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Dose calculation with respiration-averaged CT processed from cine CT without a respiratory surrogate.

ABSTRACT: Dose calculation for thoracic radiotherapy is commonly performed on a free-breathing helical CT despite artifacts caused by respiratory motion. Four-dimensional computed tomography (4D-CT) is one method to incorporate motion information into the treatment planning process. Some centers now use the respiration-averaged CT (RACT), the pixel-by-pixel average of the ten phases of 4D-CT, for dose calculation. This method, while sparing the tedious task of 4D dose calculation, still requires 4D-CT technology. The authors have recently developed a means to reconstruct RACT directly from unsorted cine CT data from which 4D-CT is formed, bypassing the need for a respiratory surrogate. Using RACT from cine CT for dose calculation may be a means to incorporate motion information into dose calculation without performing 4D-CT. The purpose of this study was to determine if RACT from cine CT can be substituted for RACT from 4D-CT for the purposes of dose calculation, and if increasing the cine duration can decrease differences between the dose distributions. Cine CT data and corresponding 4D-CT simulations for 23 patients with at least two breathing cycles per cine duration were retrieved. RACT was generated four ways: First from ten phases of 4D-CT, second, from 1 breathing cycle of images, third, from 1.5 breathing cycles of images, and fourth, from 2 breathing cycles of images. The clinical treatment plan was transferred to each RACT and dose was recalculated. Dose planes were exported at orthogonal planes through the isocenter (coronal, sagittal, and transverse orientations). The resulting dose distributions were compared using the gamma index within the planning target volume (PTV). Failure criteria were set to 2%/1 mm. A follow-up study with 50 additional lung cancer patients was performed to increase sample size. The same dose recalculation and analysis was performed. In the primary patient group, 22 of 23 patients had 100% of points within the PTV pass y criteria. The average maximum and mean y indices were very low (well below 1), indicating good agreement between dose distributions. Increasing the cine duration generally increased the dose agreement. In the follow-up study, 49 of 50 patients had 100% of points within the PTV pass the y criteria. The average maximum and mean y indices were again well below 1, indicating good agreement. Dose calculation on RACT from cine CT is negligibly different from dose calculation on RACT from 4D-CT. Differences can be decreased further by increasing the cine duration of the cine CT scan.

SUBMITTER: Riegel AC

PROVIDER: S-EPMC3910265 | biostudies-other | 2008 Dec

REPOSITORIES: biostudies-other

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Project description:The Leksell GammaPlan software version 10 introduces a CT image-based segmentation tool for automatic skull definition and a convolution dose calculation algorithm for tissue inhomogeneity correction. The purpose of this work was to evaluate the impact of these new approaches on routine clinical Gamma Knife treatment planning. Sixty-five patients who underwent CT image-guided Gamma Knife radiosurgeries at the University of Pittsburgh Medical Center in recent years were retrospectively investigated. The diagnoses for these cases include trigeminal neuralgia, meningioma, acoustic neuroma, AVM, glioma, and benign and metastatic brain tumors. Dose calculations were performed for each patient with the same dose prescriptions and the same shot arrangements using three different approaches: 1) TMR 10 dose calculation with imaging skull definition; 2) convolution dose calculation with imaging skull definition; 3) TMR 10 dose calculation with conventional measurement-based skull definition. For each treatment matrix, the total treatment time, the target coverage index, the selectivity index, the gradient index, and a set of dose statistics parameters were compared between the three calculations. The dose statistics parameters investigated include the prescription isodose volume, the 12 Gy isodose volume, the minimum, maximum and mean doses on the treatment targets, and the critical structures under consideration. The difference between the convolution and the TMR 10 dose calculations for the 104 treatment matrices were found to vary with the patient anatomy, location of the treatment shots, and the tissue inhomogeneities around the treatment target. An average difference of 8.4% was observed for the total treatment times between the convolution and the TMR algorithms. The maximum differences in the treatment times, the prescription isodose volumes, the 12 Gy isodose volumes, the target coverage indices, the selectivity indices, and the gradient indices from the convolution and the TMR 10 calculations are 14.9%, 16.4%, 11.1%, 16.8, 6.9%, and 11.4%, respectively. The maximum differences in the minimum and the mean target doses between the two calculation algorithms are 8.1% and 4.2% of the corresponding prescription doses. The maximum differences in the maximum and the mean doses for the critical structures between the two calculation algorithms are 1.3 Gy and 0.7 Gy. The results from the two skull definition methods with the TMR 10 algorithm agree either within ± 2.5% or 0.3 Gy for the dose values, except for a 4.9% difference in the treatment times for a lower cerebellar lesion. The imaging skull definition method does not affect Gamma Knife dose calculation considerably when compared to the conventional measurement-based skull definition method, except in some extreme cases. Large differences were observed between the TMR 10 and the convolution calculation method for the same dose prescription and the same shot arrangements, indicating that the implementation of the convolution algorithm in routine clinical use might be desirable for optimal dose calculation results.

Project description:The maturation of photon-counting detector (PCD) technology promises to enhance routine CT imaging applications with high-fidelity spectral information. In this paper, we demonstrate the power of this synergy and our complementary reconstruction techniques, performing 4D, cardiac PCD-CT data acquisition and reconstruction in a mouse model of atherosclerosis, including calcified plaque. Specifically, in vivo cardiac micro-CT scans were performed in four ApoE knockout mice, following their development of calcified plaques. The scans were performed with a prototype PCD (DECTRIS, Ltd.) with 4 energy thresholds. Projections were sampled every 10 ms with a 10 ms exposure, allowing the reconstruction of 10 cardiac phases at each of 4 energies (40 total 3D volumes per mouse scan). Reconstruction was performed iteratively using the split Bregman method with constraints on spectral rank and spatio-temporal gradient sparsity. The reconstructed images represent the first in vivo, 4D PCD-CT data in a mouse model of atherosclerosis. Robust regularization during iterative reconstruction yields high-fidelity results: an 8-fold reduction in noise standard deviation for the highest energy threshold (relative to unregularized algebraic reconstruction), while absolute spectral bias measurements remain below 13 Hounsfield units across all energy thresholds and scans. Qualitatively, image domain material decomposition results show clear separation of iodinated contrast and soft tissue from calcified plaque in the in vivo data. Quantitatively, spatial, spectral, and temporal fidelity are verified through a water phantom scan and a realistic MOBY phantom simulation experiment: spatial resolution is robustly preserved by iterative reconstruction (10% MTF: 2.8-3.0 lp/mm), left-ventricle, cardiac functional metrics can be measured from iodine map segmentations with ~1% error, and small calcifications (615 μm) can be detected during slow moving phases of the cardiac cycle. Given these preliminary results, we believe that PCD technology will enhance dynamic CT imaging applications with high-fidelity spectral and material information.

Project description:PurposeIn intra-arterial hepatic radioembolization using Holmium-166 (166 Ho) microspheres, a predicted lung-absorbed dose of more than 30 Gy is a contraindication for therapy. Therefore, scout imaging by means of quantitative SPECT of the lungs after a low-dose pretreatment session is essential. Earlier we showed the superiority of Monte Carlo-based iterative SPECT reconstructions over conventional reconstructions due to its quantitative nature, required for dosimetry, at the cost of substantial computation times. In clinical routine, however, the limited available time between scout imaging and therapy constrains its application. To reduce computation times, we investigated the minimum number of iterations required to guarantee a clinical acceptable accuracy in lung dose estimation using patient and phantom data.Methods166 Ho scout SPECT data (range: 222-283 MBq) were used from 10 patients. SPECT images were Monte Carlo-based OSEM reconstructed (effective iterations: 240). Additionally, the 4D XCAT anthropomorphic phantom was used to mimic studies with an injected scout activity of 250 MBq and with varying lung-absorbed doses ranging from 0.9 to 225 Gy for a therapeutic dosage of 15 GBq. These studies were reconstructed in the same way as the patient data, and were also reconstructed using a clinically available, standard OSEM algorithm for comparison. Lung-absorbed dose was determined using VOI analysis as a function of iterations.ResultsThe estimated lung-absorbed dose in nine patients ranged upon MC-based OSEM convergence from 0 to 0.26 Gy for a therapeutic dosage. One patient had an estimated lung absorbed-dose for a therapeutic dosage of 20.3 Gy upon MC-based OSEM convergence, or 18.4 Gy after 40 iterations (-9%). The phantom data showed that the lung-absorbed dose upon OSEM convergence was underestimated by 15% as compared to the actual simulated lung dose, and the dose after 40 iterations was underestimated by 9% as compared to the dose upon convergence. Both underestimations were irrespective of the magnitude of the lung-absorbed dose (0.9 to 225 Gy) and thus can be easily corrected for. The quantitative accuracy of the MC-based OSEM reconstructions (40 iterations, before convergence) outperformed the clinical OSEM reconstruction while estimating the lung dose.ConclusionsThe number of effective iterations necessary for quantitative estimation of the lung dose using MC-based OSEM can be reduced from 240 to 40. The resulting sixfold reduction in calculation time enables processing of the scout images before therapy administration.

Project description:IntroductionThe clinical use of serial quantitative computed tomography (CT) to characterize lung disease and guide the optimization of mechanical ventilation in patients with acute respiratory distress syndrome (ARDS) is limited by the risk of cumulative radiation exposure and by the difficulties and risks related to transferring patients to the CT room. We evaluated the effects of tube current-time product (mAs) variations on quantitative results in healthy lungs and in experimental ARDS in order to support the use of low-dose CT for quantitative analysis.MethodsIn 14 sheep chest CT was performed at baseline and after the induction of ARDS via intravenous oleic acid injection. For each CT session, two consecutive scans were obtained applying two different mAs: 60 mAs was paired with 140, 15 or 7.5 mAs. All other CT parameters were kept unaltered (tube voltage 120 kVp, collimation 32 × 0.5 mm, pitch 0.85, matrix 512 × 512, pixel size 0.625 × 0.625 mm). Quantitative results obtained at different mAs were compared via Bland-Altman analysis.ResultsGood agreement was observed between 60 mAs and 140 mAs and between 60 mAs and 15 mAs (all biases less than 1%). A further reduction of mAs to 7.5 mAs caused an increase in the bias of poorly aerated and nonaerated tissue (-2.9% and 2.4%, respectively) and determined a significant widening of the limits of agreement for the same compartments (-10.5% to 4.8% for poorly aerated tissue and -5.9% to 10.8% for nonaerated tissue). Estimated mean effective dose at 140, 60, 15 and 7.5 mAs corresponded to 17.8, 7.4, 2.0 and 0.9 mSv, respectively. Image noise of scans performed at 140, 60, 15 and 7.5 mAs corresponded to 10, 16, 38 and 74 Hounsfield units, respectively.ConclusionsA reduction of effective dose up to 70% has been achieved with minimal effects on lung quantitative results. Low-dose computed tomography provides accurate quantitative results and could be used to characterize lung compartment distribution and possibly monitor time-course of ARDS with a lower risk of exposure to ionizing radiation. A further radiation dose reduction is associated with lower accuracy in quantitative results.

Project description:The authors describe a novel method of predicting mass density and elemental mass fractions of tissues from dual energy CT (DECT) data for Monte Carlo (MC) based dose planning.The relative electron density ϱ(e) and effective atomic number Z(eff) are calculated for 71 tabulated tissue compositions. For MC simulations, the mass density is derived via one linear fit in the ϱ(e) that covers the entire range of tissue compositions (except lung tissue). Elemental mass fractions are predicted from the ϱ(e) and the Z(eff) in combination. Since particle therapy dose planning and verification is especially sensitive to accurate material assignment, differences to the ground truth are further analyzed for mass density, I-value predictions, and stopping power ratios (SPR) for ions. Dose studies with monoenergetic proton and carbon ions in 12 tissues which showed the largest differences of single energy CT (SECT) to DECT are presented with respect to range uncertainties. The standard approach (SECT) and the new DECT approach are compared to reference Bragg peak positions.Mean deviations to ground truth in mass density predictions could be reduced for soft tissue from (0.5±0.6)% (SECT) to (0.2±0.2)% with the DECT method. Maximum SPR deviations could be reduced significantly for soft tissue from 3.1% (SECT) to 0.7% (DECT) and for bone tissue from 0.8% to 0.1%. Mean I-value deviations could be reduced for soft tissue from (1.1±1.4%, SECT) to (0.4±0.3%) with the presented method. Predictions of elemental composition were improved for every element. Mean and maximum deviations from ground truth of all elemental mass fractions could be reduced by at least a half with DECT compared to SECT (except soft tissue hydrogen and nitrogen where the reduction was slightly smaller). The carbon and oxygen mass fraction predictions profit especially from the DECT information. Dose studies showed that most of the 12 selected tissues would profit significantly (up to 2.2%) from DECT material decomposition with no noise present. The ϱ(e) associated with an absolute noise of ±0.01 and Z(eff) associated with an absolute noise of ±0.2 resulted in ±10% standard variation in the carbon and oxygen mass fraction prediction.Accurate stopping power prediction is mainly determined by the correct mass density prediction. Theoretical improvements in range predictions with DECT data in the order of 0.1%-2.1% were observed. Further work is needed to quantify the potential improvements from DECT compared to SECT in measured image data associated with artifacts and noise.

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Dose calculation with respiration-averaged CT processed from cine CT without a respiratory surrogate.

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