M Chen

Fast free-form deformable registration via calculus of variations

In this paper, we present a fully automatic, fast and accurate deformable registration technique. This technique deals with free-form deformation. It minimizes an energy functional that combines both similarity and smoothness measures. By using calculus of variations, the minimization problem was represented as a set of nonlinear elliptic partial differential equations (PDEs). A Gauss-Seidel finite difference scheme is used to iteratively solve the PDE. The registration is refined by a multi-resolution approach. The whole process is fully automatic. It takes less than 3 min to register two three-dimensional (3D) image sets of size 256 x 256 x 61 using a single 933 MHz personal computer. Extensive experiments are presented. These experiments include simulations, phantom studies and clinical image studies. Experimental results show that our model and algorithm are suited for registration of temporal images of a deformable body. The registration of inspiration and expiration phases of the lung images shows that the method is able to deal with large deformations. When applied to the daily CT images of a prostate patient, the results show that registration based on iterative refinement of displacement field is appropriate to describe the local deformations in the prostate and the rectum. Similarity measures improved significantly after the registration. The target application of this paper is for radiotherapy treatment planning and evaluation that incorporates internal organ deformation throughout the course of radiation therapy. The registration method could also be equally applied in diagnostic radiology.

Automatic re-contouring in 4D radiotherapy

Delineating regions of interest (ROIs) on each phase of four-dimensional (4D) computed tomography (CT) images is an essential step for 4D radiotherapy. The requirement of manual phase-by-phase contouring prohibits the routine use of 4D radiotherapy. This paper develops an automatic re-contouring algorithm that combines techniques of deformable registration and surface construction. ROIs are manually contoured slice-by-slice in the reference phase image. A reference surface is constructed based on these reference contours using a triangulated surface construction technique. The deformable registration technique provides the voxel-to-voxel mapping between the reference phase and the test phase. The vertices of the reference surface are displaced in accordance with the deformation map, resulting in a deformed surface. The new contours are reconstructed by cutting the deformed surface slice-by-slice along the transversal, sagittal or coronal direction. Since both the inputs and outputs of our automatic re-contouring algorithm are contours, it is relatively easy to cope with any treatment planning system. We tested our automatic re-contouring algorithm using a deformable phantom and 4D CT images of six lung cancer patients. The proposed algorithm is validated by visual inspections and quantitative comparisons of the automatic re-contours with both the gold standard segmentations and the manual contours. Based on the automatic delineated ROIs, changes of tumour and sensitive structures during respiration are quantitatively analysed. This algorithm could also be used to re-contour daily images for treatment evaluation and adaptive radiotherapy.

Accurate convolution/superposition for multi-resolution dose calculation using cumulative tabulated kernels

Convolution/superposition (C/S) is regarded as the standard dose calculation method in most modern radiotherapy treatment planning systems. Different implementations of C/S could result in significantly different dose distributions. This paper addresses two major implementation issues associated with collapsed cone C/S: one is how to utilize the tabulated kernels instead of analytical parametrizations and the other is how to deal with voxel size effects. Three methods that utilize the tabulated kernels are presented in this paper. These methods differ in the effective kernels used: the differential kernel (DK), the cumulative kernel (CK) or the cumulative-cumulative kernel (CCK). They result in slightly different computation times but significantly different voxel size effects. Both simulated and real multi-resolution dose calculations are presented. For simulation tests, we use arbitrary kernels and various voxel sizes with a homogeneous phantom, and assume forward energy transportation only. Simulations with voxel size up to 1 cm show that the CCK algorithm has errors within 0.1% of the maximum gold standard dose. Real dose calculations use a heterogeneous slab phantom, both the broad (5 x 5 cm2) and the narrow (1.2 x 1.2 cm2) tomotherapy beams. Various voxel sizes (0.5 mm, 1 mm, 2 mm, 4 mm and 8 mm) are used for dose calculations. The results show that all three algorithms have negligible difference (0.1%) for the dose calculation in the fine resolution (0.5 mm voxels). But differences become significant when the voxel size increases. As for the DK or CK algorithm in the broad (narrow) beam dose calculation, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 10% (7%) of the maximum dose. As for the broad (narrow) beam dose calculation using the CCK algorithm, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 1% of the maximum dose. Among all three methods, the CCK algorithm is demonstrated to be the most accurate one for multi-resolution dose calculations.

Real-time respiration monitoring using the radiotherapy treatment beam and four-dimensional computed tomography (4DCT)—a conceptual study

Real-time knowledge of intra-fraction motion, such as respiration, is essential for four-dimensional (4D) radiotherapy. Surrogate-based and internal-fiducial-based methods may suffer from one or many drawbacks such as false correlation, being invasive, delivering extra patient radiation, and requiring complicated hardware and software development and implementation. In this paper we develop a simple non-surrogate, non-invasive method to monitor respiratory motion during radiotherapy treatments in real time. This method directly utilizes the treatment beam and thus imposes no additional radiation to the patient. The method requires a pre-treatment 4DCT and a real-time detector system. The method combines off-line processes with on-line processes. The off-line processes include 4DCT imaging and pre-calculating detector signals at each phase of the 4DCT based on the planned fluence map and the detector response function. The on-line processes include measuring detector signal from the treatment beam, and correlating the measured detector signal with the pre-calculated signals. The respiration phase is determined as the position of peak correlation. We tested our method with extensive simulations based on a TomoTherapy machine and a 4DCT of a lung cancer patient. Three types of simulations were implemented to mimic the clinical situations. Each type of simulation used three different TomoTherapy delivery sinograms, each with 800 to 1000 projections, as input fluences. Three arbitrary breathing patterns were simulated and two dose levels, 2 Gy/fraction and 2 cGy/fraction, were used for simulations to study the robustness of this method against detector quantum noise. The algorithm was used to determine the breathing phases and this result was compared with the simulated breathing patterns. For the 2 Gy/fraction simulations, the respiration phases were accurately determined within one phase error in real time for most projections of the treatment, except for a few projections at the start and end of the treatment in which beam intensities were extremely low. At 2 cGy/fraction dose level, the method can still determine the respiration phase very well with less than 10% of projections having more than two phases (approximately 1 s) error. This technique can also be applied in other delivery systems such as orthogonal x-ray systems, although in those cases it would entail the delivery of additional non-treatment radiation.

Impact of Image Noise on Gamma Index Calculation

Purpose: The Gamma Index defines an asymmetric metric between the evaluated image and the reference image. It provides a quantitative comparison that can be used to indicate sample-wised pass/fail on the agreement of the two images. The Gamma passing/failing rate has become an important clinical evaluation tool. However, the presence of noise in the evaluated and/or reference images may change the Gamma Index, hence the passing/failing rate, and further, clinical decisions. In this work, we systematically studied the impact of the image noise on the Gamma Index calculation. Methods: We used both analytic formulation and numerical calculations in our study. The numerical calculations included simulations and clinical images. Three different noise scenarios were studied in simulations: noise in reference images only, in evaluated images only, and in both. Both white and spatially correlated noises of various magnitudes were simulated. For clinical images of various noise levels, the Gamma Index of measurement against calculation, calculation against measurement, and measurement against measurement, were evaluated. Results: Numerical calculations for both the simulation and clinical data agreed with the analytic formulations, and the clinical data agreed with the simulations. For the Gamma Index of measurement against calculation, its distribution has an increased mean and an increased standard deviation as the noise increases. On the contrary, for the Gamma index of calculation against measurement, its distribution has a decreased mean and stabilized standard deviation as the noise increases. White noise has greater impact on the Gamma Index than spatially correlated noise. Conclusions: The noise has significant impact on the Gamma Index calculation and the impact is asymmetric. The Gamma Index should be reported along with the noise levels in both reference and evaluated images. Reporting of the Gamma Index with switched roles of the images as reference and evaluated images or some composite metrics would be a good practice.

SU-E-T-08: A Convolution Model for Head Scatter Fluence in the Intensity Modulated Field

PURPOSEnTo efficiently calculate the head scatter fluence for an arbitrary intensity-modulated field with any source distribution using the source occlusion model.nnnMETHODnThe source occlusion model with focal and extra focal radiation (Jaffray et al, 1993) can be used to account for LINAC head scatter. In the model, the fluence map of any field shape at any point can be calculated via integration of the source distribution within the visible range, as confined by each segment, using the detector eyes view. A 2D integration would be required for each segment and each fluence plane point, which is time-consuming, as an intensity-modulated field contains typically tens to hundreds of segments. In this work, we prove that the superposition of the segmental integrations is equivalent to a simple convolution regardless of what the source distribution is. In fact, for each point, the detector eyes view of the field shape can be represented as a function with the origin defined at the points pinhole reflection through the center of the collimator plane. We were thus able to reduce hundreds of source plane integration to one convolution. We calculated the fluence map for various 3D and IMRT beams and various extra-focal source distributions using both the segmental integration approach and the convolution approach and compared the computation time and fluence map results of both approaches.nnnRESULTSnThe fluence maps calculated using the convolution approach were the same as those calculated using the segmental approach, except for rounding errors (<0.1%). While it took considerably longer time to calculate all segmental integrations, the fluence map calculation using the convolution approach took only ∼1/3 of the time for typical IMRT fields with ∼100 segments.nnnCONCLUSIONSnThe convolution approach for head scatter fluence calculation is fast and accurate and can be used to enhance the online process.

SU‐E‐T‐475: An Accurate Linear Model of Tomotherapy MLC‐Detector System for Patient Specific Delivery QA

PURPOSEnAn accurate leaf fluence model can be used in applications such as patient specific delivery QA and in-vivo dosimetry for TomoTherapy systems. It is known that the total fluence is not a linear combination of individual leaf fluence due to leakage-transmission, tongue-and-groove, and source occlusion effect. Here we propose a method to model the nonlinear effects as linear terms thus making the MLC-detector system a linear system.nnnMETHODSnA leaf pattern basis (LPB) consisting of no-leaf-open, single-leaf-open, double-leaf-open and triple-leaf-open patterns are chosen to represent linear and major nonlinear effects of leaf fluence as a linear system. An arbitrary leaf pattern can be expressed as (or decomposed to) a linear combination of the LPB either pulse by pulse or weighted by dwelling time. The exit detector responses to the LPB are obtained by processing returned detector signals resulting from the predefined leaf patterns for each jaw setting. Through forward transformation, detector signal can be predicted given a delivery plan. An equivalent leaf open time (LOT) sinogram containing output variation information can also be inversely calculated from the measured detector signals. Twelve patient plans were delivered in air. The equivalent LOT sinograms were compared with their planned sinograms.nnnRESULTSnThe whole calibration process was done in 20 minutes. For two randomly generated leaf patterns, 98.5% of the active channels showed differences within 0.5% of the local maximum between the predicted and measured signals. Averaged over the twelve plans, 90% of LOT errors were within +/-10 ms. The LOT systematic error increases and shows an oscillating pattern when LOT is shorter than 50 ms.nnnCONCLUSIONnThe LPB method models the MLC-detector response accurately, which improves patient specific delivery QA and in-vivo dosimetry for TomoTherapy systems. It is sensitive enough to detect systematic LOT errors as small as 10 ms.

MO‐D‐108‐04: Validation of a Simple Portal Dose Calculation Model for Plan QA and In‐Vivo Dosimetry

PURPOSEnTo validate a simple portal dose calculator for plan QA and in-vivo dosimetry.nnnMETHODSnWe model portal dose as a function of the fluence map, patient attenuation, patient scatter and portal response. Fluence maps are reconstructed using control-point sequence in RTPlan. Patient attenuation is calculated via ray-tracing through the patient CT. The effect of patient scatter and portal response is modeled by convolution, where the convolution kernel is derived from the commissioning measurements of different beam energies, different field sizes, different phantom thickness, and different source to image distances (SIDs). For various IMRT/3D plan, phantom and patient geometry, both in-air and in-transit portals were calculated. The calculations were compared with portal measurements. The Gamma Index of measurements against predicted portals with various dose difference (DD) criteria (1%, 2%, 3%, 4%, 5%, etc) and distance to agreement (DTA) criteria (1 mm, 2 mm, 3 mm, 4 mm, 5 mm, etc) were calculated. The Gamma pass rates of various DD and DTA criteria were evaluated and formed a Gamma table.nnnRESULTSnFor various IMRT beams, the head, body and lung phantoms, the in-air and in-transit portal calculations matched well with portal measurements. The Gamma pass rates for in-air portal are above 97% for 2 mm, 2% criteria and above 99% for 3 mm, 3% criteria. The Gamma pass rates for in-transit portal were above 90% for 2 mm, 2% criteria and above 95% for 3 mm, 3% criteria.nnnCONCLUSIONnThe simple portal dose calculation model is validated via phantom measurements. The model could be used in clinic for in-air and intransit portal prediction.

SU‐E‐T‐109: Calypso RF Interference On Portal Images and a Physical Filter Solution

PURPOSEnPortal measurement is becoming an important tool for in vivo dosimetric verification, and Calypso provides real-time tracking capability; however, when both work simultaneously, large interference arises for portal measurement. The purpose of this study is to investigate the interference of Calypso on portal measurement and mitigation of the interference by applying aluminum shielding over portal panels.nnnMETHODSnFor the same IMRT field and phantom setup, we acquired portal measurements at every 15 degree gantry angle, without and with Calypso, and for those measurements with Calypso, we also acquired portal measurements without and with aluminum shielding over the portal panels. The aluminum shielding consists of a layer of aluminum foil of 0.1 mm thickness covering the portal panel. The measurements without Calypso and without aluminum shielding were regarded as the reference images. All other measurements were regarded as the test images. We measured the deviation of the test images from the reference images by the amplitude difference and using the Gamma Index (3%, 3 mm).nnnRESULTSnWith Calypso interference and without aluminum shielding, the signals are larger than the reference, and in some unfavorable gantry angles, the signals can be as much as 15% larger. With aluminum shielding, the interference was much reduced to ∼3% for those unfavorable angles, and the Gamma passing rate achieves 95% for most of the angles.nnnCONCLUSIONnThe Calypso interference on portal measurements is gantry angle dependent due to panel orientation and proximity with respect to the Calypso transducer. Shielding on the portal panel can largely reduce electronic interference, and it is anticipated that with an improved complete shielding over the entire portal panel, the interference could be further reduced.

MO‐G‐213AB‐01: Quantification of TomoTherapy MVCT Dose

Purpose: To quantify MVCT dose of TomoTherapy for three jaw settings, J4, J1, and J0.1, corresponding to beam widths of 7 mm, 4 mm, and 3 mm, respectively, at the isocenter plane, and three imaging modes, fine, normal, and coarse, corresponding to a couch speed of 4, 8, and 12 mm/rotation, respectively. Material and Methods: An MVCT dose engine was commissioned specifically for the MVCT beams, including updates to the fluence attenuation table (FAT), energy deposition kernel, cone profiles, and penumbrae. MVCT dose calculation was then applied on real and synthesized images of cylindrical water phantoms of diameters ranging from 5 cm to 40 cm, and the results were compared with film measurement. Result: For the J1 jaw and coarse imaging mode, the maximum difference between calculation and measurement was about 6% of the center dose. Calculation on synthesized phantoms showed that the center dose decreased almost linearly as the phantom diameter increased, and that the fine mode received twice the dose of the normal mode and three times that of the coarse mode. The maximal dose due to the helical ripple ranged from 100%∼200% of the center dose, with increasing ratios for larger phantoms (due to the larger radius), smaller jaws, and faster couch speed (the latter two yielding a higher helical pitch). For all jaw settings and couch speeds, the mean dose and average surface dose vary from 95%–115% of the center dose with increasing ratios for larger phantoms. Conclusion: An MVCT dose calculator was set up with validation through film measurement and subsequently used to calculate TomoTherapy MVCT dose for various phantom sizes under various imaging parameters. The results can serve as a reference for TomoTherapy MVCT dose.