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WO2023115120A1 - Optimisation basée sur la dose pour suivi de collimateur multilame (« mlc ») multicible pendant une radiothérapie, procédés et appareil - Google Patents

Optimisation basée sur la dose pour suivi de collimateur multilame (« mlc ») multicible pendant une radiothérapie, procédés et appareil Download PDF

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WO2023115120A1
WO2023115120A1 PCT/AU2022/051543 AU2022051543W WO2023115120A1 WO 2023115120 A1 WO2023115120 A1 WO 2023115120A1 AU 2022051543 W AU2022051543 W AU 2022051543W WO 2023115120 A1 WO2023115120 A1 WO 2023115120A1
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dose
mlc
radiation
planned
leaf
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Lars MEJNERTSEN
Paul Keall
Doan Trang NGUYEN
Emily HEWSON
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University of Sydney
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University of Sydney
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1042—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy with spatial modulation of the radiation beam within the treatment head
    • A61N5/1045—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy with spatial modulation of the radiation beam within the treatment head using a multi-leaf collimator, e.g. for intensity modulated radiation therapy or IMRT
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1064—Monitoring, verifying, controlling systems and methods for adjusting radiation treatment in response to monitoring
    • A61N5/1065—Beam adjustment
    • A61N5/1067—Beam adjustment in real time, i.e. during treatment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1071—Monitoring, verifying, controlling systems and methods for verifying the dose delivered by the treatment plan
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1077—Beam delivery systems
    • A61N5/1081—Rotating beam systems with a specific mechanical construction, e.g. gantries
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1071—Monitoring, verifying, controlling systems and methods for verifying the dose delivered by the treatment plan
    • A61N2005/1072—Monitoring, verifying, controlling systems and methods for verifying the dose delivered by the treatment plan taking into account movement of the target
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • A61N5/1031—Treatment planning systems using a specific method of dose optimization
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • A61N5/1036—Leaf sequencing algorithms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • A61N5/1037—Treatment planning systems taking into account the movement of the target, e.g. 4D-image based planning
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1049—Monitoring, verifying, controlling systems and methods for verifying the position of the patient with respect to the radiation beam
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Definitions

  • MLC multi -leaf collimator
  • MLC tracking The closest clinical product to MLC tracking is gating, in which a radiation beam is turned on and off when the tumor comes within the beam. Gating, by definition, is time inefficient and is also challenging in situations where there is a drift in the tumor position, where the entire patient needs to be moved to realign the radiation beam and the cancer.
  • Example systems, methods, and apparatus are disclosed herein for a dose-based optimization for multi-target MLC tracking during radiation therapy.
  • the MLC tracking system, method, and apparatus disclosed herein accounts for a radiation dose that is delivered to a tumor throughout a treatment, explicitly providing an optimal dose to the target tumor while minimizing radiation damage to healthy tissue.
  • the MLC tracking system is configured to track multiple patient tissue targets. The tracking of independent targets provides simultaneous accounting of the independent motion of multiple cancer targets, which provides a more accurate dose optimization.
  • the dose optimization disclosed herein accounts for a moving patient anatomy by accumulating dose in silico during treatment, and adapting a MLC to minimize errors due to this motion. Using a number of optimizations, the disclosed systems, methods, and apparatus achieve this optimization in real time, allowing it to be used during a standard radiotherapy treatment.
  • the methodology includes the following steps. First, a planned dose volume is calculated using a MLC plan in an un-shifted dose volume. Additionally, dose voxels are assigned to certain patient anatomical targets based on a three-dimensional contour of the target that is obtained from a radiotherapy treatment plan. During treatment, the planned dose for each time step is calculated.
  • the dose voxels are shifted independently such that the voxels that are assigned to a target are shifted according to that target’s motion.
  • the 3D dose is integrated onto a 2D Beam’s Eye View grid.
  • the MLC aperture next is fitted by minimizing a cost function to minimize a difference between the delivered and ideal dose distributions. With the fitted leaf positions, the delivered dose is calculated and accumulated. The gantry position and MLC apertures are updated, and the process repeats until the treatment has finished.
  • a method for radiation dose-based optimization for multi-target multi-leaf collimator (“MLC”) tracking includes (i) calculating, via a computer system, a planned radiation dose volume using an MLC plan in an un-shifted dose volume, (ii) assigning, via the computer system, dose voxels from the planned radiation dose volume to two or more patient tissue targets for identifying target positions for motion tracking, (iii) shifting, via the computer system, the dose voxels such that the dose voxels that are assigned to one of the patient tissue targets are shifted according to motion of the respective patient tissue target, (iv) integrating, via the computer system, a delivered three-dimensional dose into a two-dimensional beam’s eye view (“BEV”) grid for each of the dose voxels, (v) fitting, via the computer system and the radiation machine, for each leaf track, an
  • the dose voxels from the planned radiation dose volume are assigned to the two or more patient tissue targets using a three- dimensional contour of each patient tissue target obtained from a radiotherapy treatment plan.
  • the target positions include rotational or deformation changes to the patient tissue target(s) and/or organs-at-risk.
  • the motion tracking of the target positions is queried by at least one of marker tracking, computed tomography (“CT”) imaging, or magnetic resonance imaging.
  • CT computed tomography
  • the cost function is configured to adapt the MLC leaves to best conform to the planned dose by minimizing a difference between the planned dose and the accumulated delivered dose for each of the dose voxels.
  • the method further includes causing the radiation machine to deliver the planned dose as the delivered dose.
  • the MLC aperture is optimized based on the radiation dose to be delivered for a remainder of the treatment as well as the previously accumulated delivered dose.
  • the MLC is replaced by an active scanning beam direction device.
  • the patient tissue targets include tumors, lymph nodes, prostrate tissue, lung tissue, heart tissue, muscle tissue, or kidney tissue.
  • an apparatus for radiation dose-based optimization for multi-target multi-leaf collimator (“MLC”) tracking includes a memory device storing instructions and a processor communicatively coupled to the memory device.
  • the processor is configured to execute the instructions causing the processor to (i) calculate a planned radiation dose using an MLC plan in an un-shifted dose volume, (ii) acquire, using a radiation machine, target positions of two or more patient tissue targets through motion tracking, (iii) assign dose voxels from the planned radiation dose volume to the two or more patient tissue targets for identifying target positions for motion tracking, (iii) shift the dose voxels such that the dose voxels that are assigned to one of the patient tissue targets are shifted according to motion of the respective patient tissue target, (iv) integrate a delivered three- dimensional dose into a two-dimensional beam’s eye view (“BEV”) grid for each of the dose voxels, (v) fit, using the radiation machine for each leaf track, an MLC aperture by minimizing a cost function, (vi) calculate and accumulate a delivered dose based on the fitted leaf positions of the MLC, and (vii) update a gantry position and MLC leaf positions to update a next planned dose.
  • BEV beam eye
  • the dose voxels from the planned radiation dose volume are assigned to the two or more patient tissue targets using a three-dimensional contour of each patient tissue target obtained from a radiotherapy treatment plan.
  • the processor is communicatively coupled to the radiation machine.
  • the processor is configured to repeat the steps of (i) to (vii) at least once for a radiation therapy.
  • the cost function is configured to adapt the MLC leaves to best conform to the planned dose by minimizing a difference between the planned dose and the accumulated delivered dose.
  • the processor is configured to cause the radiation machine to deliver the planned dose as the delivered dose.
  • the motion tracking of the target positions is queried by at least one of marker tracking, computed tomography (“CT”) imaging, or magnetic resonance imaging.
  • CT computed tomography
  • the MLC aperture is optimized based on the radiation dose to be delivered for a remainder of the treatment as well as the previously accumulated delivered dose.
  • the processor is configured to minimize the cost function taking into account radiation beam divergence and attenuation.
  • any of the structure and functionality disclosed in connection with Figs. 1 to 17, or portions thereof, may be included or combined with any of the other structure and functionality disclosed in connection with Figs. 1 to 17.
  • Fig. 1 is a diagram of a radiation treatment system including a radiation machine and a computer system, according to an example embodiment of the present disclosure.
  • Fig. 2 is a diagram of MLC leaves, according to an example embodiment of the present disclosure.
  • Fig. 3 shows a comparison between known fluence optimization and a dose optimization performed by the computer system of Fig. 1, according to an example embodiment of the present disclosure.
  • Fig. 4 is a diagram of an example MLC tracking process for dose optimization using the computer system of Fig. 1, according to an example embodiment of the present disclosure.
  • Fig. 5 is a diagram of an example MLC tracking process showing consecutive timesteps where a dose is updated, according to an example embodiment of the present disclosure.
  • Fig. 6 is a diagram of an integration of a three-dimensional dose volume onto a beam’s eye view (“BEV”) plane for a single leaf track, according to an example embodiment of the present disclosure.
  • Fig. 7 is a diagram showing a dose distribution mapped to a single leaf track, according to an example embodiment of the present disclosure.
  • Fig. 8 is a diagram showing a difference between a planned and a delivered dose, potential dose error, and a cost as a function of trailing (xr) and leading (XL) MLC leaf position, according to an example embodiment of the present disclosure.
  • Fig. 9 is a diagram of a sample motion trace, and a comparison of dose error over treatment time for a given fraction for the dose optimization disclosed herein, known fluence optimization, and without optimization, according to an example embodiment of the present disclosure.
  • Fig. 10 is a diagram that shows dose and dose errors at an end of a given fraction, showing axial slices of the planned dose and dose errors, according to an example embodiment of the present disclosure.
  • Fig. 11 is a diagram that shows a comparison between fluence, dose optimization, and a baseline in regard to dose errors for a given fraction, according to an example embodiment of the present disclosure.
  • Fig. 12 is a diagram of graphs that show y failure rates at a threshold of 2mm/2% for doses greater than 10% of the planned dose between fluence, dose optimization, and a baseline, according to an example embodiment of the present disclosure.
  • Fig. 13 is a diagram that shows differences in the Dose Volume Histogram metrics computed using an independent dose calculation algorithm, according to an example embodiment of the present disclosure.
  • Fig. 14 is a flow diagram of an example procedure for dose-optimized multi-target MLC tracking, according to an example embodiment of the present disclosure.
  • Fig. 15 is a diagram of an example multi -target MLC tracking process showing consecutive time-steps where a dose is updated, according to an example embodiment of the present disclosure.
  • Fig. 16 is a diagram showing a range of prostate motion traces during which a pelvic lymph node target was kept static, according to an example embodiment of the present disclosure.
  • Fig. 17 is a diagram of graphs that shows y failure rates for multi -target tracking, according to an example embodiment of the present disclosure.
  • the example methods, systems, and apparatus may be performed using the radiation treatment system 10 of Fig. 1, according to an example embodiment of the present disclose.
  • the system 10 includes a radiation machine 12 to perform a radiation therapy.
  • a radiation therapy uses ionizing radiation (e.g., a radiotherapy beam), generally as part of cancer treatment to control or kill malignant cells.
  • the radiation machine 12 delivers the ionizing radiation using a linear accelerator.
  • the radiation machine 12 also includes a MLC 14, which includes a collimator or other beam-limiting device that is made of individual "leaves" of a high atomic numbered material, such as tungsten.
  • leaves shown as right leaves 201 to 209 and left leaves 101 to 109 can be moved in independently (by a processor of the radiation machine 12) in and out of the path of a radiotherapy beam 204 to shape the beam and/or vary an intensity of the beam.
  • the radiation machine 12 also includes a detector 16 for sensing a dose of radiation delivered to a patient.
  • the system 10 also includes a computer system 20.
  • the computer system 20 includes at least one monitor 22 and a processor 24.
  • the example processor 24 is configured to accept radiation treatment parameters for controlling the radiation machine 12.
  • the processor 24 is also configured to display a treatment status via one or more user interfaces displayed on the monitor 22. In some instances, the processor 24 may display a user interface on the monitor 22 for accepting radiation treatment parameters, such as a planned dose.
  • the computer system 20 may also include a memory device 26, which may comprise any computer-readable medium, including random access memory (“RAM”), read only memory (“ROM”), flash memory, magnetic or optical disks, optical memory, or other storage media.
  • the memory device 26 includes computer-readable instructions. Execution of those instructions by the processor 24 of the computer system 24 causes the operations to be carried out as described herein. In some instances, the instructions may comprise a software program or application.
  • the example computer system 20 of Fig. 1 is configured to provide an improved method for MLC tracking.
  • Motion in patient anatomy causes a reduction in dose delivered to a target, while increasing dose to healthy tissue.
  • MLC tracking adapts for this intrafraction motion.
  • the computer system 20 includes a motion adaptation algorithm (dose optimization) that accounts for the moving patient anatomy by accumulating dose data during treatment.
  • the planned dose is calculated in the patient volume, then shifted in the direction of motion.
  • the MLC aperture is optimized by minimizing the difference between the accumulation of the delivered and planned dose.
  • the delivered dose is calculated with the new apertures. This process repeats until a radiation treatment finishes.
  • Fig. 3 shows a comparison between known fluence optimization 300 and the dose optimization 350 performed by the computer system 20.
  • fluence optimization 300 the MLC leaf positions are fitted to the shifted aperture in a two-dimensional space - where under/overdose minimization on the fluence is performed.
  • the known fluence optimization 300 does not account for the three-dimensional nature of the radiation dose and/or patient anatomy. Further, error accumulation in two-dimensional space provides modest only improvements.
  • the dose optimization 350 adjusts the MLC 14 leaf positions based on a three-dimensional calculation of a dose delivered to a target.
  • a dose is accumulated using a line-of-sight dose calculation.
  • dose optimization can readily be extended to include radiation beam divergence and attenuation.
  • the dose optimization 350 accumulates and accounts for errors due to finite leaf widths and leaf velocities, and adapts for the evolution of dose errors in a beam’s eye view due to motion and gantry rotation.
  • the dose optimization 350 can be used to a wide array of radiotherapy treatments: VMAT, IMRT, etc.
  • the dose optimization 350 can be extended to multi -target/O AR sparing applications readily by weighting of tissue voxels to target/avoid certain regions. The dose optimization 350 accordingly enables real-time adaptive re-planning for radiation treatments.
  • Fig. 4 is a diagram of an example MLC tracking process 400 for dose optimization using the computer system 20 of Fig. 1, according to an example embodiment of the present disclosure.
  • a planned dose is calculated via the computer system 20 using a MLC plan in an un-shifted dose volume.
  • the computer system 20 and/or the radiation machine 12 at step 2 acquires a target position through motion tracking, and shifts the dose volume.
  • the computer system 20 then at step 3 integrates the 3D dose onto a 2D beam’s eye view (“BEV”) grid.
  • BEV eye view
  • the computer system 20 in conjunction with the radiation machine 12, for each leaf track fits the MLC 14 aperture by minimizing a cost function (disclosed below).
  • the delivered dose is calculated and accumulated by the computer system 20 in conjunction with the radiation machine 12.
  • the computer system 20 updates a gantry position and MLC apertures. The example process 400 then loops back to step 1 until the treatment has finished.
  • Two grids are defined in this methodology using the computer system 20: a three- dimensional set of points, spaced throughout a patient’s body, on which dose is accumulated; and a two-dimensional BEV grid, on which the MLC 14 leaves are optimized.
  • the three-dimensional set of points referred to as the dose points, spans a small sub-volume of the patient’s body that is in the line-of-sight of the radiation beam, i.e. it encompasses all the points that can be irradiated by the radiation beam.
  • the dose points are typically placed with uniform spacing of 2 mm (by the computer system 20), within a cylinder whose axis is symmetric about gantry rotation axis, as illustrated by the cylinder in Fig. 4.
  • the cylinder’s dimensions are set using the size of the jaws to ensure the dose points are predominantly in the aperture of the MLC. However, these points may have no underlying topology/connectivity, and thus can be molded into any shape required.
  • the two-dimensional BEV grid is aligned with the MLC itself (via the computer system 20), with each leaf track corresponding to a set of pixels on the grid along a ya direction.
  • each leaf track XB di recti on
  • the grid is uniformly spaced.
  • Each of these pixels correspond to an integral of the 3D dose points, along the direction of the normal to the BEV grid, the ZB direction.
  • the dose points and BEV grid is generated, based on the size of the jaws of a given Digital Imaging and Communications in Medicine (“DICOM”) plan in the computer system 20.
  • the DICOM plan is also loaded to provide the planned MLC leaf positions and gantry angles for the planned dose calculation.
  • the interfraction motion (step 2 of Fig. 4) is obtained in real time using a tracking method (such as fluoroscopy imaging).
  • a tracking method such as fluoroscopy imaging.
  • a motion trace may be imported from file, and simulated to provide a similar level of information as would be provided by a motion trace obtained from a tracking method.
  • Equation (1) describes a line of sight dose calculation performed by the computer system 20, where the gantry, collimator and leaf positions are considered constant within a set time-step, A t.
  • VMAT/IMRT functionality is enabled by numerous of these dose calculations, then stepping forward in time by A t, where the MLC leaves and gantry is moved.
  • d is the dose at position (x, y, z) in the IEC Beam Limiting Device (“BLD”) coordinate system
  • (dot) the dose rate
  • y is the lower bound of leaf track j
  • a t is the time-step
  • XT and XL are the trailing and leading leaf positions of leaf track j.
  • the dose is then updated again with these new positions, and the process begins anew.
  • the circle indicates an axial dose volume containing a set of dose points, colored by the dose value.
  • the MLC leaf pair is shown as the gray straight lines 502, with dose points between the lines receiving dose.
  • the gray straight lines 502 indicate the position of an MLC leaf pair (corresponding to this axial slice); points within the aperture receive dose as per Equation (1).
  • Each panel moving from left to right (1 to 6), shows consecutive time-steps where the dose is updated, indicated by the dose within the leaves changing color from white to black.
  • both the position of the dose point and the position of the MLC leaves are a function of time, t.
  • the dose points change due to the relative movement of the gantry about the dose points, and due to intrafraction motion.
  • the MLC leaf positions extend/contract in the collimator plane in a typical IMRT or VMAT treatment.
  • Dose optimization aims to adapt the MLC leaves to best conform to the planned dose. This is achieved through the computer system 20 by minimizing the difference between the delivered dose (dd) and the planned dose (dp), accumulated up until the current treatment time, as shown in Equation (2) below, where C is the cost and the integration is over the patient volume.
  • Equation (2) Due to the simplified dose calculation, Equation (2) is simplified. The use of a line of sight stationary dose calculation allows each dose point to map to a unique leaf track, i.e. those points are only ‘dosed’ by a given leaf track. This reduces the optimization problem, with each given leaf track (with index j) having its own associated cost, as shown below in Equation (3), where D is the dose integrated along the ZB direction.
  • Cj is the cost function for a given leaf track with index j
  • a Dj is the dose to be delivered in time-step n
  • e is the dose difference: the difference between delivered dose up to the previous time-step n - 1 and the planned dose at this time-step Since each leaf track has an independent cost function, the j index is omitted from further expressions.
  • the equations have dealt with continuous integrals of the dose volume.
  • the dose volume is made up of discrete points in three-dimensional space.
  • the quantities A d and e are integrated from the three-dimensional dose points to the two-dimensional BEV grid, illustrated in Fig. 6 for a single leaf track.
  • the BEV grid is uniformly spaced along the XB axis.
  • the ya axis spacing follows the spacing of the MLC leaf tracks, e.g. for a Varian Millennium 120 leaf MLC, the first 10 leaf tracks have a width of 10 mm, then forty 5 mm width leaves, then a final ten 10 mm leaf widths.
  • the integration is a sum of all dose points inside those bins.
  • the integral of the dose points D n is given by the sum of all the dose points within those bounds. This is illustrated for the annotated integration bin by the first set of crosses in Fig. 6.
  • a D and E in Equation (4) are expressed as follows in Equations (5) and
  • Equation (7) The discrete version of Equation (4) is given as follows in Equation (7):
  • Equation (7) the summation is along a leaf track (over pixels in the XB direction) and Ax is the pixel size.
  • XT/XL is the trailing/leading leaf position
  • xi is the lower bound of the BEV pixel
  • ir is the index of the BEV pixel containing the position XT, (XIT ⁇ XT ⁇ XIT+I), and similarly for the leading leaf position (L).
  • a method is outlined by which to fit the leaf, update the dose, and perform multiple iterations of the optimization.
  • a 3D dose volume that is mid-fraction.
  • a dose has already been delivered to the dose points, and the plan provides a target dose distribution.
  • the delivered dose and the planned dose are mismatched. This is illustrated in an example in Fig. 7, showing the dose distribution mapped to a single leaf track.
  • Graph 702 shows a target planned dose, which has more dose that the delivered dose shown in graph 704. This is due to the planned dose being one time-step ahead.
  • the graph 706 shows the dose difference, with under-dose in white 708 and overdose in black 710.
  • Fig. 8 also shows a difference between delivered and planned doses. It indicates where the distribution is under-and overdosed, suggesting where dose can be recouped. Areas of under-dose are shown in gray 802, and overdose in black 804.
  • the leaf fitting algorithm is configured to place the leaf aperture in the regions of gray so as to reduce the underdose, while avoiding areas of overdose as much as possibly, as it would only exacerbate the overdose.
  • the dose difference is used for leaf fitting.
  • the dose error is shown in Fig. 8. After fitting, the delivered dose is updated with the new leaf positions to get the delivered dose at time-step n (fitted dose). The planned dose is then subtracted from the fitted dose to show the dose error.
  • the graph on the right side of Fig. 8 shows the cost as a function of trailing (xr) and leading (XL) leaf position. The fitted leaf positions are at the point where the cost is at a minimum.
  • the cost function is plotted (Equation (7)) as a function of the leading, XL, and trailing, xr, leaf positions, as shown in Fig. 8.
  • the fitting procedure does not take finite leaf velocities into account; i.e. the fitted leaf positions can ‘jump’ to the position that minimizes the cost function (equation (7)), regardless of whether the MLC leaves can reach that position in the allotted time-step.
  • the MLC leaf speeds typically of up to 3.6 cm/s, are considered slow enough to adversely impact performance. This can be managed by either bounding the fitting region to only include leaf positions that are attainable and/or setting ‘target’ leaf positions, that MLC moves towards, but does not reach in the time-step.
  • the second method does not place limits on the leaf fitting algorithm, but constrains the motion of the leaves. If the algorithm returns leaf positions out of reach, the leaf positions will move towards the target fitted leaf positions. This allows the aperture to move to new regions, but means as the leaves move toward this target, they do not dose optimally. However, this dose error is accumulated and hence will factor into the leaf fitting at later timesteps.
  • the disclosed method was benchmarked against a prostate cancer VMAT treatment dataset with observed intrafraction motion. MLC tracking was applied to fifteen fractions with two arcs each, comparing three methods: dose optimization, fluence optimization, and without optimization.
  • dose optimization fluence optimization
  • the dose error fraction of the total planned dose is calculated and plotted as a function of treatment time, as seen in Fig. 9, along with the corresponding motion trace. This shows the dose errors are much lower than in the no optimization case.
  • Fig. 9 shows that the dose error increases as treatment progresses, but dose optimization 1002 is able to minimize the error, keeping the dose error lower than other optimization methods (fluence optimization 1004, and without optimization 1006).
  • Fig. 10 shows dose and dose errors at the end of a given fraction, showing axial slices of the planned dose and dose errors.
  • a graph shows the total planned dose, as calculated by the dose calculation. It is also shown that in dose optimization, the dose errors are much lower than in the no optimization case.
  • the dose error is calculated using the dose calculation as given in Equation (1). This is a sum over the entire dose volume of the absolute difference between the delivered and planned doses, normalized over the total planned dose, as given by Equation 10, shown below.
  • Fig. 13 is a diagram of line graphs and violin plots of the aggregate data of Figs. 9 and 10, giving an overview of how well each method performs overall.
  • the dashed line in the middle of each plot indicates the median, with the two dashed lines either side showing the quartiles.
  • the width of the plot shows the distribution of points, with thickness indicating the number of points at that value.
  • dose optimization performs for the fractions in this analysis, indicated by the lower mean (middle dashed line) dose error. Both fluence and no optimization both have a much larger spread of dose errors, than dose optimization. On average, dose optimization achieves a dose error of 3.4% ⁇ 0.6%, improving over fluence optimization (4.4% ⁇ 1.1%) and no tracking (7.2% ⁇ 3.4%).
  • the y metric is a common way to compare to dose distributions. Rather than just comparing the difference in dose at a given point in the dose distribution, as is done with the dose errors, y also compares points around the given dose point. If the dose distribution nearby the reference point is within a certain threshold, that point is said to have passed the y test. By determining the y pass/fail rate for each point in the 3D dose distribution, the pass/fail rate for the entire fraction is obtained.
  • Common thresholds for y tests have a distance threshold 3mm radius from the dose point, and 3% difference from the reference dose, but also step progressively lower to 2mm/2% and lmm/1%. In these results, 2mm/2% is used.
  • Fig. 12 is a diagram of graphs that show y fail rates at a threshold of 2mm/2% for doses greater than 10% of the planned dose.
  • a first graph (1) shows the results for each fraction in the validation.
  • a second graph (2) shows violin plots of the aggregate data in the first graph.
  • the dashed line in the middle indicates the median, with the dashed lines either side showing the quartiles.
  • the width of the plot shows the distribution of points, with thickness indicating the number of points at that value.
  • Fig. 12 shows a similar picture to that of the dose error results where dose optimization performs better for every case, as is shown by the data for the individuals’ cases in the graph and the aggregate data in the same graph. Dose optimization achieves y fail rates of 4.7% ⁇ 1.2%, improving over fluence optimization (7.5% ⁇ 2.9%) and no tracking (15.3% ⁇ 12.9%).
  • Results of this are shown in Fig. 13, showing box plots of the five metrics for dose- optimized apertures, fluence-optimized apertures, and the base case of without tracking.
  • the box plots show the difference from planned metric, (e.g. a 5% difference means the metric for the optimized case is 5% higher than planned).
  • Fig. 14 is a flow diagram of an example procedure 1400 for dose-optimized multitarget MLC tracking, according to an example embodiment of the present disclosure. Although the procedure 1400 is described with reference to the flow diagram illustrated in Fig. 14, it should be appreciated that many other methods of performing the steps associated with the procedure 1400 may be used.
  • the order of many of the blocks may be changed, certain blocks may be combined with other blocks, and many of the blocks described may be optional.
  • the actions described in the procedure 1400 are specified by one or more instructions and may be performed among multiple devices including, for example the radiation treatment system 10, the radiation machine 12, and/or the computer system 20.
  • the example procedure 1400 begins when the computer system 20 receives a radiotherapy treatment plan 1401 and identifies two or more patient tissue targets (e.g., tumors or organs of interest) (block 1402).
  • the identification of the patient tissue targets includes obtaining or determining a three-dimensional contour of the patient tissue targets from the radiotherapy treatment plan.
  • the patient targets may include tumors, lymph nodes, lung tissue, muscle tissue, prostate tissue, kidney tissue, cancer tissue, etc.
  • the computer system 20 identifies lymph nodes 1502 and prostate tissue 1504 as patient tissue targets. While the example procedure 1400 is described in connection with two patient tissue targets, it should be appreciated that the example procedure 1400 may provide for tracking of three, four, five, or more targets based on which patient tissue is to receive radiation doses.
  • the computer system 20 calculates a planned radiation dose volume using an MLC plan in an un-shifted dose volume (block 1404).
  • the computer system 20 then assigns dose voxels from the planned radiation dose volume to the two or more patient tissue targets (block 1406). This assignment provides for identifying target positions for motion tracking. As shown in Fig. 15 at Event B, this step may include accumulating radiation doses up to a current time using the planned MLC aperture.
  • the computer system 20 may also calculate a planned dose for each time step of a radiation procedure.
  • the computer system 20 next receives information 1407 that is indicative of a movement of a patient’s tissue (block 1408).
  • the information 1407 may include data from marker tracking, an analysis of computed tomography (“CT”) images, or an analysis of magnetic resonance imaging.
  • CT computed tomography
  • the computer system 20 uses the movement information 1407 to shift the dose voxels such that the dose voxels that are assigned to one of the patient tissue targets are shifted according to motion of the respective patient tissue target (block 1410).
  • Event C of Fig. 15 shows an example of the shifting of the dose voxels based on detected patient tissue target movement.
  • the computer system 20 then integrates a delivered three-dimensional dose into a two-dimensional beam’s eye view (“BEV”) grid for each of the dose voxels (block 1412).
  • BEV beam’s eye view
  • the computer system 20 fits for each leaf track of the radiation machine 12, an MLC aperture by minimizing a cost function, as described above (block 1414).
  • the adapted MLC apertures for the different leaf track are calculated to minimize overdosing and underdosing of patient tissue including the tissue targets.
  • the computer system 20 calculates, accumulates, or updates a delivered dose based on the fitted leaf positions of the MLC (block 1416).
  • the radiation machine 12 administers a dose using the adapted MLC apertures for each leaf and the computer system 20 models the administration of the radiation dose to the patient tissue targets based on the known adapted MLC aperture.
  • the computer system 20 then updates a gantry position and MLC leaves of the radiation machine 12 to update a next planned dose.
  • the example procedure 1400 may then end or repeat for subsequent radiation doses and/or treatments.
  • the dose-optimized multi-target tracking method of Fig. 14 was benchmarked against a previously used geometric-based multi-target tracking method, and the current standard of care for patients with locally advanced prostate cancer treated using radiotherapy.
  • the previously used geometric-based MLC tracking method separates the planned MLC apertures into sections that belong to individual targets and shifts each section of the aperture to correspond to each targets’ new positions.
  • Current standard of care treatment is not able to adapt to individual targets motion and is typically set up to a prostate’s initial position at the beginning of treatment.
  • Fig. 17 shows the y failure rates for the prostate and lymph node targets, for each treatment method and three different prostate motions (a, b and c). Dose-optimized multi-target tracking was able to provide better treatment accuracy compared to both geometric-based tracking and standard of care, where motion was not tracked. This was the case for the both the prostate and lymph node targets, across all three motions that were simulated. This extension shows that the method can be used for multiple targets, but also for more complex motions including using deformation vector fields.

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Abstract

Sont divulgués des procédés, des appareils et des systèmes pour une optimisation basée sur la dose relative à un suivi de collimateur multilame (« MLC ») multicible pendant une radiothérapie. Dans un exemple, un procédé consiste à calculer un volume de dose de rayonnement planifié à l'aide d'un plan MLC et à attribuer des voxels de dose du volume de dose de rayonnement planifié à au moins deux cibles de tissu de patient. Le procédé consiste également à décaler des voxels de dose de telle sorte que les voxels de dose qui sont attribués à l'une des cibles de tissu de patient soient décalés en fonction du mouvement de la cible de tissu de patient respective, et à intégrer une dose tridimensionnelle administrée dans une grille bidimensionnelle de vue de faisceau (« BEV ») pour chacun des voxels de dose. Le procédé consiste en outre à ajuster, pour chaque piste de lame, une ouverture MLC par réduction au minimum d'une fonction de coût, à accumuler une dose administrée sur la base des positions de lames ajustées, et à mettre à jour une position de portique et des positions de lames MLC pour mettre à jour une dose planifiée suivante.
PCT/AU2022/051543 2021-12-21 2022-12-20 Optimisation basée sur la dose pour suivi de collimateur multilame (« mlc ») multicible pendant une radiothérapie, procédés et appareil Ceased WO2023115120A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060256915A1 (en) * 2005-05-13 2006-11-16 Karl Otto Method and apparatus for planning and delivering radiation treatment
US20080002811A1 (en) * 2006-06-29 2008-01-03 Allison John W Treatment delivery optimization
WO2011005329A2 (fr) * 2009-07-09 2011-01-13 The Board Of Trustees Of The Leland Stanford Junior University Procédé et système de suivi cible en temps réel au moyen d'un collimateur multilames dynamique (dmlc), à adaptation de lames pour compensation de mouvement optimal
US20140005464A1 (en) * 2011-03-15 2014-01-02 Koninklijke Philips N.V. Studying dosimetric impact of motion to generate adaptive patient-specific margins in ebrt planning
US20170200276A1 (en) * 2016-01-13 2017-07-13 Varian Medical Systems International Ag Systems and methods for evaluating motion tracking for radiation therapy
WO2021121622A1 (fr) * 2019-12-20 2021-06-24 Elekta Ab (Publ) Algorithme d'accumulation de dose adaptative

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060256915A1 (en) * 2005-05-13 2006-11-16 Karl Otto Method and apparatus for planning and delivering radiation treatment
US20080002811A1 (en) * 2006-06-29 2008-01-03 Allison John W Treatment delivery optimization
WO2011005329A2 (fr) * 2009-07-09 2011-01-13 The Board Of Trustees Of The Leland Stanford Junior University Procédé et système de suivi cible en temps réel au moyen d'un collimateur multilames dynamique (dmlc), à adaptation de lames pour compensation de mouvement optimal
US20140005464A1 (en) * 2011-03-15 2014-01-02 Koninklijke Philips N.V. Studying dosimetric impact of motion to generate adaptive patient-specific margins in ebrt planning
US20170200276A1 (en) * 2016-01-13 2017-07-13 Varian Medical Systems International Ag Systems and methods for evaluating motion tracking for radiation therapy
WO2021121622A1 (fr) * 2019-12-20 2021-06-24 Elekta Ab (Publ) Algorithme d'accumulation de dose adaptative

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