Patent Description:
Time-of-flight (TOF) nuclear imaging, such as TOF positron emission tomography (PET), is used to construct two-dimensional and/or three-dimensional images of structures within a patient. TOF PET (and other TOF nuclear imaging) detects coincidence events representing near simultaneous detection of annihilation photon pairs using a pair of detectors. The TOF PET system determines the difference in time between the detection of the two photons (e.g., the time of flight) and localizes the point of origin of the annihilation event that occurred between the two detectors.

PET imaging of individual organs can include at-rest scans and/or stress scans of the target organ. During both at-rest and stress scanning, periodic and non-periodic motion of the organ can result in image blur or defects. Periodic motion includes recurring, expected motion of the organ, such as a heart-beat, respiratory motion, etc. A known method to take into account periodic motion is gating of acquired PET data according to cardiac and respiratory phases as disclosed in <NPL>). Non-periodic motion, which often occurs during stress cans, includes unexpected or sudden and/or non-repeating motion, such as movement of a patient during a scan, relaxation of one or more muscles (e.g., creep), coughing, etc. In current systems, non-periodic motion can result in unusable (or non-diagnostic) images due to motion blur or changes in location.

In various embodiments, a method according to claim <NUM>, a system according to claim <NUM> and a non-transitory computer readable medium according to claim <NUM> is disclosed.

The following will be apparent from elements of the figures, which are provided for illustrative purposes and are not necessarily drawn to scale.

This description of the exemplary embodiments is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description.

Various embodiments of the present disclosure address the foregoing challenges associated with generating diagnostic PET images from data sets having non-periodic movement. In some embodiments, a plurality of four-dimensional volumetric images are generated from imaging data for a predetermined imaging period. Each four-dimensional volumetric image includes target tissue. A motion vector offset is determined for each of the plurality of four-dimensional volumetric images. The motion vector offsets are determined using target tracking data generated for the target tissue over a time period associated with the four-dimensional volumetric image. Corrected imaging data is generated from the first set of imaging data and the motion vector offsets and at least one static reconstruction image including the target tissue during the imaging period is generated from the corrected imaging data.

<FIG> illustrates one embodiment of a nuclear imaging detector <NUM>. The nuclear imaging detector <NUM> includes a scanner for at least a first modality <NUM> provided in a first gantry 116a. The first modality <NUM> includes a plurality of detectors <NUM> configured to detect an annihilation photon, gamma ray, and/or other nuclear imaging event. In various embodiments, the first modality <NUM> is a PET detector. A patient <NUM> lies on a movable patient bed <NUM> that may be movable between a gantry. In some embodiments, the nuclear imaging detector <NUM> includes a scanner for a second imaging modality <NUM> provided in a second gantry 116b. The second imaging modality <NUM> can be any suitable imaging modality, such as, for example, computerized tomography (CT), single-photon emission tomography (SPECT) and/or any other suitable imaging modality.

Scan data from the first modality <NUM> is stored at one or more computer databases <NUM> and processed by one or more computer processors <NUM> of a computer <NUM>. The graphical depiction of computer <NUM> in <FIG> is provided by way of illustration only, and computer <NUM> may include one or more separate computing devices. The imaging data sets can be provided by the first modality <NUM> and/or may be provided as a separate data set, such as, for example, from a memory coupled to the computer <NUM>. The computer <NUM> can include one or more processing electronics for processing a signal received from one of the plurality of detectors <NUM>.

<FIG> illustrates a plurality of static images 200a-200e of a target organ <NUM>, such as a heart. The plurality of static images 200a-200e are generated for a predetermined imaging period, for example, using the nuclear imaging detector <NUM>. During the PET imaging procedure, movement, discomfort, and/or physiological reactions of the patient can result in non-periodic movement within the data. When non-periodic movement is present, significant artefacts and/or motion blur can occur. For example, the plurality of static images 200a-200e include significant motion blur caused by the non-periodic motion of the patient during imaging. As shown in <FIG>, a polar image <NUM> of the target organ <NUM> generated from the plurality of static images 200a-200e also includes significant artefacts 206a-206b as a result of the non-periodic motion. The non-periodic motion results in static images 200a-200e and a polar image <NUM> of a non-diagnostic quality, i.e., the images 200a-200e, <NUM> cannot be used for diagnosing defects or other issues in the target organ <NUM>, resulting in the need to do additional diagnostic imaging of the patient and exposing the patient to additional radiation and discomfort.

In some embodiments, systems and methods of motion correction are applied to PET imaging data to correct motion blue and/or artefacts introduced by non-periodic movement. <FIG> is a flowchart <NUM> illustrating a method of non-periodic motion correction for PET images, in accordance with some embodiments. The method <NUM> is configured to identify and track the position of a target organ <NUM>, such as a heart, during reconstruction of diagnostic images to allow removal and/or minimization of non-periodic movement and related artefacts. The method <NUM> allow generations of diagnostic images from image data that traditionally produces non-diagnostic images, such as, for example, the PET image data associated with the static images 200a-200e in <FIG>.

At step <NUM>, PET imaging data is received by a system, such as, for example, the computer <NUM>. The imaging data can include PET image data for each detection event detected by an imaging modality, such as the first modality <NUM>, during a nuclear imaging procedure. In some embodiments, the imaging data is generated and provided to the system in real-time (e.g., immediately provided from the imaging modality to the system). In other embodiments, the imaging data is generated by the imaging modality during an imaging period and is processed by the system during a later image generation period. In some embodiments, the image data is provided in a listmode format, although it will be appreciated that the data can be provided in any format readable by the system and converted into a listmode format.

At step <NUM>, a plurality of volumetric images are generated directly from the listmode data <NUM>. For example, as illustrated in <FIG>, the listmode data <NUM> includes a plurality of data points each including a first detector identifier (A), a second detector identifier (B), and time-of-flight (TOF), i.e., {(A<NUM>, B<NUM>, TOF<NUM>); (A<NUM>, B<NUM>, TOF<NUM>). (An, Bn, TOFn)}. The first detector identifier (A) and the second detector identifier (B) correspond to detectors 404a, 404b that each detect an annihilation event. Using the detector identifiers and the time-of-flight, the system (such as computer <NUM>) identifies a position <NUM>, or voxel, for the annihilation event. The system generates static volumetric images including each annihilation event in the listmode data <NUM> over a predetermined diagnostic period, e.g., a four-dimensional volumetric images 408a, 408b (or frames). Each four-dimensional volumetric image 408a, 408b includes three spatial dimensions (x, y, z) and a temporal dimension (t) corresponding to the predetermined time period selected from the predetermined diagnostic period.

In some embodiments, the temporal dimension t includes <NUM> second incremental intervals, although it will be appreciated that shorter and/or longer temporal dimensions can be selected. For example, in some embodiments, a first four-dimensional volumetric image is generated for a first time period (e.g., <NUM>-<NUM> second), a second four-dimensional volumetric image is generated for a second time period (e.g., <NUM>-<NUM> seconds), and an nth four-dimensional volumetric image is generated for an nth time period (e.g., (n-<NUM>)-n seconds). In some embodiments, the total number of volumetric images generated is equal to the total imagine period (ttotal) divided by the temporal dimension increment t, e.g., <NUM> second, <NUM> seconds, <NUM> seconds, etc. The predetermined diagnostic period can include an entire imaging procedure and/or a portion of an imaging procedure excluding non-diagnostic imaging such as an ingestion and/or diffusion period prior to a tracer being distributed to target tissue.

At step <NUM>, a dynamic image of the target tissue is generated for the predetermined diagnostic period. A single continuous dynamic image is generated for the entire predetermined diagnostic period and/or a plurality of dynamic images for portions of the predetermined diagnostic period can be generated. In some embodiments, the dynamic image is generated using imaging data generated by a second imaging modality <NUM>, such as a CT imaging modality. The second set of imaging data is generated simultaneously with the set of PET imaging data. The position of a target tissue is identified within the dynamic image using one or more known target identification processes. For example, in various embodiments, the identification of the target tissue can include, but is not limited to, organ finding using a matched filter for acquisition and normalized cross-correlation for tracking. In some embodiments, a center of the target tissue is identified within the dynamic image.

At step <NUM>, a motion vector is generated for each four-dimensional volumetric image 408a, 408b using target tracking data generated from the dynamic image (or portion of the dynamic image) corresponding to the temporal dimension t of the selected four-dimensional volumetric image 408a, 408b. For example, in some embodiments, motion and position information from the dynamic image is used to identify the target tissue <NUM> and/or a center point <NUM> of the target tissue <NUM> within each four-dimensional volumetric image 408b, as shown in <FIG>. Although embodiments are illustrated and discussed including translational tracking of target tissue <NUM>, it will be appreciated that any type of movement, such as translational, rotational, skew, non-rigid transformations, etc. may be tracked and used to generate a motion vector.

Motion and position information generated from the dynamic image is referenced to each image in the plurality of volumetric images to generate a set of motion vectors for the selected diagnostic period within the listmode data set <NUM>. <FIG> is a chart <NUM> illustrating motion vector offsets <NUM> for the listmode data <NUM>. The greater the offset <NUM>, the greater the non-periodic movement of the target tissue <NUM> during the temporal period t of the corresponding four-dimensional volumetric image 408b. In some embodiments, a non-diagnostic portion <NUM> of the listmode data <NUM> corresponding to ingestion and diffusion of a tracer molecule is ignored (e.g., not used for diagnostic imaging), although it will be appreciated that additional target tracking and/or diagnostic procedures may be performed that include the ingestion and/or diffusion periods. For example, during early phases of a cardiac scan, the signature of a target organ (i.e., target tissue) changes. In some embodiments, motion tracking through the changes in the target tissue can be tracked and motion correction applied according to the embodiments disclosed herein.

At step <NUM>, corrected data including axial plane shifts (or other motion correction shifts) corresponding to the motion vector offsets <NUM> is generated for the listmode data <NUM>. In some embodiments, the plane shifts correspond to discrete shift values on a predetermined axis, such as a z-axis. <FIG> is a chart <NUM> illustrating a plurality of discrete shifts <NUM> applied to the listmode data <NUM> during generation of corrected data from the listmode data <NUM>. For example, in some embodiments, a discrete shift value is applied to one or more voxels within the temporal period t to correct a position of the voxel during grouping and reconstruction. In some embodiments, the corrected data is generated using only a predetermined diagnostic portion <NUM> of the imaging period. In some embodiments, pre-processing of the listmode data <NUM> can be applied prior to generation of the corrected imaging data, such as, for example, correction for random coincidences, estimation and subtraction of scattered photons, detector dead-time correction, and/or detector-sensitivity correction.

At step <NUM>, one or more reconstructed static images are generated from the corrected imaging data. The reconstruction can be generated according to known methods for generating PET diagnostic images from the corrected imaging data, such as, for example, filtered back projection, statistical-likelihood based-approaches (e.g., Shepp-Vargi construction), Bayesian constructions, and/or any other suitable method of generating static PET reconstruction images from the corrected imaging data.

In some embodiments, the method <NUM> results in the removal of artefacts, such as artefacts 206a-206b illustrated in <FIG>, and allows generation of diagnostic-quality reconstructed images from traditionally non-diagnostic listmode data <NUM>. For example, the listmode data <NUM> includes significant non-periodic motion, such as, for example, as highlighted by box <NUM> in <FIG>. <FIG> illustrates a plurality of static images 502a-502c of the target tissue 510a generated from the listmode data <NUM> using traditional methods. As shown in <FIG>, the static images 502a-502c have significant motion blur and artefacts such that the images are of non-diagnostic quality and cannot be used for patient diagnosis. <FIG> illustrates reconstructions of the target tissue 510b generated from the listmode data <NUM> using the method <NUM> of motion correction discussed in conjunction with <FIG>. As can be seen in <FIG>, the motion blur and artefacts of each static image 504a-504c has been eliminated and/or minimized as compared to the static images 502a-502c generated using a non-motion corrected data. The motion corrected static images 504a-504c are of diagnostic quality and can be used in patient diagnosis.

Similarly, <FIG> is a chart <NUM> illustrating motion vector offsets <NUM> for listmode PET data including non-periodic organ creep or movement during a diagnostic period 420a, for example, as highlighted by box <NUM>. Organ creep occurs due to relaxation of one or more muscles during an imaging period. As the one or more muscles relax, the position of the organ within the patient shifts. This movement is non-periodic and results in distortion of a reconstructed image due to the change in position of the organ during imaging. <FIG> illustrates a plurality of static images 522a-522c of target tissue 520a generated by a traditional reconstruction from the listmode data associated with <FIG> using traditional methods. As shown in <FIG>, the traditional reconstruction produces static images having artefacts due to organ creep of the target tissue 520a. Although the images are of diagnostic quality, artefacts in the images 522a-522c can result in incorrect or missed diagnosis. <FIG> illustrates a plurality of static images 524a-524c of the target tissue 520b generated from the listmode PET data of chart <NUM> according to the methods disclosed herein. As shown in <FIG>, the artefacts of the traditional static images 522a-522c are removed, the edges of the target tissue 520b are more defined, and the diagnostic quality of the images 524a-524c is increased over a traditional static image 522a-522c.

<FIG> illustrate the scan data of <FIG>, respectively, after undergoing a motion correction method as disclosed herein. As shown in <FIG>, the polar image <NUM> generated from the plurality of motion corrected static images 208a-208e does not contain any of the defects 206a-206b included in the original polar image <NUM>. By applying the methods and systems disclosed herein, a diagnostic images 208a-208e, <NUM> can be generated from data that traditionally generated only non-diagnostic images.

The apparatuses and processes are not limited to the specific embodiments described herein. In addition, components of each apparatus and each process can be practiced independent and separate from other components and processes described herein.

Claim 1:
A computer-implemented method, comprising:
receiving (<NUM>) a first set of imaging data (<NUM>) including a plurality of annihilation events detected during an imaging period, the first set of imaging data (<NUM>) comprising a listmode data set;
generating (<NUM>) a plurality of four-dimensional volumetric images (408a, 408b) from the first set of imaging data (<NUM>) for the imaging period, wherein each four-dimensional volumetric image (408a, 408b) includes a target tissue (<NUM>);
determining (<NUM>) at least one motion correction for each of the plurality of four-dimensional volumetric images (408a, 408b), wherein the at least one motion correction is determined using target tracking data generated for the target tissue (<NUM>) over a time period associated with the four-dimensional volumetric image (408a, 408b);
generating (<NUM>) corrected imaging data from the first set of imaging data (<NUM>) and the at least one motion correction; and
generating (<NUM>) at least one static reconstruction image (504a, 504b, 504c) including the target tissue (510b) during the imaging period from the corrected imaging data.