Abstract: For estimating SAR in MR scanning, a value for a SAR metric is determined from patient measurements, such as a depth camera image of the outer surface of a patient. A machine-learned model is used in the determination, such as for segmenting different tissue types with different electric field characteristics and/or estimating a SAR characteristic from tissue maps. Rather than time consuming calculations to model the patient and/or estimate SAR from patient information, the machine-learned model is used to estimate patient-specific SAR from the measurements of the patient more rapidly and accurately.

Abstract: Systems and methods for performing a medical imaging analysis task using a foundation model are provided. One or more 3D (three-dimensional) medical images each comprising a plurality of 2D (two-dimensional) slices are received. A first set of features is extracted from the plurality of 2D slices of the one or more 3D medical images using a machine learning based encoding network. Each respective feature of the first set of features is resampled based on a spatial location of one or more pixels of the 2D slices from which the respective feature was extracted. The resampled first set of features is encoded into a second set of features. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for performing a medical imaging analysis task conditioned on multi-domain medical images with missing modalities are provided. 1) one or more medical images each in a different domain and 2) a domain code defining a presence of the different domains in a set of predefined domains are received. One or more weights are determined based on the domain code. One or more parameters of a machine learning based encoder are updated based on the one or more weights. Features are extracted from the one or more medical images using the machine learning based encoder with the one or more updated parameters. A medical imaging analysis task is performed based on the extracted features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for performing a medical imaging analysis task using a universal foundation model are provided. 1) one or more input medical images each in a domain and 2) a domain code for each of the one or more input medical images identifying its domain are provided. For each respective one of the domain codes, one or more weights are determined based on the respective domain code. One or more parameters of a dynamic convolutional layer are updated based on the one or more weights. A first set of features is extracted from the one or more input medical images using the dynamic convolutional layer with the one or more updated parameters. The first set of features are encoded into a second set of features using a machine learning based encoder. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for performing a quality assessment of a medical imaging analysis task are provided. At least one low-field MRI (magnetic resonance imaging) quality assurance imaging data of the patient is received. A quality assessment of a medical imaging analysis task is performed based on the at least one low-field MRI quality assurance imaging data using one or more machine learning based networks. Results of the quality assessment are output.

Abstract: Systems and methods for image reconstruction of magnetic resonance data using a conditioned plug-and-play (PnP) method, where a diffusion foundation model is used as implicit image prior for efficient and effective adaptation/customization for specialized clinical use-cases. By attaching an Adapter network to the diffusion foundation model, the method adds extra specialized constraints into PnP iterations.

Abstract: A computer-implemented method for reconstructing MRI images of a body includes the steps of: a) acquiring a set MRI signal samples (sq) at different k-space location along a k-space trajectory; b) acquiring or estimating a B0 inhomogeneity map ?B0(r), wherein B0 is a static magnetic field for polarizing nuclear spins of the body and r represents position in image space; c) initializing an image (f); d) updating the image by a performing a data-consistency operation in k-space, wherein conversion between image space and k-space is performed through a non-uniform discrete pseudo-Fourier transform operator taking into account the B0 inhomogeneity map obtained from step b) to correct for off-resonance effects; and e) further updating the image by using an image-correction neural network. A computer program product and a magnetic resonance imaging apparatus for carrying out such a method is also provided.

Abstract: Systems and methods for image reconstruction and quantitative MRI. Generative models such as diffusion models are used to reconstruct MR images and generative models and constrained mathematical models fit to estimate quantitative maps from the reconstructed MR images.

Abstract: Systems and methods for image restoration and reconstruction using diffusion models. A diffusion plug and play model includes measurement during reverse diffusion steps, which is based on DDIM and supports fast sampling. This measurement is carried out after a correction step that accounts for the inaccurate estimation resulting from computing the proximal solution.

Abstract: One or more tractograms of a global tractography of a tissue of interest are determined. At least one instance of diffusion magnetic resonance imaging data of the tissue of interest is obtained. A trained machine-learning algorithm generates the one or more tractograms based on the at least one instance of the diffusion magnetic resonance imaging data.

Abstract: For data analytics in magnetic resonance (MR) scanning, the scanning configuration information and the resulting raw data are directly used to determine the analytics or clinical decision. Artificial intelligence provides a value for a clinical finding characteristic of the patient based on the raw data from scanning and the controls used to scan, allowing the value to be based on all of the information content of the scan results. Reconstruction is not needed, allowing for simpler hardware, such as hardware with less homogeneous B0 and/or B1 fields than the norm and/or non-linear gradients.

Abstract: Systems and methods for determining a target imaging protocol for an image acquisition are provided. At least one of 1) one or more performance indicators, 2) an input imaging protocol for an image acquisition, or 3) changes in conditions of the image acquisition are received. A target imaging protocol is determined using an image acquisition model based on the at least one of the one or more performance indicators, the input imaging protocol, or the changes in the conditions of the image acquisition. The target imaging protocol are output.

Abstract: Various techniques of reconstructing multiple Magnetic Resonance Imaging, MRI, images for multiple slices based on an MRI measurement dataset that is acquired using a simultaneous multi-slice protocol and undersampling and K-space are disclosed. A convolutional neural network can be used to implement a regularization operation of an iterative optimization for the reconstruction, i.e., an unrolled neural network or variational neural network. A combination with Dixon imaging, i.e., separation of multiple chemical species, is disclosed.

Abstract: Systems and methods for generating synthetic images representing healthy-for-age images of an anatomical object are provided. 1) one or more input medical images of an anatomical object of a patient and 2) an input age associated with the patient are received. A feature set is extracted from the one or more input medical images. The extracted feature set is encoded with noise based on the input age associated with the patient using a machine learning based noise model. An age associated with the patient is predicted based on the extracted feature set. The encoded feature set is denoised based on the input age associated with the patient and the predicted age associated with the patient using a machine learning based denoising model. One or more synthetic images of the anatomical object of the patient are generated based on the denoised feature set. The one or more synthetic images of the anatomical object of the patient are output.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. 1) a plurality of medical images acquired at a plurality of acquisition orientations and in one or more domains and 2) a domain code for each particular acquisition orientation of the plurality of acquisition orientations are received. Each of the domain codes identify a presence of the one or more domains of the plurality of medical images that were acquired at the particular acquisition orientation. For each of the particular acquisition orientations, the domain code for the particular acquisition orientation are encoded, features are extracted from the plurality of medical images that were acquired at the particular acquisition orientation, and the encoded domain code and the extracted features are combined to generate image features for the particular acquisition orientation. A medical imaging analysis task is performed based on the image features for each of the particular acquisition orientations.

Abstract: A method of synthesizing an image of a tube assembly includes capturing an image of the tube assembly, wherein the capturing generates a captured image. The captured image is decomposed into a plurality of features in latent space using a trained image decomposition model. One or more of the features in the latent space is manipulated into one or more manipulated features. A synthesized tube assembly image is generated with at least one of the manipulated features using a trained image composition model. Other methods and systems are disclosed.

Abstract: Systems and methods for generating augmented images are provided. One or more input medical images are received. At least one of noise and one or more transformations are applied to the one or more input medical images to generate one or more noisy augmented images. The one or more noisy augmented images are denoised using a diffusion-based denoising system to generate one or more denoised augmented images. The applying and the denoising are repeated for one or more iterations using the one or more denoised augmented images as the one or more input medical images to generate one or more final augmented images. The one or more final augmented images are output.

Abstract: For automatically determining a configuration for magnetic resonance imaging (MRI), an initial scanner model for an MRI scanner is received. The initial scanner model specifies a deviation of a main magnetic field from a predefined target main magnetic field and/or a deviation of a magnetic field gradient from a predefined target gradient field. MRI measurement data measured by using the MRI scanner is received. A first updated scanner model is generated by applying a trained first machine learning model (MLM) to first input data that depends on the initial scanner model and the MRI measurement data. The configuration for MRI is determined depending on the first updated scanner model.

Abstract: An automated diagnostic analysis system includes a system controller for system-wide workflow planning and execution of sample analyses and also includes decentralized processing capabilities (e.g., one or more second controllers) for determining a work-around, where possible, to a detected fault affecting an analysis of a particular sample. The system controller communicates with system components via a first communication channel, while the second controller communicates with a subset of the system components via a second communication channel. Where a work-around for a detected fault cannot be determined by the second controller, the detected fault is communicated to the system controller for resolution. Methods of operating an automated diagnostic analysis system are also provided, as are other aspects.

Abstract: A method of scanning an organ structure of a patient using magnetic resonance imaging, includes scanning, in a first scanning process, the patient to obtain first image data indicative of at least the organ structure of the patient. The method further includes determining, based on the first image data, one or more parameters obtain second image data indicative of at least the organ structure of the patient. The first scanning process includes a first quality of imaging scan, the second scanning process includes a second quality of imaging scan, and the first quality of imaging scan is higher than the second quality of imaging scan.