Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Abstract: A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.

Abstract: A system for minimizing MGI in a superconducting magnet system of an MRI system includes a thermal shield having bi-metal material. The thermal shield is configured to be disposed about a cold mass of the superconducting magnet system, wherein the bi-metal material is configured to minimize MGI.

Abstract: A system for minimizing MGI in a superconducting magnet system of an MRI system includes a thermal shield having bi-metal material. The thermal shield is configured to be disposed about a cold mass of the superconducting magnet system, wherein the bi-metal material is configured to minimize MGI.

Abstract: A computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets.

Abstract: A pulse sequence generation computing device for a magnetic resonance imaging (MRI) system includes a processor in communication with a memory device. The processor is programmed to receive a pulse sequence including a plurality of gradient pulses and provide a pulse sequence threshold function corresponding to an acoustic noise reduction level. For each gradient pulse in the pulse sequence, the processor is programmed to determine an amplitude and a slew rate of the gradient pulse, determine a threshold amplitude and slew rate of the gradient pulse, and compare the determined amplitude and slew rate to the threshold amplitude and slew rate. If either the determined amplitude or slew rate exceeds the threshold amplitude or slew rate, the processor adjusts at least one of the amplitude and the slew rate of the gradient pulse to an amplitude and a slew rate as defined by the pulse sequence threshold function.

Abstract: The techniques discussed herein relate to a reduced acoustic noise and vibration magnetic resonance imaging (MRI) acquisition. In certain implementations acoustic noise levels for one or more MRI pulse sequences are characterized and modified by limiting the frequencies and amplitudes of the gradient waveforms so as to produce less noise and vibration when the modified waveform is used during an MRI examination. In this manner, relatively low sound pressure levels can be attained.

Abstract: The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.

Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates.

Abstract: The present disclosure relates to the use of prior images acquired of the patient and acoustic signature from a vascular region of interest to create a patient-specific model of sound propagation from the vascular region. This model is then used to monitor the progression of disease in the vascular region of interest, using subsequently-acquired acoustic signals. In an alternate embodiment, population-based images and/or population-based acoustic signatures are used to generate predictive data when a priori patient-specific imaging information is not available and this data is used to characterize or categorize at-risk patients suspected of coronary artery disease, but without prior cardiac events.

Abstract: Various methods and systems are provided for stationary CT imaging. In one embodiment, a modular imaging system comprises a plurality of distributed x-ray units releasably coupled to a plurality of detector arrays, with the plurality of distributed x-ray units and the plurality of detector arrays forming a self-supporting structure including a central opening shaped to receive a subject to be imaged. The modular imaging system may image the subject without rotation of the distributed x-ray units or detector arrays around the subject.

Abstract: The present disclosure relates to automatically determining respiratory phases (e.g., end-inspiration/expiration respiratory phases) in real time using ultrasound beamspace data. The respiratory phases may be used subsequently in a therapy or treatment (e.g., image-guided radiation-therapy (IGRT)) for precise dose-delivery. In certain implementations, vessel bifurcation may be tracked and respiration phases determined in real time using the tracked vessel bifurcations to facilitate respiration gating of the treatment or therapy.

Abstract: The techniques discussed herein relate to a reduced acoustic noise and vibration magnetic resonance imaging (MRI) acquisition. In certain implementations acoustic noise levels for one or more MRI pulse sequences are characterized and modified by limiting the frequencies and amplitudes of the gradient waveforms so as to produce less noise and vibration when the modified waveform is used during an MRI examination. In this manner, relatively low sound pressure levels can be attained.

Abstract: The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.

Abstract: The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.

Abstract: An RF coil array for use in MRI is disclosed, which includes a plurality of transmit coil elements and a plurality of RF power amplifiers. Each RF power amplifier is integrated with at least one transmit coil element for driving the at least one transmit coil element. An MRI transmit array is also disclosed, which includes a plurality of RF transmitters for generating a plurality of RF signals, the RF coil array above-mentioned, and a DC voltage source for providing a DC voltage to the plurality of transmit coil elements. The RF coil array further includes an RF shield for shielding the plurality of transmit coil elements from interacting with magnet cryostat and gradient coil elements. The plurality of RF power amplifiers are connected with respective RF transmitters and configured for power amplification of the RF signals from the respective RF transmitters, and the plurality of transmit coil elements are configured for transmitting respective amplified RF signals.

Abstract: A system and method for providing virtual real-time MRI-guidance for a biopsy outside of a conventional MRI scanner is described. MR images and ultrasound images of a region of a patient's body are simultaneously acquired during a pre-biopsy procedure. Respiratory states that the patient may experience during the biopsy are then determined from the acquired ultrasound images, and each respiratory state is associated with corresponding MR images. The MR images are indexed with their corresponding respiratory state. Ultrasound images are then acquired of the patient during a biopsy procedure. The respiratory state of the patient is determined from the ultrasound images, and the corresponding indexed MR images are displayed.

Abstract: The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.

Abstract: The present disclosure provides, in certain implementations, a rule-based or deep learning-based approach capable of assessing diagnostic utility of images in near real time with respect to acquisition. Correspondingly, an automated implementation of such an algorithm on the scanner would, in fact, emulate the doctor himself rating images in real time, and reduce the number of unneeded re-scans and recalls. In one aspect of the present invention it was found that diagnostic utility of an image is not an absolute measure, but instead depends upon the reading radiologist and the scan indication (i.e., the purpose of the scan). Therefore, adapting the threshold (probability of an imaging volume to be deemed good) as a function of reading radiologist and scan indication can result in decreasing the number of re-scans and recalls.

Abstract: The embodiments disclosed herein relate generally to magnetic resonance imaging systems and, more specifically, to the manufacturing of a gradient coil assembly for magnetic resonance imaging (MRI) systems. For example, in one embodiment, a method of manufacturing a gradient coil assembly for a magnetic resonance imaging system includes depositing a first layer comprising a base material onto a surface to form a substrate and depositing a second layer onto the first layer. The second layer may enable bonding between a conductor material and the substrate. The method also includes depositing a third layer onto the second layer using a consolidation process. The consolidation process uses the conductor material to form at least a portion of a gradient coil.