Patent Description:
Registration of medical images is the basis for many medical imaging analysis tasks. However, registration of medical images may be heavily influenced by motion patterns of a patient, such as, e.g., respiratory or cardiac motion. Such motion patterns of the patient cause various states of deformation of organs and other anatomical objects in the medical images, resulting in misalignment between the medical images, and typically occur in regions of the patient where there is a high need for accurate registration. Conventional registration techniques are often unable to accurately compensate for such deformation. In particular, many convention registration techniques focus on aligning structures with high intensity differences while missing misalignment of structures of low intensity differences.

A weakly supervised approach to learn domain specific aggregations of conventional metrics using anatomical segmentations is known from "<NPL>.

A registration algorithm for 2D CT/MRI medical images with an unsupervised end-to-end strategy using convolutional neural networks is known from "<NPL>.

A method and system for image registration using an intelligent artificial agent is known from <CIT>, wherein a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

The present invention is defined by the enclosed claims. A method for medical image registration according to claim <NUM> is provided, and an apparatus according to claim <NUM> is provided.

The regularization term may be determined by summing the distances between the motion distribution of each respective anatomical object and the prior distribution.

In one example, the motion model specific to the respective anatomical object may include a variational autoencoder including an encoder and the motion distribution of the respective anatomical object may be determined using the encoder. The machine learning network may be trained by training an encoder of a variational autoencoder to generate a code representing an encoding of deformation between the region of interest extracted from the first training image and the region of interest extracted from the second training image, and training a decoder of the variational autoencoder to generate a deformation field from the code and the region of interest extracted from the first training image, the deformation field representing the deformation between the region of interest extracted from the first training image and the region of interest extracted from the second training image.

In one example, for each respective anatomical object, the motion model specific to the respective anatomical object may be learned by receiving a first training image and a second training image of the respective anatomical object, detecting a region of interest comprising the respective anatomical object in the first training image and a region of interest comprising the respective anatomical object in the second training image, extracting the region of interest from the first training image and the second training image, and training a machine learning network to model motion of the respective anatomical object from the region of interest extracted from the first training image and the region of interest extracted from the second training image as the motion model specific to the respective anatomical object.

In one example, the region of interest comprising the respective anatomical object may be detected by segmenting the respective anatomical object in one of the first input medical image or the second input medical image and centering the region of interest around the segmented respective anatomical object.

The present invention generally relates to methods and systems for compensation of organ deformation for medical image registration. Embodiments of the present invention are described herein to give a visual understanding of such methods and systems for compensation of organ deformation for medical image registration. A digital image is often composed of digital representations of one or more objects (or shapes). The digital representation of an object is often described herein in terms of identifying and manipulating the objects. Such manipulations are virtual manipulations accomplished in the memory or other circuitry/hardware of a computer system. Accordingly, is to be understood that embodiments of the present invention may be performed within a computer system using data stored within the computer system.

Further, it should be understood that while the embodiments discussed herein may be discussed with respect to compensation of organ deformation for medical image registration, the present invention is not so limited. Embodiments of the present invention may be applied for the registration of images of any type by compensating for deformation or motion of any object of interest.

In medical image registration, the goal is to find a spatial transformation to transform a first image to align with a second image. The first image is typically referred to as the moving image and the second image is typically referred to as the fixed image. Often times, motion such as, e.g., respiratory motion or cardiac motion causes deformations of anatomical objects depicted in the medical images, resulting in inaccuracies in such medical image registration. Certain embodiments of the present invention learn a motion model specific to an anatomical object and use the motion model to determine a regularization term for the anatomical object for medical image registration, thereby compensating for deformation or other motion of the anatomical object. Advantageously, the regularization term provides for medical image registration with a high degree of accuracy.

<FIG> shows a method <NUM> for medical image registration, in accordance with one or more embodiment. Method <NUM> may be performed using any suitable computing device, such as, e.g., computer <NUM> of <FIG>.

At step <NUM>, a motion model specific to a respective anatomical object of one or more anatomical objects is learned. Step <NUM> may be performed during an offline or preprocessing stage. In one embodiment, the motion model specific to the respective anatomical object is learned by performing the steps of method <NUM> of <FIG>.

Referring to <FIG>, a method <NUM> for learning a motion model specific to an anatomical object is shown, in accordance with one or more embodiments. Method <NUM> may be performed by any suitable computing device, such as, e.g., computer <NUM> of <FIG>.

At step <NUM>, a first training image and a second training image of one or more particular anatomical objects are received. The first training image may correspond to a moving image denoted I<NUM> and the second training image may correspond to a fixed image denoted I<NUM>. The first training image I<NUM> and second training image I<NUM> depict the same one or more particular anatomical objects in various states of deformation due to, e.g., respiratory motion or cardiac motion. In one embodiment, the first training image I<NUM> and second training image I<NUM> are of a sequence of images acquired over a period of time. The one or more anatomical objects may include any anatomical structure of a patient, such as, e.g., an organ (e.g., lung, heart, liver, kidney, bladder, etc.), a vessel, a bone, etc..

In one embodiment, the first training image I<NUM> and second training image I<NUM> are of the same modality. The first training image I<NUM> and second training image I<NUM> may be of any suitable modality, such as, e.g., x-ray, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US), single-photon emission computed tomography (SPECT), positron emission tomography (PET), or any other suitable modality or combination of modalities. The first training image I<NUM> and second training image I<NUM> may be received directly from an image acquisition device (e.g., image acquisition device <NUM> of <FIG>) used to acquire the images. Alternatively, the first training image I<NUM> and second training image I<NUM> may be received by loading previously acquired images from a storage or memory of a computer system (e.g., a picture archiving and communication system, PACS) or receiving images that have been transmitted from a remote computer system. It should be understood that while method <NUM> is described with respect to a first training image I<NUM> and a second training image I<NUM>, method <NUM> may be performed using any plurality of training images.

At step <NUM>, for a respective particular anatomical object of the one or more particular anatomical objects, a region of interest comprising the respective particular anatomical object is detected in one of the first training image I<NUM> or the second training image I<NUM>. The region of interest may be detected manually via input from a user (e.g., a clinician) or automatically using any suitable known technique. In one embodiment, the respective particular anatomical object is segmented from the training image according to a selective and iterative method for performance level estimation (SIMPLE) method and the region of interest is centered around the segmented respective particular anatomical object.

At step <NUM>, the region of interest comprising the respective particular anatomical object is extracted from the first training image and the second training image.

At step <NUM>, a machine learning network is trained to model motion of the respective particular anatomical object based on the region of interest extracted from the first training image and the region of interest extracted from the second training image to learn a motion model specific to the respective particular anatomical object. The machine learning network may be any suitable machine learning for modeling motion of the anatomical object. In one embodiment, the machine learning network is a variational autoencoder (VAE), such as, e.g., VAE <NUM> shown in <FIG>, to provide for a probabilistic motion model specific to the respective particular anatomical object, in accordance with one or more embodiments.

VAE <NUM> includes an encoder pω <NUM> and a decoder pθ <NUM>. Encoder pω <NUM> is a neural network that receives as input sub-images <MAT> <NUM> and <MAT> <NUM> and outputs code z<NUM> <NUM>. Sub-images <MAT> <NUM> and <MAT> <NUM> are the regions of interest o extracted from the first training image I<NUM> and the second training image I<NUM> respectively. Code z<NUM> <NUM> is a low-dimensional vector representing the mean and variance of the multivariate Gaussian sampled from the first training image I<NUM> and the second training image I<NUM> by encoder pω <NUM>. Decoder pθ <NUM> is a neural network that receives as input sub-image <MAT> <NUM> and code z<NUM> <NUM> and outputs velocities v <NUM> and deformation field ϕ <NUM>. Velocities v <NUM> are raw outputs of VAE <NUM> and are non-diffeomorphic. Velocities v <NUM> comprise the velocity value for each pixel. Deformation field ϕ <NUM> is computed by exponentiation and represents the deformation between sub-images <MAT> <NUM> and <MAT> <NUM>. By making appearance information of the moving sub-image <MAT> <NUM> available to decoder pθ <NUM>, deformation field ϕ <NUM> is more likely to encode deformation information rather than appearance information. As shown in <FIG>, deformation field ϕ <NUM> is applied to sub-image <MAT> <NUM> to reconstruct sub-image <MAT> <NUM>. VAE <NUM> is trained according to Equation <NUM> to optimize code z<NUM> <NUM> to best transform sub-image <MAT> <NUM> to align with sub-image <MAT> <NUM>. <MAT> where p(z<NUM>) is a prior distribution of z<NUM> and is assumed to follow a multivariate unit Gaussian distribution. Prior distribution p(z<NUM>) refers to the distribution learned by VAE <NUM> representing the distribution of all probable motions of the respective particular anatomical object (as observed in a training set).

It should be understood that method <NUM> may return to step <NUM> and steps <NUM>-<NUM> may be iteratively repeated to learn a motion model specific to each particular anatomical object of the one or more particular anatomical objects.

Referring back to <FIG>, at step <NUM>, a first input medical image and a second input medical image of the one or more anatomical objects are received. The first input medical image may correspond to a moving image M and the second input medical image may correspond to a fixed image F of a dataset to be registered. The first input medical image M and the second input medical image F depict the same one or more anatomical objects in various states of deformation due to, e.g., respiratory motion or cardiac motion. In one embodiment, the first input medical image M and the second input medical image F are of a sequence of images acquired over a period of time. The first input medical image M and the second input medical image F may be of any suitable modality.

The first input medical image M and the second input medical image F may be received directly from an image acquisition device (e.g., image acquisition device <NUM> of <FIG>) used to acquire the images. Alternatively, the first input medical image M and the second input medical image F may be received by loading previously acquired images from a storage or memory of a computer system (e.g., a picture archiving and communication system, PACS) or receiving images that have been transmitted from a remote computer system.

At step <NUM>, for each respective anatomical object, a region of interest o comprising the respective anatomical object is detected in one of the first input medical image M or the second input medical image F. The region of interest o may be detected manually via input from a user (e.g., a clinician) or automatically using any suitable known technique. In one embodiment, the region of interest may be automatically by segmenting the respective anatomical object from the first input medical image M or the second input medical image F and centering the region of interest around the segmented respective anatomical object, as described above with respect to step <NUM> of method <NUM> in <FIG>.

At step <NUM>, the region of interest is extracted from the first input medical image and from the second input medical image.

At step <NUM>, a motion distribution of the respective anatomical object is determined from the region of interest extracted from the first input medical image M and the region of interest extracted from the second input medical image F using the motion model specific to the respective anatomical object (learned at step <NUM>). In one embodiment, where the motion model is a VAE, a trained encoder pω (e.g., encoder pω <NUM> of <FIG>) of the motion model is applied on the region of interest extracted from the first input medical image M and the region of interest extracted from the second input medical image F according to the function pω(M<NUM>, M<NUM> ∘ ϕθ) to determine the motion distribution z<NUM> of the respective anatomical object. In particular, the encoder pω receives as input the region of interest extracted from the first input medical image M and the region of interest extracted from the first input medical image M as modified by the deformation field ϕθ and outputs the motion distribution z<NUM>. Deformation field ϕθ represents the global motion between the first input medical image M and the second input medical image F. Advantageously, computing motion distribution z<NUM> on the same image (e.g., the first input medical image M) enables multi-modality registration with training of the motion model specific to the respective anatomical object on only one of the modalities. It should be understood that motion distribution z<NUM> may instead be determined according to pω(F<NUM>, F<NUM> ∘ ϕθ) by inverting the resulting motion distribution z<NUM> where pω is trained using the fixed image.

The distance between the motion distribution z<NUM> = pω(M<NUM>,M<NUM> ∘ ϕθ) and the prior distribution p(z<NUM>) is then minimized by varying training weights θ of the network at each iteration. Minimizing the distance between motion distribution z<NUM> and prior distribution p(z<NUM>) ensures that motion distribution z<NUM> is a probable motion for the respective anatomical object. In one embodiment, the distance is a Kullback-Leibler Divergence. However, other distance metrics <IMG> may be applied, such as, e.g., optimal transport loss, generative adversarial networks, adversarial autoencoders, or any other suitable distance metric.

Method <NUM> may return back to step <NUM> and steps <NUM>-<NUM> may be iteratively repeated for each respective anatomical object of the one or more anatomical objects (e.g., for whole body registration) to thereby determine a motion distribution of each of the one or more anatomical objects.

At step <NUM>, the first input medical image and the second input medical image are registered based on the motion distribution of each respective anatomical object of the one or more anatomical objects to generate a fused image.

In one embodiment, an anatomical object-specific regularization term <IMG>(ϕ) is determined as the summation of the distances between the motion distribution z<NUM> = pω(M<NUM>, M<NUM> ∘ ϕθ) for each respective anatomical object and the prior distribution p(z<NUM>), as shown in Equation <NUM>:
<MAT>
where o is the region of interest for a respective anatomical object, <IMG> is a distance metric (e.g., Kullback-Leibler Divergence), <MAT> is the region of interest o extracted from the first input medical image M, ϕθ is the global motion between the first input medical image M and the second input medical image F, and p(z<NUM>) is the prior distribution. The first input medical image and the second input medical image are then registered according to the loss function of Equation <NUM>:
<MAT>
where <IMG> is a distance metric (e.g., Kullback-Leibler Divergence), F is the second input medical image, M is the first input medical image, ϕθ is the divergence field of the motion model, <IMG>(ϕθ) is a spatial regularization term, <IMG>(ϕθ) is a regularization term for the one or more anatomical objects defined in Equation <NUM>, and λ<NUM> and λ<NUM> are parameters that weight <IMG>(ϕθ) and <IMG>(ϕθ) respectively. The loss function of Equation <NUM> includes the regularization term for the one or more anatomical objects and therefore may be used to compensate for organ deformation to train a machine learning network to register the first input medical image and the second input medical image to generate the fused image.

At step <NUM>, the fused image is output. For example, the fused image can be output by displaying the fused image on a display device of a computer system, storing the fused image on a memory or storage of a computer system, or by transmitting the fused image to a remote computer system.

Advantageously, embodiments of the present invention provide for an anatomical object-specific regularization term <IMG>(ϕθ) for medical image registration to focus the registration process on regions of interest and compensate for motion of anatomical objects in the regions of interest. Embodiments of the present invention are not as computationally expensive to perform as convention techniques that require segmentation of anatomical objects in both images, as certain embodiments of the present invention involve detecting a bounding box in one of the input medical images. Embodiments of the present invention allow for medical image registration with a high degree of accuracy.

In some embodiments, instead of the prior distribution p(z<NUM>), a mean posterior distribution pω(·,·) may be used. The mean posterior distribution pω(·,·) may be extracted after learning the motion models for each anatomical object.

Systems, apparatuses, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Typically, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. A computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto-optical disks, optical disks, etc..

Systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computer and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers.

Systems, apparatus, and methods described herein may be implemented within a network-based cloud computing system. In such a network-based cloud computing system, a server or another processor that is connected to a network communicates with one or more client computers via a network. A client computer may communicate with the server via a network browser application residing and operating on the client computer, for example. A client computer may store data on the server and access the data via the network. A client computer may transmit requests for data, or requests for online services, to the server via the network. The server may perform requested services and provide data to the client computer(s). The server may also transmit data adapted to cause a client computer to perform a specified function, e.g., to perform a calculation, to display specified data on a screen, etc. For example, the server may transmit a request adapted to cause a client computer to perform one or more of the steps or functions of the methods and workflows described herein, including one or more of the steps or functions of <FIG>. Certain steps or functions of the methods and workflows described herein, including one or more of the steps or functions of <FIG>, may be performed by a server or by another processor in a network-based cloud-computing system. Certain steps or functions of the methods and workflows described herein, including one or more of the steps of <FIG>, may be performed by a client computer in a network-based cloud computing system. The steps or functions of the methods and workflows described herein, including one or more of the steps of <FIG>, may be performed by a server and/or by a client computer in a network-based cloud computing system, in any combination.

Systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method and workflow steps described herein, including one or more of the steps or functions of <FIG>, may be implemented using one or more computer programs that are executable by such a processor. A computer program is a set of computer program instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result.

A high-level block diagram of an example computer <NUM> that may be used to implement systems, apparatus, and methods described herein is depicted in <FIG>. Computer <NUM> includes a processor <NUM> operatively coupled to a data storage device <NUM> and a memory <NUM>. Processor <NUM> controls the overall operation of computer <NUM> by executing computer program instructions that define such operations. The computer program instructions may be stored in data storage device <NUM>, or other computer readable medium, and loaded into memory <NUM> when execution of the computer program instructions is desired. Thus, the method and workflow steps or functions of <FIG> can be defined by the computer program instructions stored in memory <NUM> and/or data storage device <NUM> and controlled by processor <NUM> executing the computer program instructions. For example, the computer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform the method and workflow steps or functions of <FIG>. Accordingly, by executing the computer program instructions, the processor <NUM> executes the method and workflow steps or functions of <FIG>. Computer <NUM> may also include one or more network interfaces <NUM> for communicating with other devices via a network. Computer <NUM> may also include one or more input/output devices <NUM> that enable user interaction with computer <NUM> (e.g., display, keyboard, mouse, speakers, buttons, etc.).

Processor <NUM> may include both general and special purpose microprocessors, and may be the sole processor or one of multiple processors of computer <NUM>. Processor <NUM> may include one or more central processing units (CPUs), for example. Processor <NUM>, data storage device <NUM>, and/or memory <NUM> may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs).

Data storage device <NUM> and memory <NUM> each include a tangible non-transitory computer readable storage medium. Data storage device <NUM>, and memory <NUM>, may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices.

Input/output devices <NUM> may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices <NUM> may include a display device such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor for displaying information to the user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to computer <NUM>.

An image acquisition device <NUM> can be connected to the computer <NUM> to input image data (e.g., medical images) to the computer <NUM>. It is possible to implement the image acquisition device <NUM> and the computer <NUM> as one device. It is also possible that the image acquisition device <NUM> and the computer <NUM> communicate wirelessly through a network. In a possible embodiment, the computer <NUM> can be located remotely with respect to the image acquisition device <NUM>.

Any or all of the systems and apparatus discussed herein may be implemented using one or more computers such as computer <NUM>.

One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that <FIG> is a high level representation of some of the components of such a computer for illustrative purposes.

Claim 1:
A computer-implemented method, comprising:
receiving (<NUM>) a first input medical image and a second input medical image depicting the same one or more anatomical objects in various states of deformation;
for each respective anatomical object of the one or more anatomical objects:
detecting (<NUM>) a region of interest comprising the respective anatomical object in one of the first input medical image or the second input medical image,
extracting (<NUM>) the region of interest, respectively, from the first input medical image and from the second input medical image, wherein the extracted regions of interest are sub-images of the first input medical image and of the second input medical image, and
determining (<NUM>) a motion distribution of the respective anatomical object, the motion distribution being a probable motion for the respective anatomical object, by applying a motion model on the region of interest extracted from the first input medical image and the region of interest extracted from the second input medical image using the motion model, wherein the motion model is a learnt motion model specific to the respective anatomical object; and
registering (<NUM>) the first input medical image and the second input medical image based on the motion distribution of each respective anatomical object of the one or more anatomical objects,
characterized in that
registering the first input medical image and the second input medical image based on the motion distribution of each respective anatomical object of the one or more anatomical objects comprises:
determining an anatomical object-specific regularization term for the one or more anatomical objects based on distances between the motion distribution of each respective anatomical object and a prior distribution of the respective anatomical object.