Abstract: Disclosed is a cirrus image generation method that solves the problem that remote sensing image dehazing datasets are not real and the number of cirrus image features is limited. The present disclosure includes: Obtaining real cirrus images, preprocessing; using a forward process diffusion, constructing a Markov chain, and adding random noise gradually to the cirrus image of the cirrus image dataset. A training dataset is constructed according to the pure noise image and the corresponding real noise. Taking the pure noise image as the input and the corresponding noise as the output, a noise prediction model is established, and the training dataset is used for training, by inputting the randomly generated noise image into the trained noise prediction model, and combining the predicted noise with posterior probability to perform reverse denoising on the image. After a set number of denoising steps, a cirrus image is obtained with real cirrus features.

Abstract: A method for analyzing health of a pancreas of an individual includes receiving a computed tomography (CT) image of a pancreas of the individual; analyzing the CT image to determine a value of each of one or more radiomic features of the CT image; and based on the value of each of the one or more radiomic features of the CT image, determining a pancreatic ductal adenocarcinoma (PDAC) risk factor for the pancreas of the individual.

Abstract: A method for performing real-time magnetic resonance (MR) imaging on a subject is disclosed. A prep pulse sequence is applied to the subject to obtain a high-quality special subspace, and a direct linear mapping from k-space training data to subspace coordinates. A live pulse sequence is then applied to the subject. During the live pulse sequence, real-time images are constructed using a fast matrix multiplication procedure on a single instance of the k-space training readout (e.g., a single k-space line or trajectory), which can be acquired at a high temporal rate.

Abstract: A method of analyzing computed tomography (CT) images comprises receiving an initial CT image of an object, identifying calcium-free regions and a calcified region in the initial CT image of the object, generating a calcium-free image patch, and applying the calcium-free image patch to the initial CT image patch to produce a final CT image. The initial CT image shows a calcium deposit and a target structure in the object. The calcified region in the initial CT image shows the calcium deposit in the object obscuring a portion of the target structure. The calcium-free regions show the remaining portions of the target structure. The calcium-free image patch corresponds to the calcified region in the initial CT image. The final CT image shows the calcium-free image patch and the calcium-free region from the initial CT image. The calcium-free image patch is generated and applied using a convolutional neural network.

Abstract: A method for performing magnetic resonance imaging on a subject comprises obtaining undersampled imaging data, extracting one or more temporal basis functions from the imaging data, extracting one or more preliminary spatial weighting functions from the imaging data, inputting the one or more preliminary spatial weighting functions into a neural network to produce one or more final spatial weighting functions, and multiplying the one or more final spatial weighting functions by the one or more temporal basis functions to generate an image sequence. Each of the temporal basis functions corresponds to at least one time-varying dimension of the subject. Each of the preliminary spatial weighting functions corresponds to a spatially-varying dimension of the subject. Each of the final spatial weighting functions is an artifact-free estimation of the one of the one or more preliminary spatial weighting functions.

Abstract: A method for performing magnetic resonance imaging on a subject comprises obtaining undersampled imaging data, extracting one or more temporal basis functions from the imaging data, extracting one or more preliminary spatial weighting functions from the imaging data, inputting the one or more preliminary spatial weighting functions into a neural network to produce one or more final spatial weighting functions, and multiplying the one or more final spatial weighting functions by the one or more temporal basis functions to generate an image sequence. Each of the temporal basis functions corresponds to at least one time-varying dimension of the subject. Each of the preliminary spatial weighting functions corresponds to a spatially-varying dimension of the subject. Each of the final spatial weighting functions is an artifact-free estimation of the one of the one or more preliminary spatial weighting functions.

Abstract: A method of analyzing a multidimensional image tensor containing a plurality of images comprises: performing imaging scans of a subject to obtain imaging data; generating the multidimensional image tensor from the imaging data; determining a spatial basis tensor containing basis images based on the multidimensional image tensor; determining a temporal basis tensor containing basis functions for a temporal dimension based on the multidimensional image tensor; determining a core tensor that relates the spatial basis tensor to the temporal basis tensor; pre-multiplying the core tensor and the temporal basis tensor to produce a modified temporal basis tensor; storing the spatial basis tensor and the modified temporal basis tensor; and generating an image by multiplying at least (i) at least a portion of the spatial basis tensor and (ii) at least a portion of the modified temporal basis tensor.

Abstract: A method of analyzing a multidimensional image tensor containing a plurality of images comprises: performing imaging scans of a subject imaging data; generating the multidimensional image tensor from the imaging data; determining a spatial basis tensor containing basis images based on the multidimensional image tensor; determining a temporal basis tensor containing basis functions for a temporal dimension based on the multidimensional image tensor; determining a core tensor that relates the spatial basis tensor to the temporal basis tensor; pre-multiplying the core tensor and the temporal basis tensor to produce a modified temporal basis tensor; storing the spatial basis tensor and the modified temporal basis tensor; and generating an image by multiplying at least (i) at least a portion of the spatial basis tensor and (ii) at least a portion of the modified temporal basis tensor.

Abstract: Provided herein are methods based on MRI data with genetic alterations of tumors in the determination of severity and provision of treatment options for bladder cancer patients.

Abstract: There are provided methods for diagnosing coronary heart disease in a subject in need thereof comprising administering an admixture comprising CO2 to a subject to reach a predetermined PaCO2 in the subject to induce hyperemia, monitoring vascular reactivity in the subject and diagnosing the presence or absence of coronary heart disease in the subject, wherein decreased vascular reactivity in the subject compared to a control subject is indicative of coronary heart disease. There are also provided methods for increasing sensitivity and specificity of BOLD MRI.

Abstract: A method for performing real-time magnetic resonance (MR) imaging on a subject is disclosed. A prep pulse sequence is applied to the subject to obtain a high-quality special subspace, and a direct linear mapping from k-space training data to subspace coordinates. A live pulse sequence is then applied to the subject. During the live pulse sequence, real-time images are constructed using a fast matrix multiplication procedure on a single instance of the k-space training readout (e.g., a single k-space line or trajectory), which can be acquired at a high temporal rate.

Abstract: A method for performing real-time magnetic resonance (MR) imaging on a subject is disclosed. A prep pulse sequence is applied to the subject to obtain a high-quality special subspace, and a direct linear mapping from k-space training data to subspace coordinates. A live pulse sequence is then applied to the subject. During the live pulse sequence, real-time images are constructed using a fast matrix multiplication procedure on a single instance of the k-space training readout (e.g., a single k-space line or trajectory), which can be acquired at a high temporal rate.

Abstract: A method of performing multidimensional magnetic resonance imaging on a subject comprises collecting imaging data for a region of interest of the subject, the imaging data related to one or more spatially-varying parameters of the subject within the region of interest; collecting auxiliary data for the region of interest in the subject, the auxiliary data related to one or more time-varying parameters of the subject within the region of interest; linking the imaging data and the auxiliary data; and constructing an image tensor with one or more temporal dimensions based on at least a portion of the linked imaging data and at least a portion of the linked auxiliary data.

Abstract: A method for performing magnetic resonance imaging on a subject comprises: injecting a contrast agent into a region of interest of the subject; applying a pulse sequence to the region of interest; collecting auxiliary data for the region of interest, the auxiliary data being related to one or more time-varying parameters of the subject within the region of interest; determining a temporal factor ? from the auxiliary data; collecting imaging data for the region of interest, the imaging data being related to one or more spatially-varying parameters of the subject within the region of interest; determining a spatial factor Ur from the imaging data; modeling a multi-dimensional image sequence as I=Ur?; and deriving at least a first metric and a second metric from the multi-dimensional image sequence I, the first metric and the second metric being associated with distinct perfusion-based imaging techniques.

Abstract: A method for performing real-time magnetic resonance (MR) imaging on a subject is disclosed. A prep pulse sequence is applied to the subject to obtain a high-quality special subspace, and a direct linear mapping from k-space training data to subspace coordinates. A live pulse sequence is then applied to the subject. During the live pulse sequence, real-time images are constructed using a fast matrix multiplication procedure on a single instance of the k-space training readout (e.g., a single k-space line or trajectory), which can be acquired at a high temporal rate.

Abstract: A method for performing magnetic resonance imaging on a subject comprises obtaining undersampled imaging data, extracting one or more temporal basis functions from the imaging data, extracting one or more preliminary spatial weighting functions from the imaging data, inputting the one or more preliminary spatial weighting functions into a neural network to produce one or more final spatial weighting functions, and multiplying the one or more final spatial weighting functions by the one or more temporal basis functions to generate an image sequence. Each of the temporal basis functions corresponds to at least one time-varying dimension of the subject. Each of the preliminary spatial weighting functions corresponds to a spatially-varying dimension of the subject. Each of the final spatial weighting functions is an artifact-free estimation of the one of the one or more preliminary spatial weighting functions.

Abstract: In various embodiments, the invention teaches systems and methods for magnetic resonance imaging. In some embodiments, the invention teaches systems and methods for determining the source of pain in intervertebral discs by measuring one or more physiological biomarkers associated with disc pain and/or disc degeneration.

Abstract: According to some implementations of the present disclosure, a system for identifying individuals at risk for PDAC includes a CT scanner, a memory, and a control system. The CT scanner is configured to generate CT image data associated with a pancreas of a patient. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions. The CT image data associated with the pancreas of the patient is received. The received CT image data is processed to output a set of CT image features. The set of CT image features is received as an input to a machine learning PDAC prediction algorithm. An indication of whether the patient is at high risk for PDAC is determined as an output of the machine learning PDAC prediction algorithm.

Abstract: In various embodiments, the invention teaches systems and methods for magnetic resonance imaging. In some embodiments, the invention teaches systems and methods for determining the source of pain in intervertebral discs by measuring one or more physiological biomarkers associated with disc pain and/or disc degeneration.

Abstract: The invention provides methods for diagnosing coronary heart disease in a subject in need thereof comprising administering an admixture comprising CO2 to a subject to reach a predetermined PaCO2 in the subject to induce hyperemia, monitoring vascular reactivity in the subject and diagnosing the presence or absence of coronary heart disease in the subject, wherein decreased vascular reactivity in the subject compared to a control subject is indicative of coronary heart disease. The invention also provides methods for increasing sensitivity and specificity of BOLD MRI.