Patent Description:
According to CT imaging, a narrow beam of x-rays is emitted towards a patient and detected on an opposite side of the patient while the emitter and detector are rotated around the patient. The detected signals are processed to generate cross-sectional images (i.e., "slices") of the patient. Successive slices may be combined to form a three-dimensional image that facilitates identification of internal structures and any tumors or other abnormalities.

CT images typically contain more information than conventional two-dimensional x-ray images. However, the acquisition of a CT image subjects a patient to significantly more radiation exposure than the acquisition of a conventional x-ray image. Consequently, a patient may have a condition which requires limiting or avoiding significant radiation exposure to a portion (or all) of the body, but the condition cannot be detected or is otherwise unknown prior to acquisition of a CT image.

In one example, and although standard practice is to avoid acquiring a CT image of a pregnant female, pregnancy might be unknown until it is detected in a CT image. At this point it is too late to avoid radiation exposure to the fetus. Current systems may attempt to estimate the radiation exposure in retrospect and take any suitable remedial actions.

Systems are desired to efficiently detect a patient condition and change CT imaging parameters based on the detected condition within a CT imaging workflow. Document <CIT> relates to background in that <CIT> discloses using a machine learning model for detecting metal objects in patients based on image scan data so as to use different scan trajectories in CT imaging that avoid image artifacts occurring due to the detected metal objects.

The present invention is defined by the enclosed claims.

The following description is provided to enable any person in the art to make and use the described embodiments and sets forth the best mode contemplated for carrying out the described embodiments. Various modifications, however, will remain apparent to those in the art.

Some embodiments use a trained classification model to detect a patient condition based on a radiograph acquired by a CT scanner prior to a CT scan. The radiograph may comprise a low-resolution two-dimensional image of a patient lying on a scanning bed and is typically used to determine the scanning range of a subsequent CT scan based on locations of bony structures and lungs, and/or to derive attenuation information for automatic exposure control. If the patient condition (e.g., pregnancy) is detected, the subsequent imaging process can be adapted accordingly (e.g., adjust scan area, select different imaging modality). Embodiments may therefore efficiently avoid undesirable radiation exposure in view of a patient condition that might not have been visually detectable within the radiograph. Advantageously, some embodiments train the classification model using a set of radiographs which are labeled (e.g., pregnant, not pregnant) by evaluating associated CT images for the given patient condition, as will be described below.

<FIG> is a block diagram of CT imaging system <NUM> according to some embodiments. Imaging system <NUM> comprises CT scanner <NUM> including x-ray source <NUM> for emitting x-ray beam <NUM> toward opposing radiation detector <NUM>. X-ray source <NUM> and radiation detector <NUM> are mounted on gantry <NUM> such that they may be rotated about a center of rotation of gantry <NUM> while maintaining the same physical relationship therebetween.

Patient <NUM> is positioned on bed <NUM> to place a portion of patient <NUM> between x-ray source <NUM> and radiation detector <NUM>. Next, x-ray source <NUM> and radiation detector <NUM> are moved to various projection angles with respect to patient <NUM> by using rotation drive <NUM> to rotate gantry <NUM> around cavity <NUM> in which patient <NUM> is positioned. At each projection angle, x-ray source <NUM> is powered by high-voltage generator <NUM> to transmit x-ray radiation <NUM> toward detector <NUM>. Detector <NUM> receives the radiation and produces a set of data (i.e., a raw image) for each projection angle, representing the attenuative properties of patient <NUM> from the perspective of the projection angle.

The width of beam <NUM> in the z-direction (along the length of the patient and cavity <NUM>) spans a few inches and therefore the set of data (i.e., a raw image) for each projection angle represents a slice of patient <NUM> taken perpendicular to the z-direction. By moving bed <NUM> in the z-direction, data representing other slices of patient <NUM> may be similarly acquired.

Radiation detector <NUM> may comprise any system to acquire an image based on received x-ray radiation. In some embodiments, radiation detector <NUM> uses a scintillator layer and solid-state amorphous silicon photodiodes deployed in a two-dimensional array. The scintillator layer receives photons and generates light in proportion to the intensity of the received photons. The array of photodiodes receives the light and records the intensity of received light as stored electrical charge.

In other embodiments, radiation detector <NUM> converts received photons to electrical charge without requiring a scintillator layer. The photons are absorbed directly by an array of amorphous selenium photoconductors. The photoconductors convert the photons directly to stored electrical charge.

System <NUM> may comprise any general-purpose or dedicated computing system. Accordingly, system <NUM> includes one or more processing units <NUM> configured to execute program code to cause system <NUM> to operate as described herein, and storage device <NUM> for storing the program code. A processing unit may comprise a processor, a processor core, or a processor thread. Storage device <NUM> may comprise one or more fixed disks, solid-state random access memory, and/or removable media (e.g., a thumb drive) mounted in a corresponding interface (e.g., a USB port).

Storage device <NUM> stores program code of control program <NUM>. One or more processing units <NUM> may execute control program <NUM> to determine imaging parameters, to rotate gantry <NUM>, to move bed <NUM>, to cause radiation source <NUM> to emit radiation at desired energies, and to control detector <NUM> to acquire CT data <NUM>. In this regard, system <NUM> includes gantry interface <NUM>, detector interface <NUM>, radiation source interface <NUM> and bed interface <NUM> for communication with corresponding elements of scanner <NUM>. System <NUM> may also receive input from terminal <NUM> which may be used to control image acquisition.

CT data <NUM> may be stored in DICOM or another data format. CT data <NUM> may be further associated with of acquisition details, including but not limited to imaging plane position and angle, imaging position, radiation source-to-detector distance, patient anatomy imaged, patient position, contrast medium bolus injection profile, x-ray tube voltage, image resolution and radiation dosage. Processing units <NUM> may execute control program <NUM> to reconstruct three-dimensional images volumes <NUM> from CT data <NUM> as is known in the art.

System <NUM> may operate scanner <NUM> to acquire radiographs <NUM> as is known in the art. A radiograph is a two-dimensional image representing a single perspective. To acquire a radiograph according to one example, source <NUM> is maintained at a fixed position with respect to gantry <NUM> and emits beam <NUM> toward detector <NUM>. Contemporaneously, bed <NUM> is moved to pass patient <NUM> lengthwise through the beam <NUM>.

A radiograph is typically acquired in the above manner prior to a CT scan to determine/confirm a location of patient anatomy with respect to CT scanner <NUM>. Acquisition of a radiograph delivers a much smaller radiation dose to the patient than that delivered during a CT scan.

Trained classification model <NUM> comprises executable program code implementing a trained machine learning algorithm. As will be described in detail below, a radiograph <NUM> is input to model <NUM> and model <NUM> outputs a patient condition in response. Based on the condition, a subsequent CT scan may be modified, aborted or executed. In one example, a radiograph of a patient is acquired in preparation for a subsequent CT scan. The radiograph is input to model <NUM>, and model <NUM> indicates that the patient is pregnant. In response to this indication, the planned CT scan is not performed.

CT data <NUM> and/or image volumes <NUM> may be provided to terminal <NUM> for display. Terminal <NUM> may comprise a display device and an input device coupled to system <NUM>. In some embodiments, terminal <NUM> is a separate computing device such as, but not limited to, a desktop computer, a laptop computer, a tablet computer, and a smartphone. Terminal <NUM> displays images received from system <NUM>, receives user input for controlling scanner <NUM> and system <NUM>, and transmits such user input to system <NUM>.

Each of scanner <NUM>, system <NUM> and terminal <NUM> may include other elements which are necessary for the operation thereof, as well as additional elements for providing functions other than those described herein.

Embodiments are not limited to a CT scanner as described above with respect to <FIG>. For example, embodiments may employ a dual-arm CT scanner using two radiation sources and corresponding detectors. Such systems may acquire CT data from two different projection angles substantially simultaneously.

<FIG> comprises a flow diagram of process <NUM> according to some embodiments. Process <NUM> and the other processes described herein may be performed using any suitable combination of hardware, software or other means. Software embodying these processes may be stored by any non-transitory tangible medium, including but not limited to a fixed disk, a DVD, a Flash drive, or a magnetic tape. Examples of these processes will be described below with respect to the elements of system <NUM>, but embodiments are not limited thereto.

Initially, at S210, a patient is positioned with respect to a CT scanner. The patient is typically positioned according to a predetermined imaging plan. Such positioning may include alignment of the patient with markers placed or projected on the CT scanner and/or the patient as is known in the art. Positioning of the patient may include positioning of any suitable imaging accessories, including but not limited to radiation-shielding devices, stabilization devices, etc..

A radiograph of the patient is acquired using the CT scanner at S220. As described above, the radiograph may be acquired by moving the patient past a stationary and operating radiation source and detector. The radiograph may be performed as part of the typical scanning workflow. The radiograph may be used to verify a position of the patient as is known in the art.

According to some embodiments, the radiograph is also input to a trained classification network at S230 to generate a classification. In the present example, the classification network has been trained to classify the input radiograph as pregnant or not pregnant. Embodiments are not limited to this condition. In some embodiments, the trained classification network inputs a probability for one or both of the potential classifications (e.g., <NUM>% pregnant; <NUM>% not pregnant).

At S240, it is determined whether the output classification indicates that the patient is pregnant. This determination may include evaluation of the one or more probabilities output by the classification model against various thresholds. For example, the patient may be considered pregnant at S240 only if the output probability associated with the classification pregnant is greater than <NUM>%.

If the determination at S240 is negative, the originally-planned CT scan is executed at S250. If the determination at S240 is positive, the planned CT scan is modified at S260. The modification may consist of aborting the CT scan entirely, of shielding the fetus, of reducing radiation delivered to the reproductive organs, and/or any combination of modifications. Advantageously, such modification allows avoidance of undesirable radiation exposure in view of a patient condition that might not have been visually detectable within the acquired radiograph.

<FIG> is a block diagram illustrating a process according to some embodiments. Patient <NUM> is positioned in an imaging position with respect to CT scanner <NUM> in preparation for a CT scan. CT scanner <NUM> operates its x-ray source and detector to acquire radiograph <NUM>. Radiograph <NUM> is then input to trained classification network <NUM> to generate classification <NUM>. Based on classification <NUM>, it is determined whether to continue with the CT scan, modify the CT scan and execute the modified CT scan, or abort the CT scan. If it is determined to execute the CT scan, the CT scan may result in reconstructed volume <NUM> as is known in the art.

<FIG> illustrates training of a classification model according to some embodiments. Generally, architecture <NUM> trains model <NUM> to implement a function. The training is based on training radiographs<NUM>-n <NUM> and corresponding ground truth labels<NUM>-n <NUM> determined based on associated CT volumes<NUM>-n <NUM>.

Model <NUM> may comprise any type of learning network that is or becomes known. Broadly, model <NUM> may comprise a network of neurons which receive input, change internal state according to that input, and produce output depending on the input and internal state. The output of certain neurons is connected to the input of other neurons to form a directed and weighted graph. The weights as well as the functions that compute the internal state can be modified via training as will be described below. Model <NUM> may comprise any one or more types of artificial neural network that are or become known, including but not limited to convolutional neural networks, recurrent neural networks, long short-term memory networks, deep reservoir computing and deep echo state networks, deep belief networks, and deep stacking networks.

Radiographs<NUM>-n <NUM> and CT volumes<NUM>-n <NUM> may be acquired from any one or more image volume repositories. Each radiographx corresponds to a CT volumex. Although embodiments are not limited thereto, it is assumed that radiographx and its corresponding CT volumex are acquired by a same CT scanner of a same patient relatively contemporaneously. In other words, a radiographx of a given patient may have been acquired by a given CT scanner prior to acquisition of CT volumex of the given patient by the given CT scanner. In order to increase robustness of the learned function, radiographs<NUM>-n <NUM> may be acquired by many different CT scanners of many different patients using many different CT scanning settings.

Observer <NUM> determines ground truth labels<NUM>-n <NUM> based on CT volumes<NUM>-n <NUM>. Observer <NUM> reviews each of CT volumes<NUM>-n <NUM> for the existence of a given condition and generates a corresponding label <NUM> indicating whether the CT volume exhibits the condition. As noted above, certain conditions may be easier to identify in a CT volume <NUM> than in its corresponding radiograph <NUM>. Observer <NUM> may comprise one or more humans and/or automated systems, such as a trained machine learning model.

During training, a batch of radiographs<NUM>-n <NUM> is input to model <NUM>. Model <NUM> operates according to its initial configuration to output a corresponding batch of inferred labels<NUM>-n <NUM>. Loss layer <NUM> determines a loss by comparing the batch of inferred labels<NUM>-n <NUM> with corresponding ones of ground truth labels<NUM>-n <NUM>. Generally, the determined loss reflects a difference between the batch of inferred labels<NUM>-n <NUM> and corresponding ones of ground truth labels<NUM>-n <NUM>.

As is known in the art, the loss is back-propagated to model <NUM> in order to modify model <NUM> in an attempt to minimize the loss. The process repeats and model <NUM> is iteratively modified in this manner until the loss reaches acceptable levels or training otherwise terminates (e.g., due to time constraints or to the loss asymptotically approaching a lower bound). At this point, model <NUM> is considered trained. Trained model <NUM> may be subjected to testing. If the performance of trained model is not sufficient, model <NUM> may be re-trained using different training parameters.

According to some embodiments, observer <NUM> may review CT volumes<NUM>-n <NUM> for the existence of several conditions. Each of ground truth labels<NUM>-n <NUM> may then provide indications of whether each of the several conditions is present in its corresponding CT volume. Model <NUM> may be configured and trained to output probabilities for each of the several conditions. During deployment of such a trained model <NUM>, the model is used to detect the presence of conditions based on an acquired radiograph as described with respect to process <NUM>, and a planned CT scan may be modified based on the presence of one or more conditions.

<FIG> illustrates computing system <NUM> according to some embodiments. System <NUM> may comprise a computing system to facilitate the design and training of a machine learning model as is known in the art. Computing system <NUM> may comprise a standalone system, or one or more elements of computing system <NUM> may be located in the cloud.

System <NUM> includes network adapter <NUM> to communicate with external devices via a network connection. Processing unit(s) <NUM> may comprise one or more processors, processor cores, or other processing units to execute processor-executable program code. In this regard, storage system <NUM>, which may comprise one or more memory devices (e.g., a hard disk drive, a solid-state drive), stores processor-executable program code of training program <NUM> which may be executed by processing unit(s) <NUM> to train a model as described herein.

Training program <NUM> may utilize node operations library <NUM>, which includes program code to execute various operations associated with node operations as defined in node operations library <NUM>. According to some embodiments, computing system <NUM> provides interfaces and development software (not shown) to enable development of training program <NUM> and generation of model definition <NUM>. Storage device <NUM> also includes training data consisting of radiographs <NUM>, CT volumes <NUM> and labels <NUM>.

Claim 1:
A system (<NUM>) comprising:
an imaging system (<NUM>); and
a computing system (<NUM>) to:
operate the imaging system to acquire a two-dimensional radiograph (<NUM>) of a patient (<NUM>);
input the radiograph (<NUM>) to a trained machine learning model (<NUM>) to generate a classification, wherein the model (<NUM>) is trained based on a plurality of radiographs and a classification corresponding to each radiograph, wherein the classification corresponding to a radiograph is determined based on a computed tomography volume generated contemporaneously to the radiograph;
if the classification indicates that the patient (<NUM>) does not have a first condition, operate the imaging system (<NUM>) to perform a computed tomography scan of the patient (<NUM>) based on the two-dimensional radiograph, wherein the first condition is pregnancy; and
if the classification indicates that the patient (<NUM>) has the first condition, determine to modify the computed tomography scan, including at least one of: aborting the computed tomography scan of the patient (<NUM>), shielding a fetus and/or reducing radiation delivered to reproductive organs of the patient (<NUM>).