Abstract: Systems, methods, apparatuses, and computer program products for nonintrusive scanning of objects using x-ray electromagnetic radiation. One method may include adjusting, by a computed tomography scanning device, at least one operation parameter including a grey value threshold based upon a material composition of a scan target; and collecting, by the computed tomography scanning device, at least one x-ray frame of the scan target based upon the at least one adjusted operation parameter.

Abstract: Provided herein are methods, apparatuses, computer program products, and systems for anomaly detection using machine learning models. One method can include obtaining X-ray computed tomography (CT) scan data for a scan object of a predetermined object type; producing derived data from the X-ray CT scan data, wherein the derived data reveals at least one interior structure usable for anomaly detection in objects of the predetermined object type; inputting at least the derived data into a machine learning model, which has been trained using at least derived data produced from prior X-ray CT scan data for objects of the predetermined object type; receiving an output from the machine learning model that indicates that at least one anomaly has been detected for the scan object; and providing an output to a physical device based on the at least one anomaly having been detected for the scan object.

Abstract: Provided herein are methods, apparatuses, computer program products, and systems for seed quality analysis using computed tomography. One method can include obtaining computed tomography data representing a germination towel including seedlings; detecting likely seeds and candidate shoots and roots in the computed tomography data representing the germination towel; forming segments from the candidate shoots and roots; grouping the segments across a total volume of the computed tomography data into likely shoots and roots in accordance with relative proximity and alignment of the segments; assembling the likely seeds and the likely shoots and roots into likely seedlings; classifying the germination towel using a classifier trained to use at least the likely seedlings, as input; and providing a classification result from the classifying for the germination towel.

Abstract: An X-ray device includes an X-ray source, a scintillator, a single mirror, and a camera. The camera has a field-of-view around an optical axis of the camera. The single mirror fully contains the field-of-view as the field-of-view is folded by the single mirror toward the scintillator. The single mirror is positioned at an angle with respect to a plane that is normal to the optical axis at a point where the optical axis intersects the single mirror. The angle is decreased from forty-five degrees to reduce a volume of the folded field-of-view of the camera. The angle is greater than or equal to a threshold angle that prevents triple specular reflections of light between the single mirror and the scintillator.

Abstract: An X-ray device includes: an X-ray source configured to emit X-rays; a scintillator, the scintillator being configured to emit light in response to absorption of the X-rays; a detector; a frame enclosing the X-ray source and the detector; standoffs positioned on the frame; shielding panels comprising lead; one or more brackets with fasteners configured to attach to the standoffs; and exterior panels with fasteners configured to attach to the one or more brackets. The standoffs form a datum structure for the one or more brackets. Each of the standoffs has a length and a cross-sectional size, and each of the shielding panels includes holes having a cross-sectional size greater than the cross-sectional size of the standoffs by an amount that ensures the standoffs will pass through the holes. The length of the standoffs is greater than a maximum thickness possible for the given shielding panel due to variations in thickness.