A transport system with curved track pair is constructed for multiple pulsed X-ray source-in-motion to perform fast digital tomosynthesis imaging. It includes a curved rigid track pair with predetermined curvature, a primary motor stage car loaded with X-ray sources and wheels loaded with tension or compression springs. The car is driven by primary motor mounted at base frame and an engaged gear mounted at the car. The car can carry heavy loads, travel with high precision and high repeatability at all installation orientations while motion vibration is minimal. It is also scalable to have a larger radius. Track angle span usually can be from about ten degrees to about 170 degrees. During imaging acquisition, X-ray sources can sweep precisely from one location to another. The car has enough clearance to move in its path without rubbing wheels on tracks. Better than 0.2 mm overall spatial precision can be achieved with the digital tomosynthesis imaging.

Computed tomography perfusion (CTP) is used in a method to identify cancerous lesions having genetic mutations and treat them accordingly. Also, CTP values are used to distinguish primary versus metastatic lesions. For example, pulmonary blood flow is identified as one biomarker for EGFR and KRAS genetic mutations in lung cancer, lesion having dual-input pulmonary blood flow exceeding a threshold (e.g., 103 ml/min/100 mL with sensitivity 100% and specificity 62%) are determined as having mutations. The CTP values are calculated using a lesion region-of-interest (ROI) placed to include the area of maximum perfusion intensity within the lesion base and surrounding blush, while avoiding regions of perfusion inhomogeneity (e.g., due to necrosis). In certain implementations, instead of a binary determination, the method can generate probabilities associated with respective alternatives (e.g., mutation/non-nutation and/or primary/secondary), and the method can use multivariable statistical analysis that incorporates patient and/or medical information in addition to CTP values.

A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.

Disclosed are methods for quantitatively predicting the severity of a tumor in a subject. In some embodiments, the methods further comprise selecting a course of therapy for the subject. In some embodiments, the tumor comprises is non-small cell lung cancer.

Medical imaging device- and display-invariant segmentation and measurement is provided. In various embodiments, a plurality of medical images is read from a data store. Metadata of each of the plurality of medical images is read. The metadata identifies an image acquisition device associated with each of the plurality of medical images. Based on the plurality of medical images and the metadata of each of the plurality of images, a learning system is trained to determine one or more image correction parameters. The one or more image correction parameters optimize segmentation of the plurality of medical images.

Evaluation of segmentation of medical imagery is provided. In various embodiments, a candidate segmentation of a medical image of an anatomical feature is received. The candidate segmentation is provided to a first trained classifier. An indication is received from the first trained classifier of the accuracy of the candidate segmentation based on one or more feature of the candidate segmentation. One or more prior segmentation of a prior medical image of the anatomical feature is received. The candidate segmentation and the one or more prior segmentation are provided to a second trained classifier. An indication is received from the second trained classifier of the accuracy of the candidate segmentation based on one or more feature of the one or more prior segmentation.

Computed tomography perfusion (CTP) is used in a method to identify cancerous lesions having genetic mutations and treat them accordingly. Also, CTP values are used to distinguish primary versus metastatic lesions. For example, pulmonary blood flow is identified as one biomarker for EGFR and KRAS genetic mutations in lung cancer, lesion having dual-input pulmonary blood flow exceeding a threshold (e.g., 103 ml/min/100 mL with sensitivity 100% and specificity 62%) are determined as having mutations. The CTP values are calculated using a lesion region-of-interest (ROI) placed to include the area of maximum perfusion intensity within the lesion bass and surrounding blush, while avoiding regions of perfusion inhomogeneity (e.g., due to necrosis). In certain implementations, instead of a binary determination, the method can generate probabilities associated with respective alternatives (e.g., mutation/non-nutation and/or primary/secondary), and the method can use multivariable statistical analysis that incorporates patient and/or medical information in addition to CTP values.

A system for determining whether a dataset including a plurality of cross-sectional images includes a predetermined feature is provided. A first AI receives a dataset including a plurality of cross-sectional images, and analyses the dataset to identify a subset of cross-sectional images of the dataset capable of including the predetermined feature A second AI model receives a first cross-sectional image from the subset, analyses the first cross-sectional image to determine whether the first cross-sectional image includes the predetermined feature, and outputs an indication of whether the first cross-sectional image includes the predetermined feature. A processor is configured to obtain the output from the second AI model, and based on the output from the second AI model indicating that the first cross-sectional image includes the pre-determined feature, determine that the dataset includes the predetermined feature.

An X-ray imaging system using multiple pulsed X-ray source pairs in-motion to perform highly efficient and ultrafast 3D radiography is presented. The sources move simultaneously on arc trajectory at a constant speed as a group. Each individual source also moves rapidly around its static position in a small distance, but one moves in opposite direction to the other to cancel out linear momentum. Trajectory can also be arranged at a ring structure horizontally. In X-ray source pairs each moves in opposite angular direction to another to cancel out angular momentum. When an individual X-ray source has a speed that equals to group speed but an opposite linear or angular direction, the individual X-ray source is triggered through an external exposure control unit. This allows the source to stay relatively standstill during activation. 3D data can be acquired with wider view in shorter time and image analysis is real-time.