Model-based image reconstruction (MBIR) methods using convolutional neural networks (CNNs) as priors have demonstrated superior image quality and robustness compared to conventional methods. Studies have explored MBIR combined with supervised and unsupervised denoising techniques for image reconstruction in magnetic resonance imaging (MRI) and positron emission tomography (PET). Unsupervised methods like the deep image prior (DIP) have shown promising results and are less prone to hallucinations. However, since the noisy image is used as a reference, strategies to prevent overfitting are unclear. Recently, Bayesian DIP (BDIP) networks that model uncertainty tend to prevent overfitting without requiring early stopping. However, BDIP has not been studied with data-fidelity term for image reconstruction. Present disclosure provides systems and method that implement a MBIR framework with a modified BDIP. Specifically, an uncertainty-based penalty is included to the BDIP to improve reconstruction across iterations.

An imaging method including: a) acquiring N successive positron emission tomography (PET) low resolution images ?.sub.i and simultaneously, N successive Ultrafast Ultrasound Imaging (UUI) images Ui of a moving object; b) determining from each UUI image Ui, the motion vector fields M.sub.i that corresponds to the spatio-temporal geometrical transformation of the motion of the object; c) obtaining a final estimated high resolution image H of the object by iterative determination of a high resolution image H.sup.n+1 obtained by applying several correction iterations to a current estimated high resolution image H.sup.n, n being the number of iterations, starting from an initial estimated high resolution image H.sup.1 of the object, each correction iteration including at least: i) warping the estimated high resolution image H.sup.n using the motion vector fields M.sub.i to determine a set of low resolution reference images L.sup.n.sub.i; ii) determining a differential image Di by difference between each PET image ?.sub.i and the corresponding low resolution reference image L.sup.n.sub.i; iii) warping back the differential images Di using the motion vector fields M.sub.i and averaging the N warped back differential images to obtain a high resolution differential image; iv) determining the high resolution image H.sup.n+1 by correcting the high resolution image H.sup.n using the high resolution differential image; d) applying the motion vector fields M.sub.i of each UUI image Ui to the final high resolution image H.

A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Described herein are systems and methods for automated completion, combination, and completion by combination of sinograms. In certain embodiments, sinogram completion is based on a photographic (e.g. spectral or optical) acquisition and a CT acquisition (e.g., micro CT). In other embodiments, sinogram completion is based on two CT acquisitions. The sinogram to be completed may be truncated due to a detector crop (e.g., a center-based crop or an offset based crop). The sinogram to be completed may be truncated due to a subvolume crop (e.g., based on low resolution image projected onto sinogram).

Systems and methods are provided for a visualization of a multi-modal medical image for diagnostic medical imaging. The systems and methods receive first and second image data sets of an anatomical structure of interest, register the first and second image data sets to a geometrical model of the anatomical structure of interest to form a registered image. The geometrical model includes a location of an anatomical marker. The systems and methods further display the registered image.

The disclosure relates to receiving a provisioning request for providing a magnetic resonance tomography (MRT) image of a geometric image region of an object, wherein the provisioning request comprises a specification relating to at least one feature that is to be matched between an MRT reference image of the image region and the MRT image. A control unit is configured to actuate and control an MRT apparatus according to the specification in accordance with a predefined measurement technique to conduct a predefined MRT measurement at least on the geometric image region of the object, and to receive an MRT dataset generated in the measurement. The control unit is configured to provide the MRT image of the geometric image region of the object according to the specification from the MRT dataset in accordance with a predefined exporting method.

A set of first modality data is provided to an intra-reconstruction motion correction method. The set of first modality data includes a plurality of views. A set of second modality data is provided to the method. A motion estimate is generated for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data. A motion corrected model of the set of first modality data is generated by a forward projection including the motion estimate.

The embodiments relate to a reconstructing an image of an examination object, a medical imaging apparatus, and a computer program product where a first image data record is acquired with a first imaging modality and at least one further image data record of at least one further imaging modality is provided. At least one first image is reconstructed on the basis of the first image data record using the at least one further image data record.

A method and related apparatus (VS) for synthesizing a projection image (S), in particular for use in mammography. It is proposed to compute a weight function from one image volume (V1) and is then used to implement a weighted forward projection through another image volume block to compute a synthesized projection image (S) across block (V2).