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EP-4584603-B1 - ACCELERATION OF LIVER MRI EXAMINATIONS

EP4584603B1EP 4584603 B1EP4584603 B1EP 4584603B1EP-4584603-B1

Inventors

  • PIETSCH, HUBERTUS
  • FORSTING, MICHAEL
  • JOST, GREGOR
  • KNOBLOCH, Gesine
  • SCHÜTZ, Gunnar
  • KREIS, Felix, Karl
  • LIENERTH, Christian
  • HAUBOLD, Johannes
  • HOSCH, René
  • Nensa, Felix

Dates

Publication Date
20260506
Application Date
20230829

Claims (15)

  1. Computer-implemented method, comprising: • providing a trained machine learning model (MLM t ), wherein the trained machine learning model (MLM t ) was trained on the basis of training data (TD) to generate a predicted second MRI image (MRI 2* ) on the basis of at least one first MRI image (MRI 1 ) and optionally at least one native MRI image (MRI 0 ), wherein the training data (TD) comprise input data (I) and target data (T) for each examination object of a plurality of examination objects, wherein the input data (I) comprise MRI images (MRI 0 , MRI 1 ), wherein the MRI images (MRI 0 , MRI 1 ) are restricted to: ∘ at least one first MRI image (MRI 1 ), wherein the at least one first MRI image (MRI 1 ) represents a liver or a part of the liver of the examination object at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, and ∘ optionally at least one native MRI image (MRI 0 ) of the liver or the part of the liver of the examination object without contrast agent, wherein the target data (T) comprise a second MRI image (MRI 2 ), wherein the second MRI image (MRI 2 ) represents the liver or the part of the liver of the examination object at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • receiving patient data (PD), wherein the patient data (PD) comprise at least one MRI image (MRI P 0 , MRI P 1 ), wherein the at least one MRI image (MRI P 0 , MR1 P 1 ) is restricted to at least one first MRI image (MRI P 1 ) and optionally at least one native MRI image (MRI P 0 ), wherein the at least one first MRI image (MRI P 1 ) represents a liver or a part of the liver of a patient at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, wherein the optional at least one native MRI image (MRI P 0 ) represents the liver or the part of the liver of the patient without contrast agent, • inputting the patient data (PD) into the trained machine learning model (MLM t ), • receiving a predicted MRI image (MRI P 2* ) from the trained machine learning model (MLM t ), wherein the predicted MRI image (MRI P 2* ) represents the liver or the part of the liver of the patient at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • outputting and/or storing the predicted MRI image (MRI P 2* ) and/or transmitting the predicted MRI image (MRI P 2* ) to a separate computer system.
  2. Method according to Claim 1, wherein the training of the machine learning model (MLM t ) comprises: • receiving and/or providing the training data (TD), • training the machine learning model (MLM), wherein the machine learning model (MLM) is configured to generate a predicted second MRI image (MRI 2* ) on the basis of at least one first MRI image (MRI 1 ), optionally at least one native MRI image (MRI 0 ), and model parameters (MP), wherein the training comprises, for each examination object of the plurality of examination objects: ∘ inputting the input data (I) into the machine learning model (MLM), ∘ receiving a predicted second MRI image (MRI 2* ) from the machine learning model (MLM), ∘ calculating a deviation between the second MRI image (MRI 2 ) and the predicted second MRI image (MRI 2* ), ∘ modifying the model parameters (MP) with regard to reducing the deviation, • storing and/or outputting the trained machine learning model (MLM t ) and/or transmitting the trained machine learning model (MLM t ) to a separate computer system and/or using the trained machine learning model (MLM t ) for prediction.
  3. Method according to Claim 1 or 2, wherein the examination object is a human.
  4. Method according to any one of Claims 1 to 3, wherein the at least one first MRI image (MRI P 1 , MRI 1 ) represents the liver or the part of the liver of the patient/examination object 3 to 6 minutes after the administration of the contrast agent.
  5. Method according to any one of Claims 1 to 4, wherein the at least one first MRI image (MRI P 1 , MRI 1 ) is at least one T1-weighted MRI image or comprises such an image.
  6. Method according to any one of Claims 1 to 5, wherein the at least one first MRI image (MRI P 1 , MRI 1 ) is the result of a Dixon sequence, preferably a T1-weighted Dixon sequence.
  7. Method according to any one of Claims 1 to 6, wherein the at least one first MRI image (MRI P 1 , MRI 1 ) comprises an in-phase image and/or an opposed-phase image and/or a fat-only image and/or a water-only image of the liver or the part of the liver.
  8. Method according to any one of Claims 1 to 7, wherein the input data (I) and/or patient data (PD) comprise at least one native MRI image (MRI 0 , MRI P 0 ), wherein the at least one native MRI image (MRI 0 , MRI P 0 ) is the result of a Dixon sequence, preferably a T1-weighted Dixon sequence, preferably using in- and opposed-phase recording technology.
  9. Method according to any one of Claims 1 to 8, wherein the second MRI image (MRI P 2 , MRI 2 ) is a representation of the liver at a point in time in the range of 10 minutes to 30 minutes, preferably 15 minutes to 25 minutes, after the administration of the hepatobiliary contrast agent.
  10. Method according to any one of Claims 1 to 9, wherein the second MRI image (MRI P 2 , MRI 2 ) is a T1-weighted MRI image, preferably a water-only image.
  11. Method according to any one of Claims 1 to 10, wherein the contrast agent comprises: - the disodium salt of gadoxetic acid, - gadolinium 2,2',2"-(10-{1-carboxy-2-[2-(4-ethoxyphenyl)ethoxy]ethyl}-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)triacetate, - gadolinium 2,2',2"-{10-[1-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}triacetate, - gadolinium 2,2',2"-{10-[(1R)-1-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-1,4,7,10- - gadolinium (2S,2'S,2''S)-2,2',2''-{10-[(1S)-1-carboxy-4-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}butyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}tris(3-hydroxypropanoate), or - gadolinium 2,2',2"-{10-[(1S)-4-(4-butoxyphenyl)-1-carboxybutyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triylltriacetate.
  12. Computer system (10), comprising • an input unit (11), • a control and calculation unit (12), and • an output unit (13), wherein the control and calculation unit (12) is configured • to cause the input unit (11) to receive patient data (PD), wherein the patient data (PD) comprise at least one MRI image (MRI P 0 , MRI P 1 ), wherein the at least one MRI image (MRI P 0 , MR1 P 1 ) is restricted to at least one first MRI image (MRI P 1 ) and optionally at least one native MRI image (MRI P 0 ), wherein the at least one first MRI image (MRI P 1 ) represents a liver or a part of the liver of a patient at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, wherein the optional at least one native MRI image (MRI P 0 ) represents the liver or the part of the liver of the patient without contrast agent, • to input the patient data (PD) into a trained machine learning model (MLM t ), wherein the trained machine learning model (MLM t ) was trained on the basis of training data (TD) to generate a predicted second MRI image (MRI 2* ) on the basis of at least one first MRI image (MRI 1 ) and optionally at least one native MRI image (MRI 0 ), wherein the training data (TD) comprise input data (I) and target data (T) for each examination object of a plurality of examination objects, wherein the input data (I) comprise MRI images (MRI 0 , MRI 1 ), wherein the MRI images (MRI 0 , MRI 1 ) are restricted to: ∘ at least one first MRI image (MRI 1 ), wherein the at least one first MRI image (MRI 1 ) represents a liver or a part of the liver of the examination object at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, and ∘ optionally at least one native MRI image (MRI 0 ) of the liver or the part of the liver of the examination object without contrast agent, wherein the target data (T) comprise a second MRI image (MRI 2 ), wherein the second MRI image (MRI 2 ) represents the liver or the part of the liver of the examination object at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • to receive a predicted MRI image (MRI P 2* ) from the trained machine learning model (MLM t ), wherein the predicted MRI image (MRI P 2* ) represents the liver or the part of the liver of the patient at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • to cause the output unit (13) to output the predicted MRI image (MRI P 2* ) and/or to store it and/or to transmit it to a separate computer system.
  13. Computer program product comprising a data memory in which a computer program (60) is stored that can be loaded into a working memory (50) of a computer system (10), where it causes the computer system (10) to execute the following steps: • receiving patient data (PD), wherein the patient data (PD) comprise at least one MRI image (MRI P 0 , MRI P 1 ), wherein the at least one MRI image (MRI P 0 , MRI P 1 ) is restricted to at least one first MRI image (MRI P 1 ) and optionally at least one native MRI image (MRI P 0 ), wherein the at least one first MRI image (MRI P 1 ) represents a liver or a part of the liver of a patient at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, wherein the optional at least one native MRI image (MRI P 0 ) represents the liver or the part of the liver of the patient without contrast agent, • inputting the patient data (PD) into a trained machine learning model (MLM t ), wherein the machine learning model (MLM t ) was trained on the basis of training data (TD) to generate a predicted second MRI image (MRI 2* ) on the basis of at least one first MRI image (MRI 1 ) and optionally at least one native MRI image (MRI 0 ), wherein the training data (TD) comprise input data (I) and target data (T) for each examination object of a plurality of examination objects, wherein the input data (I) comprise MRI images (MRI 0 , MRI 1 ), wherein the MRI images (MRI 0 , MRI 1 ) are restricted to: ∘ at least one first MRI image (MRI 1 ), wherein the at least one first MRI image (MRI 1 ) represents a liver or a part of the liver of the examination object at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, and ∘ optionally at least one native MRI image (MRI 0 ) of the liver or the part of the liver of the examination object without contrast agent, wherein the target data (T) comprise a second MRI image (MRI 2 ), wherein the second MRI image (MRI 2 ) represents the liver or the part of the liver of the examination object at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • receiving a predicted MRI image (MRI P 2* ) from the trained machine learning model (MLM t ), wherein the predicted MRI image (MRI P 2* ) represents the liver or the part of the liver of the patient at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • outputting and/or storing the predicted MRI image (MRI P 2* ) and/or transmitting the predicted MRI image (MRI P 2* ) to a separate computer system.
  14. Use of a hepatobiliary contrast agent in an MRI examination method, comprising: • receiving and/or generating patient data (PD), wherein the patient data (PD) comprise at least one MRI image (MRI P 0 , MRI P 1 ), wherein the at least one MRI image (MRI P 0 , MRI P 1 ) is restricted to at least one first MRI image (MRI P 1 ) and optionally at least one native MRI image (MRI P 0 ), wherein the at least one first MRI image (MRI P 1 ) represents a liver or a part of the liver of a patient at a point in time in the transitional phase after an administration of the hepatobiliary contrast agent, wherein the optional at least one native MRI image (MRI P 0 ) represents the liver or the part of the liver of the patient without contrast agent, • inputting the patient data (PD) into a trained machine learning model (MLM t ), wherein the machine learning model (MLM t ) was trained on the basis of training data (TD) to generate a predicted second MRI image (MRI 2 *) on the basis of at least one first MRI image (MRI 1 ) and optionally at least one native MRI image (MRI 0 ), wherein the training data (TD) comprise input data (I) and target data (T) for each examination object of a plurality of examination objects, wherein the input data (I) comprise MRI images (MRI 0 , MRI 1 ), wherein the MRI images (MRI 0 , MRI 1 ) are restricted to: ∘ at least one first MRI image (MRI 1 ), wherein the at least one first MRI image (MRI 1 ) represents a liver or a part of the liver of the examination object at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, and ∘ optionally at least one native MRI image (MRI 0 ) of the liver or the part of the liver of the examination object without contrast agent, wherein the target data (T) comprise a second MRI image (MRI 2 ), wherein the second MRI image (MRI 2 ) represents the liver or the part of the liver of the examination object at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • receiving a predicted MRI image (MRI P 2* ) from the trained machine learning model (MLM t ), wherein the predicted MRI image (MRI P 2 *) represents the liver or the part of the liver of the patient at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • outputting and/or storing the predicted MRI image (MRI P 2* ) and/or transmitting the predicted MRI image (MRI P 2* ) to a separate computer system.
  15. Kit comprising a hepatobiliary contrast agent and a computer program product, wherein the computer program product comprises a data memory in which a computer program (60) is stored that can be loaded into a working memory (50) of a computer system (10), where it causes the computer system (10) to execute the following steps: • receiving patient data (PD), wherein the patient data (PD) comprise at least one MRI image (MRI P 0 , MRI P 1 ), wherein the at least one MRI image (MRI P 0 , MR1 P 1 ) is restricted to at least one first MRI image (MRI P 1 ) and optionally at least one native MRI image (MRI P 0 ), wherein the at least one first MRI image (MRI P 1 ) represents a liver or a part of the liver of a patient at a point in time in the transitional phase after an administration of the hepatobiliary contrast agent, wherein the optional at least one native MRI image (MRI P 0 ) represents the liver or the part of the liver of the patient without contrast agent, • inputting the patient data (PD) into a trained machine learning model (MLM t ), wherein the machine learning model (MLM t ) was trained on the basis of training data (TD) to generate a predicted second MRI image (MRI 2 *) on the basis of at least one first MRI image (MRI 1 ) and optionally at least one native MRI image (MRI 0 ), wherein the training data (TD) comprise input data (I) and target data (T) for each examination object of a plurality of examination objects, wherein the input data (I) comprise MRI images (MRI 0 , MRI 1 ), wherein the MRI images (MRI 0 , MRI 1 ) are restricted to: ∘ at least one first MRI image (MRI 1 ), wherein the at least one first MRI image (MRI 1 ) represents a liver or a part of the liver of the examination object at a point in time in the transitional phase after an administration of a hepatobiliary contrast agent, and ∘ optionally at least one native MRI image (MRI 0 ) of the liver or the part of the liver of the examination object without contrast agent, wherein the target data (T) comprise a second MRI image (MRI 2 ), wherein the second MRI image (MRI 2 ) represents the liver or the part of the liver of the examination object at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • receiving a predicted MRI image (MRI P 2* ) from the trained machine learning model (MLM t ), wherein the predicted MRI image (MRI P 2* ) represents the liver or the part of the liver of the patient at a point in time in the hepatobiliary phase after the administration of the hepatobiliary contrast agent, • outputting and/or storing the predicted MRI image (MRI P 2* ) and/or transmitting the predicted MRI image (MRI P 2* ) to a separate computer system.

Description

Die vorliegende Erfindung befasst sich mit der Beschleunigung einer MRT-Untersuchung der Leber mit Hilfe eines Modells des maschinellen Lernens. Das Modell des maschinellen Lernens ist konfiguriert und trainiert, eine MRT-Aufnahme einer Leber in der hepatobiliären Phase nach der Applikation eines hepatobiliären Kontrastmittels auf Basis einer oder mehrerer MRT-Aufnahmen, die zu einem Zeitpunkt in einer früheren Phase erzeugt worden sind, vorherzusagen. Gegenstände der vorliegenden Erfindung sind ein computer-implementiertes Verfahren zum Vorhersagen der MRT-Aufnahme in der hepatobiliären Phase mittels des trainierten Modells sowie ein Computersystem und ein Computerprogrammprodukt zum Ausführen des Vorhersageverfahrens. Die zeitliche Verfolgung von Vorgängen innerhalb des Körpers eines Menschen oder eines Tieres mit bildgebenden Verfahren spielt unter anderem bei der Diagnose und/oder Therapie von Krankheiten eine wichtige Rolle. Als Beispiel sei die Detektion und Differentialdiagnose fokaler Leberläsionen mittels dynamischer kontrastverstärkender Magnetresonanztomographie (MRT) mit einem hepatobiliären Kontrastmittel genannt. Ein hepatobiliäres Kontrastmittel wie beispielsweise Primovist® kann zur Detektion von Tumoren in der Leber eingesetzt werden. Die Blutversorgung des gesunden Lebergewebes erfolgt in erster Linie über die Pfortader (Vena portae), während die Leberarterie (Arteria hepatica) die meisten Primärtumoren versorgt. Nach intravenöser Bolusinjektion eines Kontrastmittels lässt sich dementsprechend eine Zeitverzögerung zwischen der Signalanhebung des gesunden Leberparenchyms und des Tumors beobachten. Neben malignen Tumoren findet man in der Leber häufig gutartige Läsionen wie Zysten, Hämangiome und fokal noduläre Hyperplasien (FNH). Für eine sachgerechte Therapieplanung müssen diese von den malignen Tumoren differenziert werden. Primovist® kann zur Detektion und Differenzierung gutartiger und bösartiger fokaler Leberläsionen eingesetzt werden. Es liefert mittels T1-gewichteter MRT Informationen über den Charakter dieser Läsionen. Bei der Differenzierung nutzt man die unterschiedliche Blutversorgung von Leber und Tumor und den zeitlichen Verlauf der Kontrastverstärkung. Bei der durch Primovist® erzielten Kontrastverstärkung während der Anflutungsphase beobachtet man typische Anreicherungsmuster, die Informationen für die Charakterisierung der Läsionen liefern. Die Darstellung der Vaskularisierung hilft, die Läsionstypen zu charakterisieren und den räumlichen Zusammenhang zwischen Tumor und Blutgefäßen zu bestimmen. Bei T1-gewichteten MRT-Aufnahmen führt Primovist® 10-20 Minuten nach der Injektion (in der hepatobiliären Phase) zu einer deutlichen Signalverstärkung im gesunden Leberparenchym, während Läsionen, die keine oder nur wenige Hepatozyten enthalten, z.B. Metastasen oder mittelgradig bis schlecht differenzierte hepatozelluläre Karzinome (HCCs), als T1-hypointensere Bereiche auftreten, während bspw. fokal noduläre Hyperplasien Kontrastmittel in dieser Phase anreichern. Die zeitliche Verfolgung der Verteilung des Kontrastmittels bietet also eine gute Möglichkeit der Detektion und Differentialdiagnose fokaler Leberläsionen, allerdings zieht sich die Untersuchung über eine vergleichsweise lange Zeitspanne hin. Über diese Zeitspanne sollten Bewegungen des Patienten weitgehend vermieden werden, um Bewegungsartefakte in den MRT-Aufnahmen zu minimieren. Die lang andauernde Bewegungseinschränkung kann für einen Patienten unangenehm sein. In der Offenlegungsschrift WO2021/052896A1 wird vorgeschlagen, eine oder mehrere MRT-Aufnahmen während der hepatobiliären Phase nicht messtechnisch zu erzeugen, sondern auf Basis von MRT-Aufnahmen aus einer Mehrzahl an vorangegangenen Phasen zu berechnen (vorherzusagen), um die Aufenthaltsdauer des Patienten im MRT-Scanner zu verkürzen. Überraschend wurde gefunden, dass zur Vorhersage einer MRT-Aufnahme der Leber in der hepatobiliären Phase keine MRT-Aufnahmen aus einer Mehrzahl an unterschiedlichen Phasen erforderlich ist. Überraschend wurde gefunden, dass die Qualität der vorhergesagten MRT-Aufnahme gleichwertig ist, wenn die Vorhersage lediglich auf Basis einer oder mehrerer kontrastmittelverstärkten MRT-Aufnahmen, die die Leber zu einem Zeitpunkt in der Übergangsphase repräsentiert/repräsentieren, und optional einer oder mehrerer nativen MRT-Aufnahmen erfolgt. Ein erster Gegenstand der vorliegenden Erfindung ist daher ein computer-implementiertes Verfahren zur Vorhersage einer MRT-Aufnahme gemäß Anspruch 1, wobei das Verfahren unter anderem umfasst: Empfangen von Patientendaten, wobei die Patientendaten mindestens eine MRT-Aufnahme umfassen, wobei die mindestens eine MRT-Aufnahme beschränkt ist auf mindestens eine erste MRT-Aufnahme und optional mindestens eine native MRT-Aufnahme, wobei die mindestens eine erste MRT-Aufnahme eine Leber oder einen Teil der Leber eines Patienten zu einem Zeitpunkt in der Übergangsphase nach einer Applikation eines hepatobiliäre