Method of determining a motion of an organ

Abstract

The present disclosure provides a method of estimating a motion of a lung. The method comprises receiving a set of images of organs, wherein one of the organs is the lung. The method then reconstructs a three-dimensional model of the organs based on the received images. Lung tissue is then determined within the three-dimensional model of the organs. The method then generates a lung mask based on the determined lung tissue and extends the lung mask in an inferior direction so as to include tissue below the diaphragm. The motion of the lung based on the extended lung mask is then determined.

Description

METHOD OF DETERMINING A MOTION OF AN ORGANRelated Application

[0001] This application relates to Application No. AU2024900747, filed on 20 March 2024, the contents of which is incorporated herein in its entirety.Technical Field[0001A] The present invention relates generally to an imaging method and, in particular, to an imaging method for determining the motion of an organ.Background

[0002] Cone-beam computed tomography (CBCT) scanners are capable of capturing images of an organ over a period of time. These images are then used to determine the motion of the organ (e.g., lung). Conventional techniques (e.g., motion compensated Feldkmap-Davis-Kress (MCFDK), MCFDK-prior, 3D-FDK, 4D-FDK, and the like) typically require a long acquisition time with a high number of images (e.g., over 1000).

[0003] However, with the introduction of newer technologies, there is a need for CBCT processing techniques that are capable of capturing images of an organ and determining the motion of the organ with shorter acquisition time with fewer images.

[0004] One example application with such a need is the new generation of linear accelerators (LINACS) that deliver rapid radiation therapy treatment. These new generation LINACS have gantry speed that is 4 times faster than conventional LINACS, leading to significantly shorter acquisition times (and subsequently captured images) for CBCT monitoring the operation of the LINACS. Conventional CBCT processing techniques are incapable of meeting the needs of the new generation LINACS.Summary

[0005] It is an object of the present invention to substantially overcome, or at least ameliorate, one or more disadvantages of existing arrangements.

[0006] Disclosed are CBCT arrangements (called hereinafter as MCFDK-data driven or MCFDK-DD) capable of determining the motion of an organ with a shorter acquisition time and fewer captured images, in comparison to conventional techniques.

[0007] According to a first aspect of the present disclosure, there is provided a method of estimating a motion of a lung, the method comprising:receiving a set of images of organs, wherein one of the organs is the lung; reconstructing a three-dimensional model of the organs based on the received images; determining lung tissue within the three-dimensional model of the organs; generating a lung mask based on the determined lung tissue; extending the lung mask in an inferior direction so as to include tissue below the diaphragm; and estimating the motion of the lung based on the extended lung mask.

[0008] According to another aspect of the present disclosure, there is provided an apparatus for implementing any one of the aforementioned methods.

[0009] According to another aspect of the present disclosure, there is provided a computer program product including a computer readable medium having recorded thereon a computer program for implementing any one of the methods described above.

[0010] Other aspects are also disclosed.Brief Description of the Drawings

[0011] At least one embodiment of the present invention will now be described with reference to the drawings, in which:

[0012] Fig. 1 shows a system for capturing images of an organ and determining the motion of the organ in accordance with the present disclosure;

[0013] Fig. 2 is a flow diagram of a method of estimating a motion of a lung according to the present disclosure;

[0014] Fig. 3 is a flow diagram of a sub-process of the method of Fig. 2;

[0015] Fig. 4 shows images of the lung with the diaphragm motion blur length (i.e. , tissue interface width (TIW)) being shown;

[0016] Fig. 5 shows deformation vector fields of the lung;

[0017] Figs. 6A and 6B form a schematic block diagram of a general purpose computer system upon which arrangements described can be practiced;AH25(P0066880AU_43799491_1 )

[0018] Figs. 7A to 7D show different reconstructions for the inhale (top figures) and exhale (bottom figures);

[0019] Figs. 8A to 8D show different reconstructions for the inhale (top figures) and exhale (bottom figures) for a patient with the lowest structural similarity index (SSIM);

[0020] Fig. 9 shows the mean square error (MSE) values for all respiration phases by different reconstruction methods;

[0021] Fig. 10 shows the SSIM values for all respiration phases by different reconstruction methods; and

[0022] Fig. 11 shows the TIW values for all respiration phases by different reconstruction methods;.Detailed Description

[0023] Where reference is made in any one or more of the accompanying drawings to steps and / or features, which have the same reference numerals, those steps and / or features have for the purposes of this description the same function(s) or operation(s), unless the contrary intention appears.

[0024] Fig. 1 shows a system 100 in which the method of estimating the motion of an organ 110 (e.g., lung) is performed. The system 100 includes a cone-beam computed tomography (CBCT) scanner 120 and a computer system 1300.

[0025] The CBCT scanner 120 captures a set of images of the organs 110 by rapidly scanning the organs 110 over a short period of time. In one example, the CBCT scanner 120 captures 491 images in 16.6 seconds. The captured images are then sent to the computer system 1300, which executes a method of estimating the motion of the organ(s) 110.

[0026] In one example application, the computer system 1300 transmits the estimated motion of the organ(s) 110 to a new generation LINACS to enable the LINACS to accurately deliver the radiation therapy to the moving organ(s) 110.

[0027] Suitable CBCT scanners 120 are widely available and may be used to perform the rapid scan in the short period of time. Accordingly, the functions of the CBCT scanner 120 are not discussed in the present disclosure.AH25(P0066880AU_43799491_1 )

[0028] The computer system 1300 is discussed hereinafter in relation to Figs. 6A and 6B.

[0029] Fig. 2 shows a flow diagram of a method 200 of estimating the motion of a lung (i.e., organ 110). The method 200 is implemented as one or more software application programs 1333 executable within the computer system 1300.

[0030] The method 200 commences at step 205 by receiving a set of images of the organs 110. The CBCT scanner 120 captures the set of images of the organs 110, wherein the set of images is captured over a period of time. The CBCT scanner 120 then transmits the set of images to the computer system 1300. The method 200 then proceeds from step 205 to step 210.

[0031] In step 210, the method 200 reconstructs a three-dimensional (3D) model of the organs 110 based on the received images. In one example, the 3D model is reconstructed using 401x321x321 voxels, each voxel having dimensions of 1x1x1mm3. Different number of voxels and voxel dimensions may be used in the 3D model. The 3D model reconstruction is performed using B-spline deformable registration. Other suitable registration methods may be used to reconstruct the 3D model from the set of images of the organs 110. The method 200 then proceed from step 210 to step 215.

[0032] In step 215, the method 200 determines the lung tissue within the 3D model of the organs. In one arrangement, the lung tissue is determined within the 3D model based on the attenuation coefficients or approximate Hounsfield Units (HU) of the voxels. Both attenuation coefficients (mm-1) and HU are values associated with each voxel. In a further arrangement, further determination is performed on the 3D model to determine other organs or elements, such as air, soft tissue, and bone. In one example arrangement, voxels are determined as lung tissue when the HU of the voxels are between 180 and 880. In this example, voxels having HU below 180 are determined as air, voxels having HU between 880 and 1150 are determined as soft tissue, and voxels having HU over 1150 are determined as bone. The method 200 proceeds from step 215 to step 220.

[0033] In step 220, the method 200 generates a lung mask based on the determined lung tissue. The lung mask is generated based on the determined lung tissue. The lung mask may have holes due to voxel variations during CBCT image acquisition uncertainties. These holes are filled in by using a morphological reconstruction method. The method 200 then proceeds from step 220 to step 225.AH25(P0066880AU_43799491_1 )

[0034] In step 225, the method 200 extends the lung mask in an inferior direction so as to include the tissue below the diaphragm (which is located in the inferior direction of the lung). The extension in the inferior direction is about 15mm. Such an extension amounts to 15 voxels (if each voxel has dimensions of 1mm x 1mm x 1mm). The method 200 proceeds from step 225 to sub-process 300.

[0035] The sub-process 300 estimates the motion of the lung based on the extended lung mask. The sub-process 300 is implemented as one or more software application programs 1333 executable within the computer system 1300.

[0036] The sub-process 300 commences at step 305 by determining a sub-region of the extended lung mask. First, the most superior and inferior points of the lung mask for each of the left and right lungs are determined. Second, for each lung, the number of voxels (referred to as / ) of the 3D model between the most superior and inferior points is determined. Third, for each lung, a sub-region of n x lx n, where I is as defined above, is determined. In one arrangement, n is set to 20. n is a heuristic value and must be sufficiently large to capture the respiratory motion. However, the value of n should not be too large to be affected by other factors (e.g., CBCT image quality) in the image. Accordingly, other values of n maybe used as long as the above requirements are met. Once the sub-region is determined, the sub-process 300 proceeds from step 305 to step 310.

[0037] In step 310, the sub-process 300 normalises the voxel values in the sub-region. The normalisation step scales the voxel values to be between 0 and 1, where 1 is the maximum voxel value, and 0 is the minimum voxel value. Normalisation is performed to simplify the estimation of the length over which motion blur is occurring, defined as a drop in intensity from 90% to 10% or 0.9 to 0.1 in absolute terms after normalisation. Normalisation also removes variability in absolute value for lung and diaphragm voxel values, which vary between patients. Further, normalisation of the voxel values also means that the curve used in the curve-fitting process (see step 315) does not need to be rescaled. The sub-process 300 proceeds from step 310 to step 315.

[0038] In step 315, the sub-process 300 fits a curve of a mathematical function (e.g., sigmoid) to the normalised voxel values. Curve fitting involves plotting y = f(x) where y is the normalised voxel values and x is the voxel location in the superior-inferior direction, and f is a sigmoid function. Other methods such as inverse tan function y = —tan1x + 0.5 or x = ayn+ 0.5where n are odd numbers greater than 1 may be used, and a is a heuristic parameter for controlling the slope of the curve. The purpose of the curve fitting is to estimate the length of theAH25(P0066880AU_43799491_1 )motion blur, in this case estimated as the average length over which the n2voxel runs in the SI direction change from 90% to 10%. The sub-process 300 proceeds from step 315 to step 320.

[0039] In step 320, the sub-process 300 determines diaphragm motion blur length based on a mathematical function curve fitted to the normalised voxel values. The motion blur length is measured by determining the average distance over which a mathematical function curve (e.g., sigmoids) is fitted to normalised voxel values change from 0.9 to 0.1. Using the normalised voxel values between 0.1 and 0.9 enables repeatable tissue interface width (TIW) measurements (i.e. , diaphragm motion blur length) in the presence of uncertainty in the positioning of the voxel sub-region. Fig. 4A shows a coronal plane of the lung mask showing the Left-Right (LR) anatomical direction location of the sub-region of each of the left and right lungs. The LR locations of the sub-region for the left and right lungs are 1 / 4 and 5 / 6 of the width of the lung mask in the LR direction from the right border of the right lung. Fig. 4B shows a sagittal plane of the lung mask showing the anterior-posterior (AP) anatomical direction location of the sub-region. The AP location of the sub-region for both the left and right lungs are 1 / 3 of the AP width of the lung mask from the posterior border of the lung. The sub-process 300 proceeds from step 320 to step 325.

[0040] In step 325, the sub-process 300 determines the deformation vector fields (DVF) of the extended lung mask for each respiratory bin. Respiration is divided into more than 1 phasesnbins with typically nbins= 10. Accordingly, acquired CBCT images are categorised into different respiratory bins based on the respiratory phase in which the image is acquired. For each respiratory bin, the DVF values in the lung mask are set. In one arrangement, the DVF value at the most superior voxel of the extended lung mask is set to 0, then linearly increasing the DVF value for each voxel in the inferior direction until the DVF value at the most inferior voxel of the extended lung mask is set to 1 . The setting of the DVF values is based on the physiological assumption that respiration is predominantly driven by the contraction and relaxation of the diaphragm muscles. Accordingly, the littlest movement occurs at the most superior position of the lung, while the greatest movement occurs at the most inferior position of the lung. The sub-process 300 proceeds from step 325 to step 330.

[0041] In step 330, the sub-process 300 determines the motion of the lung based on the determined diaphragm motion blur length and the determined DVF values. The diaphragm motion blur length (i.e., TIW) determined at step 320 is multiped by the DVF values. The multiplication results are then linearly scaled based on the respiratory bin distance from the exhale respiratory bin such that for the DVF W at voxel x,y,z for respiration phase j:AH25(P0066880AU_43799491_1 )Where rextialeis the index of the exhale phase (in one arrangement, phase 6 is rexhaJe) andThe DVF is then smoothed using a Gaussian smoothing filter with, in one arrangement, a a = 5 to simulate a more realistic DVF. An example of the estimated DVF is shown in Figs. 5A and 5B. The colour bar in Figs. 5A and 5B show the DVF magnitude in the superior-inferior (SI) direction. These estimated DVFs are then used to determine the motion of the lung and to improve the 3D model reconstruction (see step 210). Using a conventional motion compensated FDK reconstruction algorithm, the acquired CBCT images are deformed using the 4D-DVFs (i.e. , the estimated DVF).

[0042] The sub-process 300 concludes at the conclusion of step 330. Similarly, the method 200 concludes at the conclusion of the sub-process 300.

[0043] Figs. 7A to 7D show the different reconstructions of the lung using MCFDK-DD, MCFDK-Prior, 3D-FDK, and 4D-FDK respectively. Figs. 8A to 8D show the different reconstructions of the lung of a patient with the lowest SSIM using MCFDK-DD, MCFDK-Prior, 3D-FDK, and 4D-FDK respectively. The figures at the top are the reconstruction at the inhale phase, while the figures at the bottom are the reconstruction at the exhale phase. Figs. 7A to 7D and 8A to 8D show that the MCFDK-DD (i.e., the invention according to the present disclosure) is able to reduce the motion artefacts, while producing reconstructions with diaphragm positions that are in similar positions to the ground truth reconstruction (i.e., the reconstruction shown in Figs. 7D and 8D). The grey-dotted line (i.e., the bottom line) shows the diaphragm positions at the inhale phase, while the white-dotted line (i.e., the top line) shows the diaphragm positions at the exhale phase.

[0044] Figs. 9 and 10 show the MSE and SSIM values comparing the different reconstructions to the ground truth 4D-FDK reconstruction for all patients for all respiration phases. SSIM is on a scale of 0-1 where 1 indicates two identical images. MSE is a pixel-based image quality metric, where the lower MSE means the two images being compared are similar. Figs. 9 andAH25(P0066880AU_43799491_1 )10 show the low MSE (median 1.23 x 10-6mm-1) and high SSIM (median 0.92) for the MCFDK- DD method with the reduced number of images. Figs. 9 and 10 also show that using MCFDK- DD is able to generate a reconstruction that is of similar quality to a reconstruction generated by 4DCT (MCFDK-Prior) (median MSE 1.14 x 10-6mm-1and SSIM 0.92 respectively).

[0045] The TIW values for all reconstructions for all patients and all respiration phases are shown in Fig. 11. Fig. 11 shows that the MCFDK-Prior TIW values (median TIW 23.1 mm) are similar to the TIW values measured from the MCFDK-DD method (p=0.087), indicating that using the DVF estimation method of MCFDK-DD to estimate lung motion (when compared to a prior motion volume) does not significantly affect the diaphragm motion artefacts for these patients. The MCFDK-DD method (median TIW 18.7 mm) has reduced TIW when compared with the 3D-FDK reconstruction (median TIW 24.2 mm) (p=0.01). The reduced TIW value indicates that the MCFDK methods are able to create reconstructions that reduce motion artefacts associated with lung / diaphragm motion when compared with the 3D-FDK reconstruction. Additionally, the TIW values calculated for the MCFDK-DD method are not significantly different to the TIW values for the conventional acquisition 4D-FDK reconstructions (median TIW 21.4 mm) (p=0.62).Computer System 1300

[0046] Figs. 6A and 6B depict a general-purpose computer system 1300, upon which the various arrangements described can be practiced.

[0047] As seen in Fig. 6A, the computer system 1300 includes: a computer module 1301; input devices such as a keyboard 1302, a mouse pointer device 1303, a scanner 1326, a camera 1327, and a microphone 1380; and output devices including a printer 1315, a display device 1314 and loudspeakers 1317. An external Modulator-Demodulator (Modem) transceiver device 1316 may be used by the computer module 1301 for communicating to and from a communications network 1320 via a connection 1321. The communications network 1320 may be a wide-area network (WAN), such as the Internet, a cellular telecommunications network, or a private WAN. Where the connection 1321 is a telephone line, the modem 1316 may be a traditional “dial-up” modem. Alternatively, where the connection 1321 is a high capacity (e.g., cable) connection, the modem 1316 may be a broadband modem. A wireless modem may also be used for wireless connection to the communications network 1320.AH25(P0066880AU_43799491_1 )

[0048] The computer module 1301 typically includes at least one processor unit 1305, and a memory unit 1306. For example, the memory unit 1306 may have semiconductor random access memory (RAM) and semiconductor read only memory (ROM). The computer module 1301 also includes an number of input / output (I / O) interfaces including: an audio-video interface 1307 that couples to the video display 1314, loudspeakers 1317 and microphone 1380; an I / O interface 1313 that couples to the keyboard 1302, mouse 1303, scanner 1326, camera 1327 and optionally a joystick or other human interface device (not illustrated); and an interface 1308 for the external modem 1316 and printer 1315. In some implementations, the modem 1316 may be incorporated within the computer module 1301, for example within the interface 1308. The computer module 1301 also has a local network interface 1311, which permits coupling of the computer system 1300 via a connection 1323 to a local-area communications network 1322, known as a Local Area Network (LAN). As illustrated in Fig. 6A, the local communications network 1322 may also couple to the wide network 1320 via a connection 1324, which would typically include a so-called “firewall” device or device of similar functionality. The local network interface 1311 may comprise an Ethernet circuit card, a Bluetooth® wireless arrangement or an IEEE 802.11 wireless arrangement; however, numerous other types of interfaces may be practiced for the interface 1311.

[0049] The I / O interfaces 1308 and 1313 may afford either or both of serial and parallel connectivity, the former typically being implemented according to the Universal Serial Bus (USB) standards and having corresponding USB connectors (not illustrated). Storage devices 1309 are provided and typically include a hard disk drive (HDD) 1310. Other storage devices such as a floppy disk drive and a magnetic tape drive (not illustrated) may also be used. An optical disk drive 1312 is typically provided to act as a non-volatile source of data. Portable memory devices, such optical disks (e.g., CD-ROM, DVD, Blu-ray Disc™), USB-RAM, portable, external hard drives, and floppy disks, for example, may be used as appropriate sources of data to the system 1300.

[0050] The components 1305 to 1313 of the computer module 1301 typically communicate via an interconnected bus 1304 and in a manner that results in a conventional mode of operation of the computer system 1300 known to those in the relevant art. For example, the processor 1305 is coupled to the system bus 1304 using a connection 1318. Likewise, the memory 1306 and optical disk drive 1312 are coupled to the system bus 1304 by connections 1319. Examples of computers on which the described arrangements can be practised include IBM-PC’s and compatibles, Sun Sparcstations, Apple Mac™ or like computer systems.AH25(P0066880AU_43799491_1 )

[0051] The method 200 and sub-process 300 may be implemented using the computer system 1300 wherein the processes of Figs. 2 and 3 described above, may be implemented as one or more software application programs 1333 executable within the computer system 1300. In particular, the steps of the method 200 and sub-process 300 are effected by instructions 1331 (see Fig. 6B) in the software 1333 that are carried out within the computer system 1300. The software instructions 1331 may be formed as one or more code modules, each for performing one or more particular tasks. The software may also be divided into two separate parts, in which a first part and the corresponding code modules performs the motion estimation method and sub-process and a second part and the corresponding code modules manage a user interface between the first part and the user.

[0052] The software may be stored in a computer readable medium, including the storage devices described below, for example. The software is loaded into the computer system 1300 from the computer readable medium, and then executed by the computer system 1300. A computer readable medium having such software or computer program recorded on the computer readable medium is a computer program product. The use of the computer program product in the computer system 1300 preferably effects an advantageous apparatus for estimating the motion of organs.

[0053] The software 1333 is typically stored in the HDD 1310 or the memory 1306. The software is loaded into the computer system 1300 from a computer readable medium, and executed by the computer system 1300. Thus, for example, the software 1333 may be stored on an optically readable disk storage medium (e.g., CD-ROM) 1325 that is read by the optical disk drive 1312. A computer readable medium having such software or computer program recorded on it is a computer program product. The use of the computer program product in the computer system 1300 preferably effects an apparatus for estimating the motion of organs.

[0054] In some instances, the application programs 1333 may be supplied to the user encoded on one or more CD-ROMs 1325 and read via the corresponding drive 1312, or alternatively may be read by the user from the networks 1320 or 1322. Still further, the software can also be loaded into the computer system 1300 from other computer readable media. Computer readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and / or data to the computer system 1300 for execution and / or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROM, DVD, Blu-ray™ Disc, a hard disk drive, a ROM or integrated circuit, USB memory, a magnetooptical disk, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the computer module 1301. Examples of transitory orAH25(P0066880AU_43799491_1 )non-tangible computer readable transmission media that may also participate in the provision of software, application programs, instructions and / or data to the computer module 1301 include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like.

[0055] The second part of the application programs 1333 and the corresponding code modules mentioned above may be executed to implement one or more graphical user interfaces (GUIs) to be rendered or otherwise represented upon the display 1314. Through manipulation of typically the keyboard 1302 and the mouse 1303, a user of the computer system 1300 and the application may manipulate the interface in a functionally adaptable manner to provide controlling commands and / or input to the applications associated with the GUI(s). Other forms of functionally adaptable user interfaces may also be implemented, such as an audio interface utilizing speech prompts output via the loudspeakers 1317 and user voice commands input via the microphone 1380.

[0056] Fig. 6B is a detailed schematic block diagram of the processor 1305 and a “memory” 1334. The memory 1334 represents a logical aggregation of all the memory modules (including the HDD 1309 and semiconductor memory 1306) that can be accessed by the computer module 1301 in Fig. 6A.

[0057] When the computer module 1301 is initially powered up, a power-on self-test (POST) program 1350 executes. The POST program 1350 is typically stored in a ROM 1349 of the semiconductor memory 1306 of Fig. 6A. A hardware device such as the ROM 1349 storing software is sometimes referred to as firmware. The POST program 1350 examines hardware within the computer module 1301 to ensure proper functioning and typically checks the processor 1305, the memory 1334 (1309, 1306), and a basic input-output systems software (BIOS) module 1351, also typically stored in the ROM 1349, for correct operation. Once the POST program 1350 has run successfully, the BIOS 1351 activates the hard disk drive 1310 of Fig. 6A. Activation of the hard disk drive 1310 causes a bootstrap loader program 1352 that is resident on the hard disk drive 1310 to execute via the processor 1305. This loads an operating system 1353 into the RAM memory 1306, upon which the operating system 1353 commences operation. The operating system 1353 is a system level application, executable by the processor 1305, to fulfil various high level functions, including processor management, memory management, device management, storage management, software application interface, and generic user interface.AH25(P0066880AU_43799491_1 )

[0058] The operating system 1353 manages the memory 1334 (1309, 1306) to ensure that each process or application running on the computer module 1301 has sufficient memory in which to execute without colliding with memory allocated to another process. Furthermore, the different types of memory available in the system 1300 of Fig. 6A must be used properly so that each process can run effectively. Accordingly, the aggregated memory 1334 is not intended to illustrate how particular segments of memory are allocated (unless otherwise stated), but rather to provide a general view of the memory accessible by the computer system 1300 and how such is used.

[0059] As shown in Fig. 6B, the processor 1305 includes a number of functional modules including a control unit 1339, an arithmetic logic unit (ALU) 1340, and a local or internal memory 1348, sometimes called a cache memory. The cache memory 1348 typically includes a number of storage registers 1344 - 1346 in a register section. One or more internal busses 1341 functionally interconnect these functional modules. The processor 1305 typically also has one or more interfaces 1342 for communicating with external devices via the system bus 1304, using a connection 1318. The memory 1334 is coupled to the bus 1304 using a connection 1319.

[0060] The application program 1333 includes a sequence of instructions 1331 that may include conditional branch and loop instructions. The program 1333 may also include data 1332 which is used in execution of the program 1333. The instructions 1331 and the data 1332 are stored in memory locations 1328, 1329, 1330 and 1335, 1336, 1337, respectively. Depending upon the relative size of the instructions 1331 and the memory locations 1328-1330, a particular instruction may be stored in a single memory location as depicted by the instruction shown in the memory location 1330. Alternately, an instruction may be segmented into a number of parts each of which is stored in a separate memory location, as depicted by the instruction segments shown in the memory locations 1328 and 1329.

[0061] In general, the processor 1305 is given a set of instructions which are executed therein. The processor 1305 waits for a subsequent input, to which the processor 1305 reacts to by executing another set of instructions. Each input may be provided from one or more of a number of sources, including data generated by one or more of the input devices 1302, 1303, data received from an external source across one of the networks 1320, 1302, data retrieved from one of the storage devices 1306, 1309 or data retrieved from a storage medium 1325 inserted into the corresponding reader 1312, all depicted in Fig. 6A. The execution of a set of the instructions may in some cases result in output of data. Execution may also involve storing data or variables to the memory 1334.AH25(P0066880AU_43799491_1 )

[0062] The disclosed motion estimation arrangements use input variables 1354, which are stored in the memory 1334 in corresponding memory locations 1355, 1356, 1357. The motion estimation arrangements produce output variables 1361 , which are stored in the memory 1334 in corresponding memory locations 1362, 1363, 1364. Intermediate variables 1358 may be stored in memory locations 1359, 1360, 1366 and 1367.

[0063] Referring to the processor 1305 of Fig. 6B, the registers 1344, 1345, 1346, the arithmetic logic unit (ALU) 1340, and the control unit 1339 work together to perform sequences of micro-operations needed to perform “fetch, decode, and execute” cycles for every instruction in the instruction set making up the program 1333. Each fetch, decode, and execute cycle comprises:

[0064] a fetch operation, which fetches or reads an instruction 1331 from a memory location 1328, 1329, 1330;

[0065] a decode operation in which the control unit 1339 determines which instruction has been fetched; and

[0066] an execute operation in which the control unit 1339 and / or the ALU 1340 execute the instruction.

[0067] Thereafter, a further fetch, decode, and execute cycle for the next instruction may be executed. Similarly, a store cycle may be performed by which the control unit 1339 stores or writes a value to a memory location 1332.

[0068] Each step or sub-process in the processes of Figs. 2 and 3 is associated with one or more segments of the program 1333 and is performed by the register section 1344, 1345, 1347, the ALU 1340, and the control unit 1339 in the processor 1305 working together to perform the fetch, decode, and execute cycles for every instruction in the instruction set for the noted segments of the program 1333.

[0069] The method of estimation the motion of organs may alternatively be implemented in dedicated hardware such as one or more integrated circuits performing the functions or sub functions of the method 200 or sub-process 300. Such dedicated hardware may include graphic processors, digital signal processors, or one or more microprocessors and associated memories.AH25(P0066880AU_43799491_1 )Industrial Applicability

[0070] The arrangements described are applicable to the computer and data processing industries and particularly for the medical industry in determining the motion of organs.

[0071] The foregoing describes only some embodiments of the present invention, and modifications and / or changes can be made thereto without departing from the scope and spirit of the invention, the embodiments being illustrative and not restrictive.

[0072] In the context of this specification, the word “comprising” means “including principally but not necessarily solely” or “having” or “including”, and not “consisting only of”. Variations of the word "comprising", such as “comprise” and “comprises” have correspondingly varied meanings.AH25(P0066880AU_43799491_1 )

Claims

CLAIMS:

1. A method of estimating a motion of a lung, the method comprising: receiving a set of images of organs, wherein one of the organs is the lung; reconstructing a three-dimensional model of the organs based on the received images; determining lung tissue within the three-dimensional model of the organs; generating a lung mask based on the determined lung tissue; extending the lung mask in an inferior direction so as to include tissue below the diaphragm; and estimating the motion of the lung based on the extended lung mask.

2. The method according to claim 1, wherein the images further comprises any one of air, soft tissue, and bone.

3. The method of claim 1 or 2, wherein the step of estimating the lung motion comprises: determining a sub-region of the lung mask; normalising voxel values of the sub-region; determining diaphragm motion blur length based on a mathematical function curve fitted to the normalised voxel values; determining deformation vector fields (DVF) of the extended lung mask for each respiratory bin; determining the motion of the lung based the determined diaphragm motion blur length and the determined DVF.

4. The method of claim 3, wherein the step of determining diaphragm motion blur length comprises: determining an average distance over which the mathematical function curve is fitted to normalised voxel values change from 0.9 to 0.1.

5. The method of any one of claims 1 to 4, wherein the determining of the lung tissue is based on attenuation coefficients or Hounsfield Units of voxel values of the three-dimensional model.

6. The method of any one of claims 1 to 5, wherein the lung mask includes holes, wherein the method further comprises filling the holes in the lung mask using a morphological reconstruction method.AH25(P0066880AU_43799491_1 )7. A device for estimating a motion of a lung, the device comprising: a scanner configured for capturing images of organs, wherein one of the organs is the lung; and a computer system having a processor and a memory unit, wherein the memory unit stores one or more application programs that are executable by the processor, wherein the one or more application programs include a method of estimating the motion of the lung, wherein the method comprises: receiving a set of images of organs, wherein one of the organs is the lung; reconstructing a three-dimensional model of the organs based on the received images; determining lung tissue within the three-dimensional model of the organs; generating a lung mask based on the determined lung tissue; extending the lung mask in an inferior direction so as to include tissue below the diaphragm; and estimating the motion of the lung based on the extended lung mask.

8. The device of claim 7, further comprising a linear accelerator that is configured to deliver radiation therapy to the lung, wherein the computer system is configured to transmit the estimated lung motion to the linear accelerator, and wherein the linear accelerator is configured to deliver the radiation therapy based on the estimated lung motion.

9. The device of claim 7 or 8, wherein the images further comprises any one of air, soft tissue, and bone.

10. The device of any one of claims 7 to 9, wherein the method step of estimating the lung motion comprises: determining a sub-region of the lung mask; normalising voxel values of the sub-region; determining diaphragm motion blur length based on a mathematical function curve fitted to the normalised voxel values; determining deformation vector fields (DVF) of the extended lung mask for each respiratory bin; determining the motion of the lung based the determined TIW and the determined DVF.

11. The device of claim 10, wherein the method step of determining diaphragm motion blur length further comprises:AH25(P0066880AU_43799491_1 )determining an average distance over which the mathematical function curve is fitted to normalised voxel values change from 0.9 to 0.1.

12. The device of any one of claims 7 to 11 , wherein the method step of determining of the lung tissue is based on attenuation coefficients or Hounsfield Units of voxel values of the three- dimensional model.

13. The device of any one of claims 7 to 12, wherein the lung mask includes holes, wherein the method further comprises filling the holes in the lung mask using a morphological reconstruction method.

14. A non-transitory computer readable medium storing one or more application programs that are executable by the processor, wherein the one or more application programs include a method of estimating the motion of the lung, wherein the method comprises: receiving a set of images of organs, wherein one of the organs is the lung; reconstructing a three-dimensional model of the organs based on the received images; determining lung tissue within the three-dimensional model of the organs; generating a lung mask based on the determined lung tissue; extending the lung mask in an inferior direction so as to include tissue below the diaphragm; and estimating the motion of the lung based on the extended lung mask.

15. The non-transitory computer readable medium according to claim 14, wherein the images further comprises any one of air, soft tissue, and bone.

16. The non-transitory computer readable medium of claim 14 or 15, wherein the step of estimating the lung motion comprises: determining a sub-region of the lung mask; normalising voxel values of the sub-region; determining diaphragm motion blur length based on a mathematical function curve fitted to the normalised voxel values; determining deformation vector fields (DVF) of the extended lung mask for each respiratory bin; determining the motion of the lung based the determined TIW and the determined DVF.AH25(P0066880AU_43799491_1 )17. The non-transitory computer readable medium of claim 16, wherein the step of determining diaphragm motion blur length comprises: determining an average distance over which the mathematical function curve is fitted to normalised voxel values change from 0.9 to 0.1.

18. The non-transitory computer readable medium of any one of claims 14 to 17, wherein the determining of the lung tissue is based on attenuation coefficients or Hounsfield Units of voxel values of the three-dimensional model.

19. The non-transitory computer readable medium of any one of claims 14 to 18, wherein the lung mask includes holes, wherein the method further comprises filling the holes in the lung mask using a morphological reconstruction method.AH25(P0066880AU_43799491_1 )

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