

[0031] - the region of interest belongs to a tumor of the subject.

[0032] The specification also relates to a method for predicting that a subject is at risk of suffering from an hypoxia-induced or hypoxia-inducing related disease, the method for predicting at least comprising the step of:

[0033] - executing instructions read from a computer readable memory with a processor, the processor being in communication with an input device and an output device, to carry out the steps of a method for determining a biological state of a subject as previously described, to obtain a biological state of the subject, and- executing instructions read from the computer readable memory with the processor to predict that the subject is at risk of suffering from the hypoxia-induced or hypoxiainducing disease based on the obtained biological state of the subject.

[0034] The specification also concerns a method for diagnosing an hypoxia-induced or hypoxia-inducing disease, the method for diagnosing at least comprising the step of:

[0035] - executing instructions read from a computer readable memory with a processor, the processor being in communication with an input device and an output device, to carry out the steps of a method for determining a biological state of a subject as previously described, to obtain a biological state of the subject, and

[0036] - executing instructions read from the computer readable memory with the processor to diagnose the hypoxia-induced or hypoxia-inducing disease based on the obtained biological state of the subject.

[0037] The specification also relates to a method for identifying a therapeutic target for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease, the method comprising the steps of:

[0038] - executing instructions read from a computer readable memory with a processor, the processor being in communication with an input device and an output device, to carry out the steps of a method for determining a biological state of a first subject as previously described, to obtain a first biological state and the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease,

[0039] - executing instructions read from the computer readable memory with the processor, to carry out the steps of a method for determining a biological state of a second subject as previously described, to obtain a second biological state and the second subject being a subject not suffering from the hypoxia-induced or hypoxia-inducing disease, - executing instructions read from the computer readable memory with the processor, to select a therapeutic target based on the comparison of the first and second biological states.

[0040] The specification also concerns a method for identifying a biomarker, the biomarker being a diagnostic biomarker of an hypoxia-induced or hypoxia-inducing disease, a prognostic biomarker of an hypoxia-induced or hypoxia-inducing disease or a predictive biomarker in response to the treatment of an hypoxia-induced or hypoxia-inducing disease, the method comprising the steps of:

[0041] - executing instructions read from a computer readable memory with a processor, the processor being in communication with an input device and an output device, to carry out the steps of a method for determining a biological state of a first subject aspreviously described, to obtain a biological first state and the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease,

[0042] - executing instructions read from the computer readable memory with the processor, to carry out the steps of a method for determining a biological state of a second subject as previously described, to obtain a second biological state and the second subject being a subject not suffering from the hypoxia-induced or hypoxia-inducing disease, - executing instructions read from the computer readable memory with the processor, to select a biomarker based on the comparison of the first and second biological states.

[0043] The specification also deals with a method for screening a compound useful as a probiotic, a prebiotic ora medicine, the compound having an effect on a known therapeutical target, for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease, the method comprising the steps of:

[0044] - executing instructions read from a computer readable memory with a processor, the processor being in communication with an input device and an output device, to carry out the steps of a method for determining a biological state of a first subject, to obtain a first biological state, the method for determining being as previously described and the first subject being a subject suffering from the hypoxia-induced or hypoxiainducing disease and having received the compound,

[0045] - executing instructions read from the computer readable memory with the processor (16), to carry out the steps of a method for determining a biological state of a second subject, to obtain a second biological state, the method for determining being as previously described and the second subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease and not having received the compound, and

[0046] - executing instructions read from the computer readable memory with the processor, to select a compound based on the comparison of the first and second biological states.

[0047] The specification also relates to a method for monitoring patients enrolled in a clinical trial to provide a quantitative measure for the therapeutic efficacy of a therapy of an hypoxia-induced or hypoxia-inducing disease which is subject to the clinical trial by executing instructions read from a computer readable memory with a processor, to carry out the steps of a method for determining a state of a subject as previously described.

[0048] The specification also concerns a method for controlling a therapy of a subject suffering from an hypoxia-induced or hypoxia-inducing disease, the method for controlling comprising at least the step of:- executing instructions read from a computer readable memory with a processor to carry out the steps of a method for determining a biological state of a subject as previously described, and

[0049] - executing instructions read from the computer readable memory with the processor to assess the efficiency of the therapy, and optionally to determine a change in the therapy based on the assessed efficiency.

[0050] The specification also relates a computer program product comprising instructions for carrying out the steps of a method as previously described when said computer program product is executed on a suitable computer device.

[0051] The specification also concerns a computer readable medium having encoded thereon a computer program as previously described.

[0052] BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The invention will be better understood on the basis of the following description which is given in correspondence with the annexed figures and as an illustrative example, without restricting the object of the invention. In the annexed figures:

[0054] - figure 1 shows schematically a device for analyzing a region of interest of a subject, - figure 2 is a flowchart illustrating an example of carrying out a method for determining a biological state of a subject,

[0055] - figure 3 is a flowchart illustrating an example of carrying out a specific processing step of the method of figure 2

[0056] - figure 4 illustrates an example of a voxel-wise map for each IVIM parameter in a patient presenting a pelvic leiomyosarcoma,

[0057] - figure 5 shows a voxel-wise map for T2* (ms), T2 (ms) and PDFF (%) (left to right) of a patient presenting a myxoid liposarcoma of the right thigh,

[0058] - figure 6 shows a voxel-wise map for R2’ of a patient presenting a dedifferentiated liposarcoma of the left groinof the right thigh,

[0059] - figure 7 shows a voxel-wise map for rOEF (%) of a patient presenting a dedifferentiated liposarcoma of the left groinof the right thigh, and

[0060] - figure 8 shows a voxel-wise map for quantitative susceptibility mapping (ppm) of a second patient presenting a myxoid liposarcoma of the right thigh.

[0061] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0062] Description of a device for determining a region of interest of a subject

[0063] A device 10 for analyzing a region of interest of a subject is illustrated on figure 1.The device 10 is a device devoted to a clinical use.

[0064] According to another embodiment, the device 10 is adapted for a preclinical use. The region of interest is noted ROI in the remainder of the specification.

[0065] The device 10 comprises a calculator 11, a magnetic resonance imaging system 12 and four servers 13.

[0066] According to the embodiment of figure 1, the calculator 11 is such that the interaction between a computer program product and the calculator 11 enables to carry out a method for determining a biological state S of the subject.

[0067] In this specific example, the subject is a human being.

[0068] More generally, the subject is a living subject and notably an animal.

[0069] For instance, the subject is a mammal, and more specifically a rodent such a mouse. A biological state S encompasses all the biological features, which can be of interest for the user of the method for determining.

[0070] A biological state S is, for instance, a tumor stage.

[0071] The tumor grade and aggressivity is also important biological state, and can be reflected by the presence or absence of peri-tumor edema, hemorrhage, cyst, or tumor density.

[0072] The spatial characterization of tissue is also an important biological state to identify contingents of various aggressivity, using lipid composition, presence or absence of fibrosis or vascularization.

[0073] Other specific examples are given hereinafter.

[0074] The calculator 11 is a computer. In the present case, the calculator 11 is a laptop. More generally, the calculator 11 is a computer or computing system, or similar electronic computing device adapted to manipulate and / or transform data represented as physical, such as electronic, quantities within the computing system's registers and / or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices.

[0075] The calculator 11 comprises an output device 14, an input device 15 and a processor 16.

[0076] The input device 15 is a device enabling the user of the device 10 to input information or command to the device 10.

[0077] In figure 1, the input device 15 is a keyboard. Alternatively, the input device 15 is a pointing device (such as a mouse, a touch pad, a trackball and a digitizing tablet), a voicerecognition device, an eye tracker or a haptic device (motion gestures analysis).The output device 15 is a graphical user interface, which is a display unit adapted to provide information to the user of the device 10.

[0078] In figure 1, the output device 14 is a display screen for visual presentation of output, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor.

[0079] In other embodiments, the output device is a printer, an augmented and / or virtual display unit, a speaker or another sound generating device for audible presentation of output, a unit producing vibrations and / or odors or a unit adapted to produce electrical signal.

[0080] In other words, interaction with the user can also involve other sensory feedback mechanisms, including visual, auditory, or tactile feedback, and inputs can be received through various means, including acoustic, speech, or tactile input.

[0081] In a specific embodiment, the input device 15 and the output device 14 are the same component forming man-machine interfaces, such as an interactive screen.

[0082] A communication device enables unidirectional or bidirectional communication between the components of the device 10.

[0083] More specifically, the communication device enables that the processor 16 be in communication with the input device 15 and with the output device 14.

[0084] For instance, the communication device 36 is a bus communication system or an input / output interface.

[0085] The presence of the communication device 36 enables that, in some embodiments, the components of the device 10 be remote one from another.

[0086] The processor 16 comprises a data-processing unit, memories and a reader. The reader is adapted to read a computer readable medium.

[0087] The computer program product comprises a computer readable medium.

[0088] The computer readable medium is a medium that can be read by the reader of the processor 16. The computer readable medium is a medium suitable for storing electronic instructions, and capable of being coupled to a computer system bus.

[0089] Such computer readable storage medium is, for instance, a disk, a floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs) electrically programmable read-only memories (EPROMs), electrically erasable and programmable read only memories (EEPROMs), magnetic or optical cards, or any other type of media suitable for storing electronic instructions, and capable of being coupled to a computer system bus.

[0090] A computer program is stored in the computer readable storage medium. The computer program comprises one or more stored sequence of program instructions.Such program instructions when run by the processor 16, cause the execution of steps of any method that will be described below.

[0091] For instance, the form of the program instructions is a source code form, a computer executable form or any intermediate forms between a source code and a computer executable form, such as the form resulting from the conversion of the source code via an interpreter, an assembler, a compiler, a linker or a locator. In variant, program instructions are a microcode, firmware instructions, state-setting data, configuration data for integrated circuitry (for instance VHDL) or an object code.

[0092] Program instructions are written in any combination of one or more languages, such as an object oriented programming language (FORTRAN, C++, JAVA, HTML), procedural programming language (language C for instance).

[0093] Alternatively, the program instructions is downloaded from an external source through a network, as it is notably the case for applications. In such case, the computer program product comprises a computer-readable data carrier having stored thereon the program instructions or a data carrier signal having encoded thereon the program instructions.

[0094] In each case, the computer program product comprises instructions, which are loadable into the data-processing unit and adapted to cause execution of steps of any method described below when run by the data-processing unit. According to the embodiments, the execution is entirely or partially achieved either on the device 10, that is a single computer, or in a distributed system among several computers (notably via cloud computing).

[0095] In specific embodiments, the algorithm can be deployed in a computing system comprising a back-end component (e.g., a data server), middleware component (e.g., an application server), or front-end component (e.g., a client computer with a graphical user interface or Web browser) to enable user interaction. These components can interconnect via any form of digital communication network, such as a LAN (local area network) or WAN (wide area network), including the Internet. Such a computing system may consist of clients and servers, typically remote from each other, interacting through a communication network. The client-server relationship is defined by the computer programs running on the respective computers, establishing a client-server relationship.

[0096] The calculator 11 is thus adapted to execute instructions read from a computer readable memory with the processor 16 to carry out steps of any of the methods that we will be described hereinafter.

[0097] For clarity issue, each step consisting in executing instructions read from a computer readable memory with the processor 16 to carry out an action will be simply named step of carrying out said action.The calculator 11 is coupled to the four servers 13

[0098] The four servers are a pulse sequence server 18, a data acquisition server 20, a data processing server 22 and a data store server 23.

[0099] According to the example of figure 1, the data store server 23 is performed by the processor 16 and associated disc drive interface circuitry.

[0100] The remaining three servers 18, 20 and 22 are performed by separate processors mounted in a single enclosure and interconnected using a 64-bit backplane bus. The pulse sequence server 18 employs a commercially available microprocessor and a commercially available quad communication controller. The data acquisition server 20 and data processing server 22 both employ the same commercially available microprocessor and the data processing server 22 further includes one or more array processors based on commercially available parallel vector processors.

[0101] The calculator 11 and each processor for the servers 18, 20 and 22 are connected to a serial communications network. This serial network conveys data that is downloaded to the servers 18, 20 and 22 from the calculator 11. The network conveys tag data that is communicated between the servers 18, 20, 22 and 23 and between the calculator 11. In addition, a high speed data link is provided between the data processing server 22 and the calculator 1 A in order to convey image data to the data store server 23.

[0102] The pulse sequence server 18 functions in response to program elements downloaded from the calculator 11 to operate a gradient system 24 and an RF system 26. Gradient waveforms necessary to perform the prescribed scan are produced and applied to the gradient system 24 which excites gradient coils in an assembly 30 to produce the magnetic field gradients Gx, Gyand Gzused for position encoding nuclear magnetic resonance NMR signals. NMR is a physical property according to which the nuclei of atoms absorb and re-emit electromagnetic energy at a specific resonance frequency in the presence of a magnetic field.

[0103] The gradient coil assembly 30 forms part of a magnet assembly which includes a polarizing magnet 32 and a whole-body RF coil 34.

[0104] RF excitation waveforms are applied to the RF coil 34 by the RF system 26 to perform the prescribed magnetic resonance pulse sequence. Responsive NMR signals detected by the RF coil 34 are received by the RF system 26, amplified, demodulated, filtered and digitized under direction of commands produced by the pulse sequence server 18. The RF system 26 includes an RF transmitter for producing a wide variety of RF pulses used in MR pulse sequences. The RF transmitter is responsive to the scan prescription and direction from the pulse sequence server 18 to produce RF pulses of the desired frequency, phaseand pulse amplitude waveform. The generated RF pulses may be applied to the whole body RF coil 34 or to one or more local coils or coil arrays.

[0105] The RF system 26 also includes one or more RF receiver channels. Each RF receiver channel includes an RF amplifier that amplifies the NMR signal received by the coil to which it is connected and a quadrature detector which detects and digitizes the I and Q quadrature components of the received NMR signal.

[0106] The magnitude of the received NMR signal may thus be determined at any sampled point by the square root of the sum of the squares of the I and Q components:

[0107] M = Vi2+ Q2

[0108] and the phase of the received NMR signal may also be determined by the following equation:

[0109] = tan11 y J

[0110] The pulse sequence server 18 also optionally receives patient data from a physiological acquisition controller 36. The controller 36 receives signals from a number of different sensors connected to the subject, such as electroencephalogram (ECG) signals from electrodes or respiratory signals from a bellows. Such signals are typically used by the pulse sequence server 18 to synchronize, or "gate", the performance of the scan with the subject's respiration or heart beat.

[0111] The pulse sequence server 18 also connects to a scan room interface circuit 38 which receives signals from various sensors associated with the condition of the subject and the magnet system. It is also through the scan room interface circuit 38 that a subject positioning system 40 receives commands to move the subject to desired positions during the scan.

[0112] It should be apparent that the pulse sequence server 18 performs real-time control of magnetic resonance imaging system elements during a scan. As a result, the hardware elements of the pulse sequence server 18 are operated with program instructions that are executed in a timely manner by run-time programs. The description components for a scan prescription are downloaded from the calculator 11 in the form of objects. The pulse sequence server 18 contains programs which receive these objects and converts them to objects that are employed by the run-time programs.

[0113] The digitized NMR signal samples produced by the RF system 26 are received by the data acquisition server 20. The data acquisition server 20 operates in response to description components downloaded from the calculator 11 to receive the real-time NMR data and provide buffer storage such that no data is lost by data overrun. In some scans the data acquisition server 20 does little more than pass the acquired NMR data to the data processor server 22. However, in scans which require information derived from acquiredNMR data to control the further performance of the scan, the data acquisition server 20 is programmed to produce such information and convey it to the pulse sequence server 18. For example, during prescans NMR data is acquired and used to calibrate the pulse sequence performed by the pulse sequence server 18. Also, navigator signals may be acquired during a scan and used to adjust RF or gradient system operating parameters or to control the view order in which k-space is sampled. And, the data acquisition server 20 may be employed to process NMR signals used to detect the arrival of contrast agent in an MRA scan. In all these examples the data acquisition server 20 acquires NMR data and processes it in real-time to produce information which is used to control the scan.

[0114] The data processing server 22 receives NMR data from the data acquisition server 20 and processes it in accordance with description components downloaded from the calculator 11. Such processing may include Fourier transformation of raw k-space NMR data to produce two or three-dimensional images.

[0115] Images reconstructed by the data processing server 22 are conveyed back to the calculator 11 where they are stored. Real-time images are stored in a data base memory cache (not shown) from which they may be output to operator display 14 or a display 42 which is located near the magnet assembly 30 for use by attending physicians. Batch mode images or selected real time images are stored in a host database on disc storage 44. When such images have been reconstructed and transferred to storage, the data processing server 22 notifies the data store server 23 on the calculator 11.

[0116] The magnetic resonance imaging system 12 is adapted to image the region of interest ROI in the liver of the subject by using a magnetic resonance imaging technique, the magnetic resonance imaging technique involving successive echoes of a multiple-gradient echo sequence, to obtain images.

[0117] In the case of a preclinical use, the magnetic resonance imaging system 12 is further adapted to apply a magnetic field whose magnetic field value comprised between 0.35 T and 18 T. It can be noticed here that smaller values such as values comprised between 10 mT and 0.35 T can be used.

[0118] In case of a clinical use, the magnetic resonance imaging system 12 is further adapted to apply a magnetic field whose magnetic field value comprised between 10 mT and 7.0 T.

[0119] According to a specific embodiment, the magnetic resonance imaging system 12 is adapted to apply a magnetic field whose magnetic field value comprised between 1.0 T and 2.0 T.Operation of the device 10

[0120] Operation of the device 10 is now described by illustrating an example of carrying out the method for determining a biological state S of the subject as illustrated by the flowchart of figure 2.

[0121] The images post-processed in the present method are images of the region of interest RO I of a subject.

[0122] In this case, the region of interest ROI is a part of a tumor of a subject.

[0123] As an example, the region of interest ROI belongs to the prostate, the abdomen or the breast of the subject.

[0124] The subject is usually human beings.

[0125] The images are acquired with a magnetic resonance imaging technique.

[0126] The magnetic resonance imaging technique involving successive echoes of a multiple-gradient echo sequence.

[0127] The method for determining comprises three phases, namely a phase of obtaining P1 , a phase of determining P2 and a phase of assessing P3.

[0128] On an example of implementation of the phase of obtaining P1

[0129] The phase of obtaining P1 aims at obtaining images of a region of interest ROI of the tumor of the subject.

[0130] According to the example of figure 2, the phase of obtaining P1 comprises a step ot receiving images of said ROI.

[0131] The images have been acquired with a magnetic resonance imaging technique by the magnetic resonance imaging system 12.

[0132] On an example of implementation of the phase of determining P2

[0133] The phase of determining 12 has the aim of determine a multi-parametric characterization of the region of interest ROI, to obtain a determined multi-parametric characterization.

[0134] A multi-parametric characterization of the region of interest ROI is a set of several parameters, which will be detailed hereinafter.

[0135] In the present case, each of these parameters is obtained by applying a respective processing of the received images (or at least a part of the received images).

[0136] Such processings are generally numerical algorithms enabling to retrieve by an analysis the value of the parameters from the part of the received images.

[0137] Said part is not necessarily the same from one processing to another.In the example of figure 2, there are six processings corresponding to six steps of processing E52, E54, E56, E58, E60 and E62.

[0138] According to this case, these steps of processing E52, E54, E56, E58, E60 and E62 are carried out simultaneously but could be carried out in any appropriate order.

[0139] The first step of processing E52 aims at obtaining the relaxation time T2.

[0140] Such T2-mapping is performed with a multiple echoes-turbo-spin-echo sequence. For instance, the number of echoes n is comprised between 3 and 10, preferably between 5 and 7.

[0141] The interval of times TE between each echo is constant and comprised between 5 ms and 25 ms, preferably between 5 ms and 15 ms.

[0142] For obtaining the T2 map, the processor 16 fits each amplitude signal in a voxel by using a fitting curve.

[0143] In the present case, the fitting curve is an exponential fitting as follows, for each voxel (except for the first echo):

[0144] n*TE

[0145] Sn= Soe T2

[0146] where :

[0147] • Snis the amplitude value of the MRI signal at t = n * TE for said voxel, and • Sois the amplitude value of the MRI signal at t = 0 for said voxel.

[0148] The second step of processing E54 aims at obtaining the transverse relaxation time T2*.

[0149] For this, the processor 16 carries out a chemical-shift encoded multiple echoes-gradient-echo sequence with several echoes.

[0150] The number of echoes is comprised in this case between 3 and 10, preferably between 7 and 9.

[0151] The interval of times TE between each echo is constant and comprised between 1 ms and 5 ms, preferably between 1.5 ms and 3.0 ms. This enables such sequence to be used for other measurements and notably for the third step of processing E56.

[0152] For the fitting of the second processing, a processing derived from the part of the method disclosed in document US 10251599 B2 or US 11403752 B2 may be used. The content of both of these documents is included in the present specification, each processing enabling to obtain one of the searched parameter being herein included by reference.

[0153] The third step of processing E56 aims at obtaining the proton density fat fraction (PDFF).

[0154] The third processing is similar to the one described in document US 10251599 B2 or US 11403752 B2.For instance, the third processing comprises a phase correction algorithm to unwrap and correct the native phase images for zero- and first-order phase and rebuild the Bo-demodulated real part images. Then, using a model of fat1H MR spectrum integrating eight components, the processor 16 estimate PDFF voxel by voxel by a step-wised data fitting procedure on the real part of the corrected signal.

[0155] The fourth step of processing E58 aims at obtaining at least one parameter of the IVIM model.

[0156] IVIM stands for Intravoxel incoherent motion and refer to a model wherein the overall MRI signal attenuation is the sum of the Brownian diffusion of tissue water and the pseudodiffusion imputable to circulating water.

[0157] This leads to a biexponential decay model.

[0158] In the present case, such IVIM parameters are:

[0159] • the pseudo-diffusion coefficient DastThe signal decay of diffusion-weighted imaging induced by the circulating water molecules,

[0160] • the perfusion fraction f The proportion of the signal decay of diffusion- weighted imaging imputable to circulating water molecules, and • the true diffusion coefficient Dsiow'. The signal decay of diffusion-weighted imaging induced by the individual Brownian movement of water molecules. The specific model used here expresses the MRI signal as follows:

[0161] Sb= S0(fe~b Dfast+ (1 - f)e~bDsl°w>)

[0162] Such IVIM parameters are thus obtained by applying a fitting procedure based on this relation.

[0163] The fitting procedure is done using an optimization algorithm based on the constrained-Bayesian inference method.

[0164] The f parameter was first corrected by the T2 value of the blood:

[0165] f_corr = argmin

[0166] X

[0167]

[0168] With:

[0169] • TEdwithe echo time of the diffusion sequence,

[0170] •

[0171]

[0172] the voxel-wise measured T2 and f.

[0173] Then, this corrected f parameter was normalized by the fat fraction to obtain the blood volume (BV):

[0174] BV = (1 — PDFF) * f_corr

[0175] Preferably, all the parameters of the IVIM model are obtained when carrying out the fourth step of processing E58.The fifth step of processing E60 aims at obtaining a map of the magnetic susceptibility. Examples of processing technique which can be used at this step of processing E60 is given in document US 10251599 B2 or US 11403752 B2.

[0176] The sixth step of processing E62 aims at obtaining the relative oxygen extraction fraction rOEF.

[0177] For this, the processor 16 applies the following formula:

[0178]

[0179] Where:

[0180]

[0181] • c is a quantification of the microscopic inhomogeneity contribution to the magnetic field due to the difference between fully oxygenated and fully 4

[0182] deoxygenated haemoglobin and is given here by c = -7ryA HtB0wherein: o y is the gyromagnetic ratio of hydrogen and is equal to 2.675*108s'1T'1o A is the difference between the magnetic susceptibilities (expressed in ppm) of fully oxygenated and fully deoxygenated haemoglobin, o Ht is the haematocrit level, such haematocrit level is taken to 75% of the macrovascular hematocrit for a human subject and to 0.42 for a mouse subject, and

[0183] o Bois the external macroscopic magnetic field.

[0184] In such processes, the magnetic resonance imaging technique MRI comprises applying a 3 dimensional chemical-shift encoding multi-gradient-echo sequence, 2 dimensional multi-spin-echo sequence, and multi-b-value diffusion-weighted imaging.

[0185] In variant, the 3 dimensional chemical-shift encoding multi-gradient-echo sequence is any pulse sequence (or combination of pulse sequences) acquiring single or multiple gradient echoes, leveraging differences in the precession frequencies of fat and water molecules to generate in-phase and opposed-phase images.

[0186] In variant, the 2 dimensional multi-spin-echo sequence is any pulse sequence (or combination of pulse sequences) that applies single or multiple refocusing radiofrequency (RF) pulses following an initial excitation pulse to obtain spin echoes at different echo times.

[0187] In variant, the multi-b-value diffusion-weighted imaging is any pulse sequence (or combination of pulse sequences) that applies single or multiple paired-of to obtain signal at different diffusion weights (b-values).

[0188] Of course, any combination of the previous sequence may be considered here. Additional processings may be considered here.Such additional processings are the following:

[0189] • Additional processing 1: Obtaining Bo map,

[0190] BO map (Bo) is obtained before the fat-water separation step providing PDFF and T2*.

[0191] The multiple echo phase images are temporally unwrapped along the echo times (TEs) by adding multiples of ± 2TT when absolute jumps between consecutive elements of the array are greater than or equal to a jump tolerance of TT radians. From the unwrapped phase images, the zero-order phase (linked to radiofrequency penetration and eddy current effect) and the first-order phase (linked to local heterogeneities in the magnetic field) are extracted voxel-by-voxel. This procedure is achieved with a weighted linear least-square fit accounting for the phase-to-noise ratio difference occurring with the echo time TE and using the spoiled gradient echo sequence model for the MR signal phase cp (TE):

[0192] P(TE) = <p0+ PIT£

[0193] where:

[0194] • cpo accounts for the zero order phase (in rad), and

[0195] • epi for the first order phase (in rad.s-1).

[0196] The BO map is deduced from the extracted first-order phase using:

[0197] Bo = <PI / 2TT.

[0198] • Additional processing 2: Obtaining fat only, water only images and PDFF, T2* maps internal

[0199] From the phase correction step described before, BO-demodulated real part images are computed.

[0200] Such as described in document US 11403752 B2, using a model of a fat1H MR spectrum integrating water resonance and eight fat components, fat proton density (fat image), water proton density (water images) and T2* are derived with a nonlinear least square-constrained fit with a trust-region algorithm.

[0201] Proton density fat fraction (PDFF) is deduced from fat and water proton densities according to:

[0202] 100

[0203]

[0204] where pf and pware fat and water proton densities respectively.• Additional processing 3: Obtaining internal magnetic field and external magnetic field

[0205] Quantitative susceptibility mapping required an initial step of separation between internal (Bint) and external (Bout) magnetic fields.

[0206] In the Bo inhomogeneity field map (ABo), the internal field is linked to the local susceptibility (%) while the external field is linked to external field heterogeneities caused by shims, Bi inhomogeneity or air-tissue interfaces.

[0207] To accurately measure %, one has to distinguish Bint and Bout components from ABo. Such separation can notably be performed with a projection onto dipole field method, as previously described in document US 11403752 B2).

[0208] • Additional processing 4: Obtaining quantitative susceptibility mapping (OSM),

[0209] QSM computation consists in dipole inversion to compute the susceptibility map from the internal field.

[0210] From the integration of the Maxell magnetostatic equations, the internal field (Bint) generated by a nucleus (assumed to be in a small Lorentzian sphere) can be expressed as the convolution product between an arbitrary susceptibility distribution (%) and the field generated by the unit dipole (d) according to:

[0211]

[0212] Where:

[0213] o Ort is the angle between d - r and

[0214] o B^ the magnetic static field intensity.

[0215] In the Fourier domain, this non-local expression becomes a pointwise frequency product:

[0216]

[0217] Where:

[0218] o F corresponds to the Fourier transform,

[0219] o d(k) the dipole kernel in k-space, and

[0220] o k the reciprocal space vector (k = (kx2, ky2,kz2)1 / 2).

[0221] This expression can be rewritten in matrix vector form:

[0222] Bint =Where:

[0223] o C is a sparse Toeplitz matrix representing the convolution kernel Box F(d(k)) and

[0224] o is the susceptibility distribution as a column vector.

[0225] At and near the magic angle (54.7°), k2= 3kz2and the dipole response function includes values equal or near to zero. Given that in k-space, this leads to noise amplification and streaking artefacts, C cannot be directly inverted.

[0226] To overcome this ill-posed problem, a I2 regularization is performed by penalizing the I2 norm of spatial gradients of the susceptibility distribution and introducing priors (W1) derived from magnitude images in the regularization term:

[0227]

[0228] Where:

[0229] o V is the gradient operator,

[0230] o p the regularization term and

[0231] o W1 is a diagonal matrix describing smoothness constraints on the susceptibility distribution chosen from a thresholding from the magnitude image gradient.

[0232] This system is solved using an iterative least square method with a number of iteration and a tolerance set to 1000 and 0.001 respectively.

[0233] • Additional processing 5: Obtaining apparent diffusion coefficient ADC, One preferred method is using diffusion-weighted imaging and considering monoexponential signal decay between a signal So at b=0 and Sb at a defined b value:

[0234]

[0235] • Additional processing 6: Obtaining blood volume BV (corresponding to corrected f)

[0236] One preferred technique is the one described before and implies the application of the following relation:

[0237] BV = (1 — PDFF~) * f_corr• Additional processing 7 obtaining venous oxygen saturations Notably, it is interesting to determine_SvO2 = SaO2 - rOEF by using digital pulse oxymeter to obtain a more accurate value. In this case, SvO2 is a better surrogate of the oxygenation than rOEF.

[0238] More generally, any value obtained by an equation based on rOEF can be considered here, and notably a linear relation.

[0239] Any combination of the previous additional processings may be added to the six processings of the steps of processing E52, E54, E56, E58, E60 and E62.

[0240] One specific interesting combination is the combination of the additional processings'!, 3, 5, 6 and 7.

[0241] More generally, the following combinations of parameters are of interest:

[0242] • Dsiowand rOEF or SvO2,

[0243] • QSM and rOEF or SvO2,

[0244] • T2 and rOEF or SvO2,

[0245] • T2* and rOEF or SvO2,

[0246] • f and rOEF or SvO2,

[0247] • PDFF and rOEF or SvO2,

[0248] • Dslowand f,

[0249] • Dslowand T2,

[0250] • Dstowand QSM,

[0251] • Dslowand PDFF,

[0252] • T2 and QSM,

[0253] • T2 and PDFF, and

[0254] • T2 and T2*.

[0255] In accordance, the determined multi-parametric characterization of the phase of determining P2 at the comprises all the previous parameters, namely Dslow, QSM, T2, T2*, PDFF, f, rOEF and SVO2.

[0256] On an example of implementation of the phase of assessing P3

[0257] The phase of assessing P3 is devoted to assess the biological state S of the subject. According to the example of figure 2, the phase of assessing P3 comprises a step of determining E64 and a step of outputting E66.During the step of determining E64, the processor 16 determines the biological state S of the subject based on the multi-parametric characterization obtained by carrying out the phase of determining P2.

[0258] In the present case, the biological state S is a tumor stage.

[0259] For this, the processor 16 applies a function on the parameters determined at the previous phase.

[0260] Such function is, for instance, an algorithm or a table.

[0261] The choice of the parameters corresponding to steps E52 to E62 is here particularly pertinent in so far as all these parameters are involved in oncology.

[0262] More specifically, the spin-spin irreversible relaxation time T2 is associated with higher grade and poorer prognosis of peri-tumor edema. A long relaxation time T2 for secretions can also distinguish mucinous rectal tumors and adenocarcinoma of the rectum. A long relaxation time T2 for liquid can highlight cystic components of tumors, which can be a prognostic feature. A short relaxation time T2 can also help distinguish fibrosis from viable tumor.

[0263] For the parameter T2*, it can distinguish tumors such as tenosynovial giant cell tumors from surrounding tissues. It is also help for diagnosing and evaluating local invasion of lesions such as ovarian cysts.

[0264] PDFF can distinguish benign, intermediate and malignant tumors (lipomas, atypical lipomatous tumors and liposarcomas, respectively), metastatic from non-metastatic nodes, and metastatic from osteoporotic vertebral fractures.

[0265] The parameter Dsiowis a measurement of water diffusion coefficient linked to Brownian motion and is representative of the density of cells. Such parameter can help tumor diagnosis, and can be correlated with histological grade and clinical stage. It can help distinguish estrogen receptor positive vs negative breast cancer. It can also be an assistance for distinguishing fibrosis (low ADC) from viable or recurrent tumor.

[0266] The parameter Dsiow is a measurement of water diffusion coefficient linked to the microperfusion and is representative of the perfusion-related diffusion. Such parameter can be correlated with major drivers of tumor angiogenesis.

[0267] The parameter f is a measurement of the fraction of microperfusion in a voxel and is representative of the perfusion fraction. Such parameter can be correlated with major drivers of tumor angiogenesis and can be correlated with ductal in situ (non-invasive) carcinoma component in breast cancer.

[0268] The measurement of internal magnetic susceptibility may be representative of haemorrhage and can be used in diagnosis to distinguish histological subtypes of sarcomas. Such measurement is also representative of the iron accumulation and canreflect the state of inflammation. Such measurement may also characterize calcifications and thus be related to tumor prognosis.

[0269] The parameter of relative oxygene extraction fraction f is known to be a surrogate of tissue oxygen supply. Hypoxia (lack of oxygen in tissues) is associated with chemotherapy resistance, radiotherapy resistance, cancer progression and metastasis.

[0270] During the step of outputting E66, the output device 14 outputs the determined biological state S.

[0271] According to the present example, the output device 14 displays said biological state S.

[0272] Advantages of the proposed method

[0273] Such method enables to obtain in an efficient way an assessment of the biological state of the subject S.

[0274] This assessment can be used to predict the aggressiveness of a disease, the prognosis of a patient, or a probability of response to a treatment, as will be further detailed in the following section.

[0275] Applications of the method for determining

[0276] Such determined biological state S of the subject may be used specific applications. Some examples are provided hereinafter for many applications linked to hypoxia-induced or hypoxia-inducing diseases.

[0277] An hypoxia-induced disease is, by definition, a disease, which is notably caused by hypoxia. Diabete, Neuro-degenerative diseases (such a Parkinson’s, Alzheimer’s diseases) and stroke are examples of hypoxia-induced diseases.

[0278] An hypoxia-inducing disease is, by definition, a disease, which induces hypoxia. Cancers and inflammatory autoimmune disease, such as systemic lupus erythematosus and rheumatoid arthritis.

[0279] It can thus be considered to use the predicting method in a method for predicting that a subject is at risk of suffering from an hypoxia-induced or hypoxia-inducing disease. The term “risk” relates to the probability that an event will occur over a specific time period, and can mean a subject's “absolute” risk or “relative” risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed. Oddsratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p / (1 — p) where p is the probability of event and (1-p) is the probability of no event).

[0280] The method for predicting comprises at least the steps of carrying out the steps of the determining method on the subject, to obtain a determined biological state S, and predicting that the subject is at risk of suffering from the hypoxia-induced or hypoxia-inducing disease based on the determined biological state S.

[0281] Alternatively, it can be considered a method for diagnosing an hypoxia-induced or hypoxia-inducing disease wherein the method for diagnosing comprises at least the steps of carrying out the steps of the determining method, to obtain a determined biological state S, and diagnosing the hypoxia-induced or hypoxia-inducing disease based on the determined biological state S.

[0282] The determining method can also be advantageously used in a method for identifying a therapeutic target for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease, the method comprising at least the steps of carrying out the steps of the determining method for a first subject, to obtain a first determined biological state S, the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease, carrying out the steps of the predicting method for a second subject, to obtain a second determined biological state S, the second subject being a subject not suffering from the hypoxia-induced or hypoxia-inducing disease, and selecting a therapeutic target based on the comparison of the first and second determined biological states S.

[0283] Alternatively, it can be considered a method for identifying a biomarker for an hypoxia-induced or hypoxia-inducing disease, the biomarker being a diagnosis biomarker of said disease, a susceptibility biomarker of said disease, a prognostic biomarker of said disease or a predictive biomarker in response to the treatment of said disease, the method comprising at least the steps of carrying out the steps of the determining method for a first subject, to obtain a first predicted biological state S, the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease, carrying out the steps of the determining method for a second subject, to obtain a second determined biological state S, the second subject being a subject not suffering from the hypoxia-induced or hypoxiainducing disease, and selecting a biomarker target based on the comparison of the first and second determined biological states S.

[0284] The determining method can also be advantageously used in a method for screening a compound useful as a medicament, the compound having an effect on a known therapeutical target for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease, the method comprising at least the steps of carrying out the steps of thedetermining method for a first subject, to obtain a first determined biological state S, the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease and having received the compound, carrying out the steps of the predicting method for a second subject, to obtain a second predicted biological state S, the second subject being a subject suffering from the an hypoxia-induced or hypoxia-inducing disease and not having received the compound, and selecting a biomarker target based on the comparison of the first and second determined biological states S.

[0285] The determining method is also advantageous in a method for monitoring patients enrolled in a clinical trial to provide a quantitative measure for the therapeutic efficacy of the therapy, which is subject to the clinical trial by carrying out the steps of the determining method on said patients.

[0286] The determining method is also relevant for a method for controlling a therapy of a subject suffering from an hypoxia-induced or hypoxia-inducing disease, the method for controlling comprising at least the step of carry out the steps of a determining method, and the step of assessing the efficiency of the therapy, and optionally to determine a change in the therapy based on the assessed efficiency. This opens the way to reaching magnetic resonance imaging-guided radiotherapy for cancer.

[0287] More generally, the predicting method can advantageously be used in any context where the biological state S is useful and, even more in the case where such biological state S can only be obtained in an invasive way.

[0288] In addition, the person skilled in the art can consider any combination of the features of the previously mentioned embodiment of a method to obtain new embodiments when the features are technically compatible.

[0289] EXPERIMENTAL SECTION

[0290] Hypoxia plays a central role in tumour chemo- and radioresistance. Reliable tumour hypoxia imaging would allow the monitoring of tumor response and a more personalized adaptation of radiotherapy planning. Here, the Applicant showed a proof of concept of the feasibility of relative oxygen extraction fraction (rOEF) mapping using multi-parametric quantitative MRI (qMRI) achieved for the first time on extra-cranial tumors on a 3.0 T MRI and without any contrast agent. T2, T2* relaxation times maps, intra-voxel incoherent motion (IVIM) and quantitative susceptibility (QSM) parametric maps mapping were computed on 13 patients presenting localized tumors of various locations (Soft-tissue sarcomas of the limbs and trunc). R2’ were calculated based on a multi-parametric model. Median tumor values were highly variable among patients, ranging from 74.8 to 411.9 ms,from 2.6 to 15.2 s’1, from 2.9% to 21.8%, from 76.2% to 95.6% and from -0.057 to 0.053 ppm for T2, R2’, rOEF, SvO2 and QSM maps, as examples. qMRI parameters are measurable on 3.0 T MRI. From T2, T2* and IVIM parameters maps, the Applicant was able to obtain rOEF and SvO2 maps of tumors. These results are the first step to a non-invasive imaging of tumour hypoxia during cancer management leading to a biological image-guided personalized medicine.

[0291] Introduction

[0292] Soft tissue sarcoma are rare and heterogeneous tumors, with more than 100 histological subtypes. Despite poor prognosis and heterogeneous chemo- and radiosensitity, few factors were described as prognostic or predictive to treatment response. Furthermore, most of them necessitate transcriptomic data on tissue samples. This kind of biological biomarkers necessitate additional exam, the results are available only some days after, and the evaluation is performed on a small part of the tumor. On the other hand, quantitative magnetic resonance imaging (qMRI) enables the measurement of various biomarkers to better characterize the whole tumors, with rapid results, and with an exam already performed in standard management. Indeed, these MRI are indicated for work-up, during treatment, for tumor response evaluation and during patient follow-up. IVIM imaging is particularly useful, since it assists for the estimation of diffusion and perfusion effects in a tissue without the injection of an MRI contrast agent. After fitting a bi-exponential model, IVIM parameters such as molecular tissue diffusion coefficient Dsiow, the perfusion fraction f, and the pseudo-diffusion coefficient Dfast are estimated and might provide a predictive information about the tumor response to radiotherapy. Tumor hypoxia is a known factor of prognostic and radioresistance of cancer. However, performing an MR-based hypoxia measures is challenging. A theoretical model to estimate the consumption of oxygen of a tissue with the relative oxygen extraction fraction (rOEF) was developed based on the magnetic properties of haemoglobin (deoxygenated haemoglobin is paramagnetic while oxynated one is diamagnetic). rOEF is measured by MRI based on independent quantification of the transverse relaxation times T2 and T2* and the blood volume (BV). In previous studies on patients with gliomas, rOEF was associated with a higher tumour grade and unfavourable molecular alterations. In these studies, BV was estimated using injection of a contrast agent with dynamic susceptibility contrast (DSC) or dynamic contrastenhancement (DCE) sequences. Nevertheless, these agents induce economic, logistical and ecological burden. Furthermore, they can’t be used in patients with renal dysfunction or be iteratively performed, while most tumors arise in elderly patients and necessitate repeated MRI to monitor the response. Here, the Applicant showed the feasibility of a non-invasive rOEF mapping of tumors using multi-parametric quantitative MRI (qMRI) on a 3.0 T MRI with perfusion measures based on IVIM imaging.

[0293] Methods

[0294] Patients characteristics

[0295] Thirteen patients were prospectively evaluated, presenting various subtypes of sarcomas of limb or trunk, with indication of neoadjuvant radiotherapy. Patients were aged 33 to 78, and mostly presented myxoid liposarcoma (n=9, 69.2%), and tumors located on the thigh (n=8, 61.5%).

[0296] MRI protocols

[0297] MRI acquisition were performed on a 3.0T MRI (Siemens Magnetom VIDA, Siemens Healthcare GmbH, Erlangen, Germany). The study was registered and approved by an ethical review board, and patients provided written informed consent. The MRI protocol was performed using sequences with main acquisition parameters listed in Table 1:

[0298]

[0299] Table 1: Acquisition parameters of each sequence

[0300] The T2-mapping was performed with a multiple echoes-turbo-spin-echo sequence with 10 echoes (ATE = 8 ms). The T2*-mapping was obtained with a chemical-shift encoded multiple echoes-gradient-echo sequence with 10 echoes; ATE was chosen at 1.2 ms to allow proper separation of water and fat signal. For I VI M, the Applicant chose a maximum b-value of 800 s / mm2to maintain reasonable TR- and TE-values and an acceptable signal-to-noise ratio (SNR). Ten b-values were used, with 7 values between 0 and 200 s / mm2to correctly fit perfusion-derived IVIM parameters. The total duration of the acquisition was 13 min 44 sec. To facilitate post-processing registration, all sequences were acquired with identical image location, field of view (FOV) and pixel size. The tumors were manually delineated by an experienced radiation oncologist to build a volume of interest (VOI) andextract interest values for each parametric map. All calculations were performed with Matlab software (The Math Works, Inc. MATLAB.Version 2023. b, Natick, Massachusetts, USA).

[0301] Quantitative parameter calculation

[0302] T2 map were calculated fitting the following equation 1 to the data:

[0303] Sn= Soe T2

[0304] where :

[0305] • Snis the signal value at TEn, and

[0306] • Sois the signal value at TEn= 0.

[0307] To avoid T2 overestimation, corrected T2 were calculated discarding the first echo. The I VI M parameters were calculated by fitting the following equation 2 to the data:

[0308] Sb= S0(fe~b Dfast+ (1 - f)e~b Dslow)

[0309] where :

[0310] • Dfastis the pseudo-diffusion coefficient,

[0311] • f is the perfusion fraction,

[0312] • Dsiowis the true diffusion coefficient, and

[0313] • Sois the signal value at b = 0.

[0314] First, all parameters were estimated using an optimization algorithm based on a constrained Bayesian-inference method.

[0315] The f parameter was first corrected by the T2 value of the blood:

[0316] f corr = argmin X

[0317]

[0318] With:

[0319] • TEdwithe echo time of the diffusion sequence,

[0320] •

[0321]

[0322] the voxel-wise measured T2 and f.

[0323] Then, this corrected f parameter was normalized by the fat fraction to obtain the blood volume (BV):

[0324] BV = (1 — PDFF) * f_corr

[0325] T2*, PDFF and magnetic susceptibility ( z) mapping

[0326] To avoid chemical shift effect, T2* mapping was done simultaneously with fat-water decomposition using the method described in document US 11403752 B2. Briefly, a phase correction algorithm was used to unwrap and correct the native phase images for zero- andfirst-order phase and rebuild the Bo-demodulated real part images. Then, using a model of fat1H MR spectrum integrating eight components, T2* and PDFF was estimated voxel by voxel by a step-wised data fitting procedure on the real part of the corrected signal.

[0327] From the phase correction step an off-resonance Bo field map was measured. From this map, the internal field linked to local magnetic susceptibility effect and the external field linked to the macroscopic field heterogeneities was first separated and the local magnetic susceptibility map was derived using a regularized dipole inversion such as described in document US 11403752 B2.

[0328] Multi-parametric oxygenation map

[0329] The Applicant has adapted a method initially described for brain MRI, to map the tissue relative oxygen extraction fraction. Briefly, the Applicant calculated the reversible transverse relaxation coefficient R2' =corresPor|ding to the measure of macroscopic

[0330]

[0331] inhomogeneity variation of magnetic field including the susceptibility effect on MR signal induced by variation of blood level of oxyhemoglobin and deoxyhemoglobin. This can be written mathematically as the following equation 3:

[0332] R2' = R2* — R2

[0333] where R2* = T2*

[0334]

[0335] =2andarethus respectively derived from T2 and T2* maps.

[0336] R2’ is also related to the haematocrit level (Ht), the difference between the magnetic susceptibilities (expressed in ppm) of fully oxygenated and fully deoxygenated haemoglobin (A ), the tissue consumption in oxygen (relative oxygen extraction fraction =rOEF), Bo, the gyromagnetic ratio of hydrogen y and the blood volume (BV) in the microcapillary compartment.

[0337] The Applicant used the IVIM parameter f to estimate BV. The Applicant considered fixed values A (0.264 xW6CGS), Bo(3.0 T) and y (2.67502 xio8rad / s / T). To obtain the individual microcapillary Ht, the Applicant multiplied the obtained macrovascular Ht of a recent total blood count by 85%.

[0338] Then, the Applicant estimated relative tissue oxygenation by applying the following equation 4:

[0339]

[0340] where c is a global constant taking into account the different variables set as constants in the model (Bo, A and y), defined as:

[0341] &

[0342]

[0343] Finally, an oxygen venous saturation map was calculated by applying the following equation 5:

[0344] Sv02 = Sa02 — rOEF

[0345] The Applicant used a digital pulsed oximeter before MR exam to estimate the individual oxygen arterial saturation (SaO2).

[0346] Results

[0347] Figures 4 and 5 show the quantitative MRI parameters for various subjects. Median and interquartile range of each parameter are detailed in the Table 2. As an example, individual tumor median values of T2 ranged from 74.8 to 411.9 ms. Based on the T2 and T2* values, the Applicant obtained R2’ maps (Figure 6) with individual tumor median values ranging from 2.6 to 15.2 s’1. The Applicant obtained multi-parametric maps of rOEF and Sv02 (Figure 7), with individual tumor median values ranging from 2.9% to 21.8% and from 76.2% to 95.6%, respectively. The Applicant also obtained QSM maps (Figure 8), with individual tumor median values ranging from -0.057 to 0.053 ppm.

[0348]

[0349] Table 2: Patients and tumor characteristics, and multiparametric quantitative magnetic resonance measures of tumors

[0350] Abbreviations:

[0351] • ADC, apparent diffusion coefficient;

[0352] • BV, corrected and normalized blood volume;• DLPS, dedifferentiated liposarcoma; Leio, leiomyosarcoma;

[0353] • MLS, myxoid liposarcoma;

[0354] • rOEF, relative oxygen extraction fraction;

[0355] • SFT, solitary fibrous tumor;

[0356] • SvO2, oxygen venous saturation;

[0357] • QSM, quantitative susceptibility mapping

[0358] Discussion

[0359] In this study, the Applicant showed that incorporating non-invasive QSM and rOEF mapping of tumors on a 3.0 T MRI based on the T2, T2* and I VI M parameters measure was feasible with an acceptable clinical examination time and without contrast agent injection. These tumor qMRI measures are the first available in literature but are consistent with usual biological data, reported in prostate or muscles. Furthermore, the Applicant’s values of rOEF are consistent with previous published values achieved using other methods like BOLD MRI, and consistent with physiologically expected measures. The measure of changes in qMRI parameters during cancer treatment might be useful to predict and monitor the treatment response. Furthermore, by identifying more aggressive tumor subregion, the dose of radiotherapy could be spatially adapted.

[0360] One of the innovative approaches in this study is the use of f based on IVIM model, corrected by the T2 effect of the blood and normalized by the PDFF, to calculate the BV. In previous studies performed on brain tumours, the BV was usually estimated with the measure of a semi-quantitative parameter after injection of a MRI contrast agent (as gadolinium): the relative cerebral blood volume (rCBV).

[0361] This study had several limitations. The rOEF model did not consider potential confounding factors that could influence R2’ or the haemoglobin dissociation curve (as variations of temperature, Ht, pH, lactate level) and then modify rOEF value. Also, the Applicant has not correlated the rOEF map to a histological confirmation or to another validated hypoxia-imaging technique (as18F-MISO PET). Such studies, despite their challenging design, remain expected to confirm these findings and to better understand the underlying mechanisms.

[0362] In conclusion, from T2, T2* and IVIM parameters maps of the umor, the Applicant showed that rOEF mapping was feasible on 3.0T MRI. qMRI parameters are achievable in a clinically acceptable time on this system without contrast agent injection. These promising results could lead to a non-invasive image of tumor hypoxia and could be the first step to a biological image-guided adaptive radiotherapy.

Claims

CLAIMS1.- A method for determining a biological state (S) of a subject, said method comprising:- a phase of obtaining images of a region of interest (ROI) of the subject, the phase of obtaining comprising a step of:- executing instructions read from a computer readable memory with a processor (16), the processor (16) being in communication with an input device (15) to receive images of a region of interest (ROI) of the subject, the images being acquired with a magnetic resonance imaging technique,- a phase of determining a multi-parametric characterization of the region of interest (ROI), to obtain a determined multi-parametric characterization, the phase of determining comprising a step of:- executing instructions read from the computer readable memory with the processor (16), the processor (16) being in communication with the input device (15), to carry out a first processing of the received images to obtain the relaxation time (T2), - executing instructions read from the computer readable memory with the processor (16), the processor (16) being in communication with the input device (15), to carry out a second processing of the received images to obtain the transverse relaxation time (T2*),- executing instructions read from the computer readable memory with the processor (16), the processor (16) being in communication with the input device (15), to carry out a third processing of the received images to obtain the proton density fat fraction (PDFF),- executing instructions read from the computer readable memory with the processor (16), the processor (16) being in communication with the input device (15), to carry out a fourth processing of the received images to obtain at least one parameter of the intravoxel incoherent motion model,- executing instructions read from the computer readable memory with the processor (16), the processor (16) being in communication with the input device (15), to carry out a fifth processing of the received images to obtain a map of the quantitative magnetic susceptibility,- executing instructions read from the computer readable memory with the processor (16), the processor (16) being in communication with the input device (15), to carry out a sixth processing of the received images to obtain the relative oxygen extraction fraction (rOEF),- a phase of assessing the biological state (S) of the subject, the phase of assessing comprising a step of:- executing instructions read from the computer readable memory with the processor (16) to determine the biological state (S) of the subject based on the multi-parametric characterization, and- executing instructions read from the computer readable memory with the processor, the processor (16) being in communication with an output device (14), to output the determined biological state (S).2.- The method for determining according to claim 1, wherein the biological state (S) of the subject is a tumor stage.3.- The method for determining according to any one of the previous claims, wherein the magnetic resonance imaging technique comprises applying a 3 dimensional chemicalshift encoding multi-gradient-echo sequence, 2 dimensional multi-spin-echo sequence and a multi-b-value diffusion-weighted sequence.4.- The method for determining according to any one of the claims 1 to 3, wherein carrying out the fourth processing of the received images enables to obtain each parameter among the intravoxel incoherent motion model.5.- The method for determining according to any one of the claims 1 to 4, wherein the phase of determining comprises a step of executing instructions read from the computer readable memory with the processor (16), the processor (16) being in communication with the input device (15), to carry out additional processing of the received images to obtain:• a map of the external macroscopic magnetic field,• internal magnetic field,• external magnetic field,• apparent diffusion coefficient,• blood volume, and• venous oxygen saturations.6.- The method for determining according to any one of the claims 1 to 5, wherein the region of interest (ROI) belongs to a tumor of the subject.7.- A method for predicting that a subject is at risk of suffering from an hypoxia-induced or hypoxia-inducing related disease, the method for predicting at least comprising the step of:- executing instructions read from a computer readable memory with a processor (16), the processor (16) being in communication with an input device (15) and an output device (14), to carry out the steps of a method for determining a biological state (S) of a subject, to obtain a biological state (S) of the subject, the method for determining being according to any one of claims 1 to 6, and- executing instructions read from the computer readable memory with the processor (16) to predict that the subject is at risk of suffering from the hypoxia-induced or hypoxia-inducing disease based on the obtained biological state (S) of the subject.8.- A method for diagnosing an hypoxia-induced or hypoxia-inducing disease, the method for diagnosing at least comprising the step of:- executing instructions read from a computer readable memory with a processor (16), the processor (16) being in communication with an input device (15) and an output device (14), to carry out the steps of a method for determining a biological state (S) of a subject, to obtain a biological state (S) of the subject, the method for determining being according to any one of claims 1 to 6, and- executing instructions read from the computer readable memory with the processor (16) to diagnose the hypoxia-induced or hypoxia-inducing disease based on the obtained biological state (S) of the subject.9.- A method for identifying a therapeutic target for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease, the method comprising the steps of:- executing instructions read from a computer readable memory with a processor (16), the processor (16) being in communication with an input device (15) and an output device (14), to carry out the steps of a method for determining a biological state (S) of a first subject, to obtain a first biological state (S), the method for determining being according to any one of claims 1 to 6 and the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease,- executing instructions read from the computer readable memory with the processor (16), to carry out the steps of a method for determining a biological state (S) of a second subject, to obtain a second biological state (S), the method for determining being according to any one of claims 1 to 6 and the second subject being a subject not suffering from the hypoxia-induced or hypoxia-inducing disease,- executing instructions read from the computer readable memory with the processor (16), to select a therapeutic target based on the comparison of the first and second biological states (S).10.- A method for identifying a biomarker, the biomarker being a diagnostic biomarker of an hypoxia-induced or hypoxia-inducing disease, a prognostic biomarker of an hypoxia-induced or hypoxia-inducing disease or a predictive biomarker in response to the treatment of an hypoxia-induced or hypoxia-inducing disease, the method comprising the steps of:- executing instructions read from a computer readable memory with a processor (16), the processor (16) being in communication with an input device (15) and an output device (14), to carry out the steps of a method for determining a biological state (S) of a first subject, to obtain a biological first state, the method for determining being according to any one of claims 1 to 6 and the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease,- executing instructions read from the computer readable memory with the processor (16), to carry out the steps of a method for determining a biological state (S) of a second subject, to obtain a second biological state (S), the method for determining being according to any one of claims 1 to 6 and the second subject being a subject not suffering from the hypoxia-induced or hypoxia-inducing disease,- executing instructions read from the computer readable memory with the processor (16), to select a biomarker based on the comparison of the first and second biological states (S).11.- A method for screening a compound useful as a probiotic, a prebiotic or a medicine, the compound having an effect on a known therapeutical target, for preventing and / or treating an hypoxia-induced or hypoxia-inducing disease, the method comprising the steps of:- executing instructions read from a computer readable memory with a processor (16), the processor (16) being in communication with an input device (15) and an output device (14), to carry out the steps of a method for determining a biological state (S) of a first subject, to obtain a first biological state (S), the method for determining being according to any one of claims 1 to 6 and the first subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease and having received the compound,- executing instructions read from the computer readable memory with the processor (16), to carry out the steps of a method for determining a biological state (S) of asecond subject, to obtain a second biological state (S), the method for determining being according to any one of claims 1 to 6 and the second subject being a subject suffering from the hypoxia-induced or hypoxia-inducing disease and not having received the compound, and- executing instructions read from the computer readable memory with the processor (16), to select a compound based on the comparison of the first and second biological states (S).12.- A method for monitoring patients enrolled in a clinical trial to provide a quantitative measure for the therapeutic efficacy of a therapy of an hypoxia-induced or hypoxia-inducing disease which is subject to the clinical trial by executing instructions read from a computer readable memory with a processor (16), to carry out the steps of a method for determining a state of a subject, the method for determining being according to any one of claims 1 to 6.13.- A method for controlling a therapy of a subject suffering from an hypoxia-induced or hypoxia-inducing disease, the method for controlling comprising at least the step of:- executing instructions read from a computer readable memory with a processor (16) to carry out the steps of a method for determining a biological state (S) of a subject, the method for determining being according to any one of claims 1 to 6, and- executing instructions read from the computer readable memory with the processor (16) to assess the efficiency of the therapy, and optionally to determine a change in the therapy based on the assessed efficiency.14.- A computer program product comprising instructions for carrying out the steps of a method according to any one of claims 1 to 13 when said computer program product is executed on a suitable computer device.15.- A computer readable medium having encoded thereon a computer program according to claim 14.

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