Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating a plurality of emitters of a stationary distributed x-ray source unit to emit x-ray beams toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving attenuated x-ray beams with one or more detector arrays to form a sparse view projection dataset, where each attenuated x-ray beam generates a different view, and reconstructing an image from the sparse view projection dataset using a sparse view reconstruction method.

A method, system and computer readable storage media for segmenting individual intra-oral measurements and registering said individual intraoral measurements to eliminate or reduce registration errors. An operator may use a dental camera to scan teeth and a trained deep neural network may automatically detect portions of the input images that can cause registration errors and reduce or eliminate the effect of these sources of registration errors.

The present disclosure relates to a method and a system for computed tomography imaging. The method may comprise obtaining original data; obtaining a preprocessing result by preprocessing the original data; obtaining intensity of the artifact based on the preprocessing result; and updating a damaged channel or the air correction table based on the intensity of the artifact. Updating the air correction table may comprise: obtaining a first air correction table corresponding to a first temperature of detector; obtaining real-time temperature of detector; and obtaining a second air correction table corresponding to the real-time temperature based on the real-time temperature and the first air correction table.

An imaging apparatus and associated methods are provided to efficiently estimate scatter during multi-fraction treatments for improved quality and workflow. Estimated scatter from one fraction during a treatment course can be utilized during subsequent fractions, allowing for measurements with higher scatter-to-primary ratios. The quality of scatter estimates can be maintained, while workflow improves and dosage decreases. Scan configuration limits can be utilized to maintain a minimum level of scatter measurement quality. Patient information can be monitored to ensure that prior fraction scatter estimates are still applicable to current patient status.

In an embodiment of a motion correction method, a first virtual non-contrast X-ray image of a region under examination is determined based upon first spectral raw X-ray data associated with a first contrast distribution, via material decomposition. In addition, a second virtual non-contrast X-ray image of the region under examination is determined based upon second spectral raw X-ray data associated with a second contrast distribution, differing from the first contrast distribution, via material decomposition. Then the first virtual non-contrast X-ray image is registered with the second virtual non-contrast X-ray image to determine a transformation field between the two virtual non-contrast X-ray images. Finally, based upon the determined transformation field, first X-ray image data based on the first raw X-ray data is registered with second X-ray image data based on the second raw X-ray data. An X-ray imaging method, a motion correction device and an X-ray imaging system are also discussed.

Disclosed is a medical system (100, 300, 500, 700) comprising: a memory (128) storing machine executable instructions (130); a processor (122) configured for controlling the medical system; and a pilot tone system (106). The pilot tone system comprises a radio frequency system (108) comprising multiple transmit channels (110) and multiple receive channels (112). The multiple transmit channels are configured for each transmitting unique pilot tone (132) signals via multiple transmit coils. The multiple receive channels are configured for receiving multi-channel pilot tone data (134) via multiple receive coils. Execution of the machine executable instructions causes the processor to: transmit (200) multi-channel pilot tone signals by controlling at least a portion of the multiple transmit channels to transmit the unique pilot tone signals; acquire (202) multi-channel pilot tone data (134) by controlling at least a portion of the multiple receive channels to receive the multi-channel pilot tone data; and determine (204) a motion state (136) of the subject using the multi-channel pilot tone data.

An imaging method may include obtaining original imaging data of an object in a raw-data domain including original time of flight (TOF) information. The method may also include gating the original imaging data into a plurality of data sets in the raw-data domain. The method may also include determining a plurality of motion vector fields based on the plurality of data sets. The method may also include generating corrected imaging data in the raw-data domain by performing motion correction on at least one of the plurality of data sets based on the original TOF information and at least one corresponding MVF of the plurality of MVFs. The method may also include generating one or more target images of the object by performing, based on the corrected imaging data, image reconstruction.

Disclosed are devices and methods for measuring lung respiration volume including processor means for receiving a detected series of heart beats, measuring variability between a period of successive beats, identifying the start and finish of successive breaths by the maxima and minima in the period, identifying the amplitude of variability of period between successive breaths, and thereby determining a value for a measurement of an extent of lung respiration, and output means for generating the value for the measurement of the extent of lung respiration. The disclosed devices and methods have applications in different medical fields. The disclosed devices can be utilised as wearable devices, wherein the signals are generated and may be processed remotely or locally.

A method for evaluating image quality is provided. The method may include: obtaining an image, the image including a plurality of elements, each element of the plurality of elements being a pixel or voxel, each element having a gray level; determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; determining one or more sub-images of a region of interest by segmenting, based on the one or more thresholds, the image; and determining, based on the one or more sub-images of the region of interest, a quality index for the image.

Systems and techniques for determining degree of motion using machine learning to improve medical image quality are presented. In one example, a system generates, based on a convolutional neural network, motion probability data indicative of a probability distribution of a degree of motion for medical imaging data generated by a medical imaging device. The system also determines motion score data for the medical imaging data based on the motion probability data.