Systems and methods for assessing pulmonary ventilation and perfusion gradients - Patents.com

Abstract

A system and method for quantifying pulmonary ventilation and perfusion gradients in a subject's lungs, wherein local Hounsfield density histograms are generated from computed tomography data and each local histogram is cross-correlated with a global Hounsfield density histogram at a number of different shift values, and a final shift value is determined for each local histogram based on the correlation values ​​obtained by the cross-correlation.

Description

[Technical field]

[0001] The present invention relates to the field of evaluating computed tomography (CT) images. [Background technology]

[0002] Gravity can create pulmonary ventilation and perfusion gradients between the ventral and dorsal regions of the lungs, especially in subjects who have been lying down for extended periods of time.

[0003] The potential adverse effects that may be caused by this gradient should be taken into consideration when treating patients with acute respiratory syndrome (ARDS), which is often caused by infectious lung diseases such as COVID-19, by applying mechanical ventilation while the patient is in the prone position in addition to the standard supine position.

[0004] However, the presence and magnitude of the gravity-induced gradient can vary widely between patients. Some diseases increase or decrease the magnitude of this gradient. For example, patients with vascular disorders associated with systemic sclerosis exhibit a low gravity-dependent attenuation effect due to reduced elastic compliance of the pulmonary arterial tree. Some diffuse lung diseases (e.g., hypersensitivity pneumonitis, cellular nonspecific interstitial pneumonia, and subacute diffuse alveolar injury) exhibit a subtle and relatively uniform increase in pulmonary attenuation that can overlap with a gravity-induced gradient.

[0005] Pulmonary ventilation and perfusion gradients in patients can be seen in three-dimensional images of chest CT scans as slight deviations in the overall Hounsfield density between the ventral and dorsal lung regions (with the dorsal regions having higher Hounsfield density when the subject is in the supine position), however the effect is difficult to objectively quantify.

[0006] Furthermore, the lung parenchyma surrounds normal anatomical structures (blood vessels, bronchi, lymph nodes, heart, etc.). In patients with pulmonary disease, lesions, pleural effusion, atelectasis, mesothelioma, and scars can be embedded within the lung parenchyma. This "anatomical noise" means that there are much larger variations in Hounsfield density in CT images of the lung than those caused by pulmonary ventilation and perfusion gradients. Therefore, simple quantitative measures such as regional mean Hounsfield density cannot be used to provide information about ventral-dorsal gravity effects.

[0007] Treatment decisions for patients with acute respiratory syndromes often need to be made quickly in emergency situations, when there is insufficient time to make careful manual measurements to determine information about pulmonary ventilation and perfusion gradients.

[0008] Thus, there is a need for improved information regarding pulmonary ventilation and perfusion gradients between the ventral and dorsal regions of a subject's lungs.

[0009] US Patent Application Publication No. 2021 / 065361 discloses a method and system for determining areas of dense lung parenchyma in lung images.

[0010] International Patent Application Publication No. WO2020 / 231904 discloses a method for imaging the lungs of a patient.

[0011] The article "Volumetric xenon-CT imaging of conventional and high-frequency oscillatory ventilation" by Mulreany, DG et al., Academic Radiology, 16(6):718-725, June 1, 2009, discloses a method for obtaining high-resolution volumetric ventilation maps of the lungs.

[0012] Kircher, Michael et al., "Regional lung perfusion analysis in experimental ARDS by electrical impedance and computed tomography," IEEE Transactions on Medical Imaging, 40(1):251-261, September 21, 2020, discloses a method for detecting pulmonary diffusion capacity based on indicator-enhanced electrical impedance tomography.

[0013] The article by Gunnar, Elke et al., “Quantification of ventilation distribution in regional lung injury by electrical impedance tomography and xenon computed tomography”, Physiological Measurement, 34(10):1303-1318, September 11, 2013, discloses an electrical impedance tomography-based assessment of regional ventilation in pathological conditions. Summary of the Invention [Problem to be solved by the invention]

[0014] There is a need for improved information regarding pulmonary ventilation and perfusion gradients between the ventral and dorsal regions of a subject's lungs. [Means for solving the problem]

[0015] The invention is defined by the claims.

[0016] According to an example according to an aspect of the present invention, a processing system for assessing pulmonary ventilation and perfusion gradients of at least one lung of a subject is provided.

[0017] The processing system is configured to: receive CT imaging data including a representation of at least one lung of the subject; perform segmentation on the CT imaging data to identify the representation of the at least one lung of the subject; process the CT imaging data to generate a plurality of local histograms of a Hounsfield density frequency distribution, where each of the plurality of local histograms represents a different region of the at least one lung represented in the CT imaging data; generate a global histogram of the Hounsfield density frequency distribution for the at least one lung of the subject; and generate, for each of the plurality of local histograms, a plurality of shifted local histograms, each of which is a shifted version of the local histogram by a different shift value, cross-correlating each shifted local histogram with the global histogram to generate a corresponding plurality of correlation values, and processing the shift values ​​and correlation values ​​to generate a final shift value; where a total range of magnitudes of the final shift values ​​represents a magnitude of a pulmonary ventilation and perfusion gradient.

[0018] The inventors have recognized that pulmonary ventilation and perfusion gradients can be quantified by shift values ​​in Hounsfield units from cross-correlated local histograms. Local histograms in regions with higher density will typically have a higher correlation with the global histogram for shifts in a first direction (i.e., positive shift values), while local histograms in regions with lower density will typically have a higher correlation with the global histogram for shifts in a second direction opposite the first direction (i.e., negative shift values).

[0019] The overall range of magnitude of the final shift values ​​represents the magnitude of the pulmonary ventilation and perfusion gradient, while the direction in which the range of magnitude of the final shift values ​​is greatest represents the direction of the gradient. The difference in magnitude between the final shift values ​​of adjacent regions represents the local gradient between those regions.

[0020] The CT imaging data can be two-dimensional or three-dimensional imaging data, with three-dimensional imaging data providing improved accuracy and statistical power over two-dimensional imaging data.

[0021] In some examples, the final shift value for each of the multiple local histograms is the shift value that corresponds to the correlation value having the maximum value among the multiple correlation values ​​for that local histogram.

[0022] The shift value that corresponds to the maximum histogram cross-correlation (ie, the shift value at which the correlation value is greatest) can be considered to best represent the difference between the local and global histograms.

[0023] In some examples, the final shift value of each of the multiple local histograms is a correlation weighted average shift value.

[0024] Using the correlation weighted average shift value as the final shift value improves robustness to noise and outliers.

[0025] In some examples, the processing system is configured to provide a visualization of the final shift value in a user interface.

[0026] Visualization of the shift values ​​allows the clinician to immediately see the presence, amount and distribution of pulmonary ventilation and perfusion gradients, allowing the clinician to take this into account during treatment.

[0027] The visualization also allows the clinician to detect the presence of attenuation differences due to disease rather than gravity, as the visualization reveals local attenuation gradients that are not aligned with the direction of gravity.

[0028] A negative final shift value may be represented by a first color and a positive final shift value may be represented by a second, different color.

[0029] By using different colors for positive and negative shift values, the direction of the gradient becomes immediately apparent since that is the direction of color change.

[0030] In some examples, the visualization of the final shift value is generated by: for each local histogram, generating a visualization of the maximum correlation value for that local histogram; and generating an overlay representing the final shift value on the visualization of the maximum correlation value.

[0031] This recognizes that the correlation value at which the cross-correlation is highest also provides valuable clinical information. Typical parenchymal regions will have a higher maximum correlation value than diseased regions where cross-correlation with the global histogram produces lower correlation values ​​at any shift value. Diseased regions include lesions, exudates, atelectasis, mesothelioma, etc.

[0032] By overlaying a representation of the final shift value onto the visualization of the maximum correlation value, the clinician has all this information from a single image, allowing the clinician to make rapid treatment decisions.

[0033] The visualization can use different effects to represent the final shift value and the maximum correlation value, for example a grey value visualization can be used to represent the maximum correlation value with lighter shading representing higher maximum correlation values, and a color scale overlay can be used to represent the final shift value. The shift value may be within a predetermined range, for example between -200 Hounsfield units and +200 Hounsfield units.

[0034] The multiple local histograms can include lateral and / or axial (ie, cranio-caudal) ray-wise histograms.

[0035] For prone or supine subjects, the lateral and axial directions are perpendicular to the direction of gravity, so the effect of gravity is generally lowest in these directions, and therefore the systematic mutual shifts between different histograms in either the lateral or axial directions can be used to quantify and visualize the gravity-dependent gradient.

[0036] If the results are presented as a sagittal overview image, horizontal line histograms can be used as the local histograms for improved visual resolution and smoothness.

[0037] For three-dimensional assessment of pulmonary ventilation and perfusion gradients, both horizontal and axial linear histograms can be used as the local histograms, for example, a three-dimensional visualization of the final shift values ​​can be generated by cross-correlating both linear histograms with the global histogram.

[0038] The multiple local histograms may include two-dimensional patch-wise histograms in the coronal plane.

[0039] For prone or supine subjects, the coronal plane is perpendicular to the direction of gravity. Patch-wise histograms in the coronal plane may provide the most visually pleasing results for results presented in a coronal view.

[0040] In some examples, each of the multiple local histograms is generated by: generating multiple sub-local histograms, each representing a different sub-region of at least one lung region corresponding to the local histogram; and accumulating the sub-local histograms into the local histogram.

[0041] When a local histogram includes multiple partial local histograms, the partial local histograms have a finer resolution than the local histograms, for example, the partial local histograms can be line histograms (e.g., lateral and / or axial) or two-dimensional patch histograms (e.g., coronal).

[0042] By first generating histograms at a finer resolution than is required (e.g., the finest resolution possible), and then accumulating these histograms into a histogram with the desired resolution, local histograms of various resolutions can be efficiently generated as required by the user.

[0043] For example, if a user determines that the resolution of a visualization generated based on cross-correlating local histograms does not provide enough useful information, the user can provide user input requesting a visualization at a different resolution. In another example, a user may find multiple visualizations at different resolutions useful. These can be efficiently generated by accumulating partial local histograms into a new local histogram according to the required resolution.

[0044] In some examples, a global histogram is generated by accumulating the local histograms into a global histogram.

[0045] A system for evaluating at least one lung of a subject is also proposed, comprising: a CT scanner configured to generate CT imaging data; and a processing system as described above configured to receive the CT imaging data from the CT scanner.

[0046] The processing system can be configured to generate a visualization of the final shift values, the system further comprising a user interface configured to receive and display the generated visualization of the final shift values ​​from the processing system.

[0047] According to another aspect of the invention, there is provided a computer-implemented method for assessing pulmonary ventilation and perfusion gradients of at least one lung of a subject, the computer-implemented method comprising: receiving CT imaging data including a representation of at least one lung of the subject; performing segmentation on the CT imaging data to identify the representation of the at least one lung of the subject; processing the CT imaging data to generate a plurality of local histograms of Hounsfield density frequency distribution, each of the plurality of local histograms representing a different region of the at least one lung represented in the CT imaging data; generating a global histogram of Hounsfield density frequency distribution of the at least one lung of the subject; and, for each of the plurality of local histograms, generating a plurality of shifted local histograms, each of which is a shifted version of the local histogram by a different shift value, cross-correlating each shifted local histogram with the global histogram to generate a corresponding plurality of correlation values, and processing the shift values ​​and correlation values ​​to generate a final shift value, where a total range of magnitudes of the final shift values ​​represents a magnitude of the pulmonary ventilation and perfusion gradient.

[0048] A computer program product is also proposed, which comprises computer program code means which, when executed on a computing device having a processing system, causes the processing system to perform all of the steps of the method described above.

[0049] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0050] For a better understanding of the present invention and to show more clearly how the same may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings in which: [Brief description of the drawings]

[0051] [Figure 1]FIG. 1 illustrates a system for evaluating at least one lung of a subject, according to one embodiment of the present invention. [Diagram 2] FIG. 2 shows an exemplary image generated from CT imaging data of the lungs of a COVID-19 subject. [Diagram 3] FIG. 3 illustrates a computer-implemented method for evaluating at least one lung of a subject, according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0052] The present invention will now be described with reference to the drawings.

[0053] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the devices, systems and methods, are for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects and advantages of the devices, systems and methods of the present invention will become better understood from the following description, appended claims and accompanying drawings. It should be understood that the same reference numerals are used throughout the drawings to indicate the same or similar parts.

[0054] In accordance with the inventive concept, a system and method are proposed for quantifying pulmonary ventilation and perfusion gradients in a subject's lungs. Local Hounsfield density histograms are generated from computed tomography data, and each local histogram is cross-correlated with a global Hounsfield density histogram at a number of different shift values. A final shift value is determined for each local histogram based on the correlation values ​​obtained by the cross-correlation.

[0055] Each embodiment is based at least in part on the recognition that pulmonary ventilation and perfusion gradients can be represented by the Hounsfield unit shift values ​​whose local histograms most closely correlate with the global histogram.

[0056] Exemplary embodiments may be used, for example, in computed tomography systems, in particular PACS and workstation reading modules (eg Philips Intellispace-Portal, -Pacs and Illumeno), as well as in intensive care and ventilator care.

[0057] 1 illustrates a system 100 for evaluating at least one lung of a subject 110, according to an embodiment of the present invention. The system includes a computed tomography (CT) scanner 120, a processing system 130, and optionally a user interface 140. The processing system 130 is itself an embodiment of the present invention.

[0058] The CT scanner 120 may be any suitable CT scanner. A conventional CT scanner includes an X-ray radiation generator mounted on a rotatable gantry or C-arm opposite one or more integrated detectors. The X-ray generator rotates fully or partially around an examination region located between the X-ray generator and one or more detectors, emitting (usually polychromatic) radiation that traverses the examination region and subjects and / or objects placed within the examination region. The one or more detectors detect the radiation that traverses the examination region and generates signals (or projection data) indicative of the examination region and subjects and / or objects placed within the examination region. The projection data represents raw detector data and can be used to form projection sinograms, the latter being a visual representation of the projection data captured by the detectors.

[0059] A reconstructor is further used to process the projection data to reconstruct a volumetric image of the subject or object. The volumetric image is composed of a plurality of cross-sectional image slices, each of which is generated from the projection data via a tomographic reconstruction process, such as application of a filtered backprojection algorithm. The reconstructed image data is effectively the inverse Radon transform of the raw projection data.

[0060] The CT scanner 120 acquires / generates CT imaging data 125 including a representation of at least one lung of the subject 110. The CT imaging data may be any imaging data capable of assessing gravity-dependent pulmonary ventilation and perfusion gradients in at least one lung. In some examples, the CT imaging data may be two-dimensional imaging data including a component in the direction of gravity. When the subject is in a supine or prone position, the direction of gravity is parallel or nearly parallel to the anterior-posterior (y-) direction. Thus, the CT imaging data may include two-dimensional transverse or axial slices through the subject's chest. In other examples, the CT imaging data may be three-dimensional (e.g., a full chest scan).

[0061] The processing system 130 receives the CT imaging data 125 from the CT scanner 120 and performs segmentation on the CT imaging data to identify a representation of at least one lung of the subject 110. Any suitable segmentation method may be used to identify the representation of the at least one lung, such as voxel-based segmentation methods, mesh model-based, and AI-based segmentation methods (e.g., using one or more convolutional neural networks or other machine learning methods). The segmentation may include separating / identifying (the boundaries of) the left lung, right lung, or both lungs of the subject.

[0062] The processing system 130 processes the CT imaging data 125 to generate multiple local histograms of Hounsfield density frequency distribution. A histogram is a data structure that identifies (for a group of data values) the frequency of each data value within the group. In the present context, a group of data values ​​is a group of Hounsfield unit values ​​(e.g., of different pixels).

[0063] Each of the multiple local histograms represents a different region of the at least one lung represented in the CT imaging data identified based on the results of the segmentation. The multiple local histograms may correspond to one or more image planes perpendicular to the gravity direction to reduce the effect of gravity on the variation of the Hounsfield density within each local histogram.

[0064] In the case of three-dimensional imaging data, the local histograms may, for example, comprise linear histograms in the lateral (x-) direction. In other words, a Hounsfield density frequency distribution may be established for multiple yz points in the lateral view. Alternatively or additionally, the local histograms may comprise linear histograms in the axial (z-) direction (i.e., a Hounsfield density frequency distribution may be established for multiple xy points in the axial view).

[0065] In another example, the multiple local histograms may comprise two-dimensional patch-wise histograms in the coronal (xz-) plane.

[0066] In some examples, each of the multiple local histograms may have multiple partial local histograms. In other words, for each local histogram, multiple partial local histograms may be generated, each of which represents a sub-region of the at least one lung region represented by the local histogram. In this case, the partial local histograms corresponding to a particular local histogram may be accumulated to generate the local histogram.

[0067] The partial local histograms may have the finest resolution possible, which allows local histograms of any desired resolution to be generated by accumulating partial local histograms.

[0068] For example, the local histograms may include linear histograms (e.g., in the lateral (x-) and / or axial (z-) directions). In other examples, the local histograms may include two-dimensional patch histograms (e.g., in the coronal plane).

[0069] The number of partial local histograms accumulated into one local histogram can be any number between 1 and N, where N is the total number of partial local histograms across at least one lung. In some examples, the number of partial local histograms accumulated into a single local histogram can be a factor of N, such that each local histogram can have the same number of partial local histograms.

[0070] The number of partial local histograms accumulated into one local histogram may be pre-determined or may be determined based on a desired resolution, which may be obtained by user input.

[0071] Hounsfield density histograms are commonly used in processing CT imaging data, and it will be clear to one skilled in the art how to compute multiple local or sub-local histograms, which can be performed using massively parallel processing approaches.

[0072] The histograms are accumulated by combining the histograms. For example, the histograms can be accumulated using a summation operation (e.g., sum or weighted sum of histograms). Other methods of accumulating histograms will be apparent to those skilled in the art, such as using averaging, multiplication, or weighted multiplication. In some examples, when the accumulation involves weighted sum, Gaussian weighting can be applied when accumulating the partial local histograms into the local histograms (e.g., using standard three-dimensional Gaussian filtering when the x-axis of the image volume is the axis of the histogram bins). In other examples, other types of filtering or weighting (e.g., mean filtering, median filtering, etc.) can be applied when accumulating the partial local histograms.

[0073] The processing system 130 then generates a global histogram of the Hounsfield density frequency distribution for at least one lung of the subject 110. The global histogram may be generated by accumulating the local histograms. The global histogram may be generated for a single lung or both lungs, or a separate global histogram may be generated for each lung.

[0074] In some examples, a global histogram may be generated by first accumulating local histograms into regional histograms and then accumulating these regional histograms into a global histogram. The regional histograms may correspond to multiple neighborhood histograms that extend in a direction perpendicular to the direction of the linear histograms. In other words, linear histograms that correspond to positions within a particular neighborhood histogram may be accumulated into a regional histogram. For example, a linear histogram in the transverse (x-) direction may be accumulated into a regional histogram that corresponds to a yz-neighborhood histogram, and a linear histogram in the axial (i.e., head-to-tail) (z-) direction may be accumulated into a regional histogram that corresponds to a xy-neighborhood histogram.

[0075] For each of the multiple local histograms, the processing system 130 generates multiple shifted (displaced) local histograms and cross-correlates each shifted local histogram with the global histogram. Each of the multiple shifted local histograms is a shifted version (along the Hounsfield density axis) of the local histogram by a different shift value. The shift value by which the local histogram is shifted may be within a predetermined range. For example, the shift value may be within a range between -200 Hounsfield units and +200 Hounsfield units.

[0076] Cross-correlation is a well-known technique in image processing for determining the similarity between two histograms, and provides a sensitive and robust estimate of goodness of fit (see, e.g., Wu and Hudson, “An image-clustering method based on cross-correlation of color histograms,” Proc. SPIE5682, Storage and Retrieval Methods and Applications for Multimedia 2005, and Guthier et al., “Parallel implementation of a real-time high dynamic range video system,” Integrated Computer-Aided Engineering, 21(2):189-202).

[0077] Cross-correlation of each of the multiple shifted local histograms with the global histogram produces a corresponding multiple correlation values. The processing system 130 then processes the shift values ​​and the correlation values ​​to generate a final shift value for each of the multiple local histograms. The final shift value of the local histogram may be, for example, the shift value corresponding to the correlation value having the maximum value among the correlation values ​​of the local histogram.

[0078] As another example, to reduce the influence of noise and outliers, the correlation weighted average shift value can also be used as the final shift value. It is expected that the correlation value will increase almost smoothly as it approaches the "true" shift value corresponding to the maximum correlation. Therefore, the "true" peak shift value can be estimated as the correlation weighted average shift even if the peak position is affected by noise. The correlation weighted average shift is:

number

[0079] In some examples, the processing system 130 may be further configured to generate visualizations of the final shift values ​​for the multiple local histograms and output the generated visualizations to the user interface 140. Pulmonary ventilation and perfusion gradients become more apparent in such visualizations compared to Hounsfield density visualizations, allowing a clinician to identify the presence and magnitude of gravity-dependent gradients and determine treatment options for the subject accordingly. The visualization of the final shift values ​​also allows a clinician to identify areas where the local gradients are not aligned with the direction of gravity, which may aid in the diagnosis of certain pulmonary diseases.

[0080] The visualization may use any suitable scale for representing the final shift values. In some examples, negative final shift values ​​may be represented by a first color and positive final shift values ​​may be represented by a second, different color. The magnitude of the final shift value may be represented by the intensity of the first or second color.

[0081] In some examples, the visualization of the final shift value can be overlaid on a visualization of the maximum correlation value (or correlation-weighted average shift value). For example, the processing system 130 can generate a grayscale visualization of the maximum correlation value for multiple local histograms and generate a color overlay representing the final shift value on the visualization of the maximum correlation value.

[0082] Local histograms corresponding to typical parenchymal tissue regions are highly correlated with the global histogram at shift values ​​corresponding to maximum correlation values, while diseased regions generally have very low maximum correlation values.

[0083] By combining the visualization of the final shift value with the visualization of the maximum correlation value, a "key image" can be generated that shows the local presence, amount and distribution of pulmonary ventilation and perfusion (diffusion) gradients together with diseased parenchymal tissue regions.

[0084] In some examples, where the CT imaging data 125 is three-dimensional imaging data, a three-dimensional visualization may be generated that represents pulmonary ventilation and perfusion gradients. For example, the processing system 130 may generate a first plurality of line histograms in the transverse (x-) direction and a second plurality of line histograms in the axial (z-) direction, and cross-correlate each of these histograms with the global histogram as described above to generate a three-dimensional visualization of the final shift values.

[0085] Three-dimensional visualization can reveal regional variations in ventral-dorsal gravity effects, particularly in the coronal (xz-) plane, that are not evident from two-dimensional visualization.

[0086] In another example, a two-dimensional visualization of the coronal view can be generated in addition to the two-dimensional visualization of the lateral and / or axial views, allowing changes in the axial (z-) and lateral (x-) directions to be observed, for example using a linear histogram in the anterior-posterior (y-) direction as the local histogram, as described above.

[0087] FIG. 2 shows an example image generated from CT imaging data of the lungs of a subject infected with COVID-19.

[0088] Image 210 shows standard Hounsfield values ​​for a single slice in a sagittal view.

[0089] Image 220 shows a visualization of the maximum correlation value generated by cross-correlating the histograms as described above, where the local histograms used for cross-correlation with the global histogram were generated by accumulating sub-local histograms with transverse linear histograms in a Gaussian weighted neighborhood radius of 9 pixels in the sagittal plane.

[0090] Image 230 shows a visualization of the shift values ​​at which maximum cross-correlation is reached, with negative shifts represented by darker shading and positive shifts represented by lighter shading. In this visualization, pulmonary ventilation and perfusion gradients are clearly visible.

[0091] Image 240 shows a composite image of images 220 and 230, showing both pulmonary ventilation and perfusion gradients and diseased areas of the lung (in this case the dorsal COVID-19 lesions, which appear as defects in the maximum correlation visualization). Although image 240 is a grayscale image, pulmonary ventilation and perfusion gradients can be more easily distinguished from changes due to disease by using color in one or both of the visualizations.

[0092] FIG. 3 illustrates a computer-implemented method 300 for assessing at least one lung of a subject, according to one embodiment of the present invention.

[0093] The method begins at step 310 where CT imaging data including a representation of at least one lung of a subject is acquired.

[0094] In step 320, segmentation is performed on the CT imaging data to identify a representation of at least one lung of the subject.

[0095] At step 330, the CT imaging data is processed to generate a plurality of local histograms of the Hounsfield density frequency distribution, each of the plurality of local histograms representing a different region of at least one lung represented in the CT imaging data.

[0096] In step 340, a global histogram of the Hounsfield density frequency distribution for at least one lung of the subject is generated.

[0097] In step 350, for each of the multiple local histograms, multiple shifted local histograms are generated, where each shifted local histogram is a version of the local histogram shifted by a different shift value.

[0098] In step 360, for each of the plurality of local histograms, each shifted local histogram is cross-correlated with the global histogram to generate a corresponding plurality of correlation values.

[0099] In step 370, for each of the multiple local histograms, the shift value and the correlation value are processed to generate a final shift value.

[0100] It will be understood that the disclosed methods are computer-implemented methods, and therefore the concept of a computer program comprising code means for carrying out any of the described methods when executed on a processing system is also proposed.

[0101] As previously mentioned, the system utilizes a processor to perform data processing. The processor can be implemented in a variety of ways using software and / or hardware to perform the various functions required. The processor typically employs one or more microprocessors that can be programmed using software (e.g., microcode) to perform the required functions. The processor can also be implemented as a combination of dedicated hardware to perform some functions and one or more programmed microprocessors and associated circuitry to perform other functions.

[0102] Examples of circuitry that may be used in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs).

[0103] In various implementations, the processor may be associated with one or more storage media, such as volatile and non-volatile computer memories, such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on the one or more processors and / or controllers, perform the necessary functions. The various storage media may be fixed within the processor or controller, or may be portable so that the one or more programs stored thereon can be loaded into the processor.

[0104] Variations of the disclosed embodiments can be understood and implemented by those skilled in the art in implementing the claimed invention by studying the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the singular does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain means are recited in mutually different dependent claims does not indicate that a combination of these means cannot be used to advantage. The computer program can be stored / distributed by an appropriate medium, such as an optical storage medium or a solid-state medium, provided together with or as part of other hardware, but also can be distributed in other forms, such as via the Internet or other wired or wireless communication systems. It should be noted that when the term "adapted" is used in the claims or the description, it is intended that the term "adapted" is equivalent to the term "configured". Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. 1. A processing system for assessing pulmonary ventilation and perfusion gradients in at least one lung of a subject, comprising: receiving CT imaging data including a representation of at least one lung of the subject; performing segmentation on the CT imaging data to identify a representation of at least one lung of the subject; processing the CT imaging data to generate a plurality of local histograms of Hounsfield density frequency distributions, each of the plurality of local histograms representing a different region of the at least one lung represented in the CT imaging data; generating a global histogram of Hounsfield density frequency distributions for the at least one lung of the subject; For each of the plurality of local histograms, generating a plurality of shifted local histograms, each of which is a version of the local histogram shifted by a different shift value; cross-correlating each shifted local histogram with the global histogram to generate a corresponding plurality of correlation values; processing the shift values ​​and the correlation values ​​to generate final shift values; the final shift value represents the difference between the local histogram and the global histogram; A processing system wherein the total range of magnitudes of the final shift values ​​represents the magnitude of the pulmonary ventilation and perfusion gradient.

2. 2. The processing system of claim 1, wherein the final shift value of each of the plurality of local histograms is a shift value corresponding to the correlation value having the largest value among the plurality of correlation values ​​of that local histogram.

3. The processing system of claim 1 , wherein the final shift value of each of the plurality of local histograms is a correlation weighted average shift value.

4. 4. The processing system according to claim 1, wherein the system provides a visualisation of the final shift value in a user interface.

5. 5. The processing system of claim 4, wherein negative final shift values ​​are represented by a first color and positive final shift values ​​are represented by a second, different color.

6. The visualization information of the final shift value is generating, for each local histogram, a visualization of the maximum correlation value of that local histogram; generating an overlay representing the final shift value on the visualization of the maximum correlation value; The processing system of claim 5 , wherein the processing system is generated by:

7. 4. The processing system according to claim 1, wherein the shift value is within a predetermined range, for example between -200 Hounsfield units and +200 Hounsfield units.

8. The processing system of claim 1 , wherein the plurality of local histograms comprises lateral and / or axial, i.e. cranio-caudal, linear histograms.

9. The processing system of claim 1 , wherein the plurality of local histograms comprises two-dimensional patch-wise histograms in a coronal plane.

10. Each of the plurality of local histograms comprises: generating a plurality of sub-local histograms, each representing a different sub-region of the at least one lung region corresponding to said local histogram; Accumulating the partial local histograms into the local histogram The processing system according to claim 1 , wherein the processing system is generated by:

11. The processing system of claim 1 , wherein the global histogram is generated by accumulating the local histograms into the global histogram.

12. 1. A system for assessing at least one lung of a subject, comprising: a CT scanner that generates CT imaging data; The processing system according to any one of claims 1 to 3, which receives the CT imaging data from the CT scanner; A system having:

13. 13. The system of claim 12, wherein the processing system generates visualization information of the final shift values, the system further comprising a user interface that receives and displays the generated visualization information of the final shift values ​​from the processing system.

14. 1. A computer-implemented method for assessing pulmonary ventilation and perfusion gradients in at least one lung of a subject, comprising: receiving CT imaging data including a representation of at least one lung of the subject; performing segmentation on the CT imaging data to identify a representation of at least one lung of the subject; generating a plurality of local histograms of Hounsfield density frequency distributions by processing the CT imaging data, each of the plurality of local histograms representing a different region of the at least one lung represented in the CT imaging data; generating a global histogram of Hounsfield density frequency distributions for the at least one lung of the subject; For each of the plurality of local histograms, generating a plurality of shifted local histograms, each of which is a version of the local histogram shifted by a different shift value; cross-correlating each shifted local histogram with the global histogram to generate a corresponding plurality of correlation values; processing the shift values ​​and the correlation values ​​to generate final shift values; Steps and and the final shift value represents the difference between the local histogram and the global histogram; A computer-implemented method wherein the entire range of magnitudes of the final shift values ​​represents the magnitude of the pulmonary ventilation and perfusion gradient.

15. 15. A computer program comprising computer program code means which, when executed on a computing device having a processing system, causes the processing system to perform all of the steps of the computer-implemented method of claim 14.

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