Paul Klahr

Displacement-based binning of time-dependent computed tomography image data sets

Respiration can cause tumors in the thorax or abdomen to move by as much as 3 cm; this movement can adversely affect the planning and delivery of radiation treatment. Several techniques have been used to compensate for respiratory motion, but all have shortcomings. Manufacturers of computed tomography (CT) equipment have recently used a technique developed for cardiac CT imaging to track respiratory-induced anatomical motion and to sort images according to the phase of the respiratory cycle they represent. Here we propose a method of generating CT images that accounts for respiratory-induced anatomical motion on the basis of displacement, i.e., displacement-binned CT image sets. This technique has shown great promise, however, it is not fully supported by currently used CT image reconstruction software. As an interim solution, we have developed a method for extracting displacement-binned CT image data sets from data sets assembled on the basis of a prospectively determined breathing phase acquired on a multislice helical CT scanner. First, the projection data set acquired from the CT scanner was binned at small phase intervals before reconstruction. The manufacturers software then generated image sets identified as belonging to particular phases of the respiratory cycle. All images were then individually correlated to the displacement of an external fiducial marker. Next, CT image data sets were resorted on the basis of the displacement and assigned an appropriate phase. Finally, displacement-binned image data sets were transferred to a treatment-planning system for analysis. Although the technique is currently limited by the phase intervals allowed by the CT software, some improvement in image reconstruction was seen, indicating that this technique is useful at least as an interim measure.

Gated CT imaging using a free-breathing respiration signal from flow-volume spirometry.

Respiration-induced tumor motion is known to cause artifacts on free-breathing spiral CT images used in treatment planning. This leads to inaccurate delineation of target volumes on planning CT images. Flow-volume spirometry has been used previously for breath-holds during CT scans and radiation treatments using the active breathing control (ABC) system. We have developed a prototype by extending the flow-volume spirometer device to obtain gated CT scans using a PQ 5000 single-slice CT scanner. To test our prototype, we designed motion phantoms to compare image quality obtained with and without gated CT scan acquisition. Spiral and axial (nongated and gated) CT scans were obtained of phantoms with motion periods of 3-5 s and amplitudes of 0.5-2 cm. Errors observed in the volume estimate of these structures were as much as 30% with moving phantoms during CT simulation. Application of motion-gated CT with active breathing control reduced these errors to within 5%. Motion-gated CT was then implemented in patients and the results are presented for two clinical cases: lung and abdomen. In each case, gated scans were acquired at end-inhalation, end-exhalation in addition to a conventional free-breathing (nongated) scan. The gated CT scans revealed reduced artifacts compared with the conventional free-breathing scan. Differences of up to 20% in the volume of the structures were observed between gated and free-breathing scans. A comparison of the overlap of structures between the gated and free-breathing scans revealed misalignment of the structures. These results demonstrate the ability of flow-volume spirometry to reduce errors in target volumes via gating during CT imaging.

Prospects for in vivo estimation of photon linear attenuation coefficients using postprocessing dual-energy CT imaging on a commercial scanner: Comparison of analytic and polyenergetic statistical reconstruction algorithms

PURPOSE Accurate patient-specific photon cross-section information is needed to support more accurate model-based dose calculation for low energy photon-emitting modalities in medicine such as brachytherapy and kilovoltage x-ray imaging procedures. A postprocessing dual-energy CT (pDECT) technique for noninvasive in vivo estimation of photon linear attenuation coefficients has been experimentally implemented on a commercial CT scanner and its accuracy assessed in idealized phantom geometries. METHODS Eight test materials of known composition and density were used to compare pDECT-estimated linear attenuation coefficients to NIST reference values over an energy range from 10 keV to 1 MeV. As statistical image reconstruction (SIR) has been shown to reconstruct images with less random and systematic error than conventional filtered backprojection (FBP), the pDECT technique was implemented with both an in-house polyenergetic SIR algorithm, alternating minimization (AM), as well as a conventional FBP reconstruction algorithm. Improvement from increased spectral separation was also investigated by filtering the high-energy beam with an additional 0.5 mm of tin. The law of propagated uncertainty was employed to assess the sensitivity of the pDECT process to errors in reconstructed images. RESULTS Mean pDECT-estimated linear attenuation coefficients for the eight test materials agreed within 1% of NIST reference values for energies from 1 MeV down to 30 keV, with mean errors rising to between 3% and 6% at 10 keV, indicating that the method is unbiased when measurement and calibration phantom geometries are matched. Reconstruction with FBP and AM algorithms conferred similar mean pDECT accuracy. However, single-voxel pDECT estimates reconstructed on a 1 × 1 × 3 mm(3) grid are shown to be highly sensitive to reconstructed image uncertainty; in some cases pDECT attenuation coefficient estimates exhibited standard deviations on the order of 20% around the mean. Reconstruction with the statistical AM algorithm led to standard deviations roughly 40% to 60% less than FBP reconstruction. Additional tin filtration of the high energy beam exhibits similar pDECT estimation accuracy as the unfiltered beam, even when scanning with only 25% of the dose. Using the law of propagated uncertainty, low Z materials are found to be more sensitive to image reconstruction errors than high Z materials. Furthermore, it is estimated that reconstructed CT image uncertainty must be limited to less than 0.25% to achieve a target linear-attenuation coefficient estimation uncertainty of 3% at 28 keV. CONCLUSIONS That pDECT supports mean linear attenuation coefficient measurement accuracies of 1% of reference values for energies greater than 30 keV is encouraging. However, the sensitivity of the pDECT measurements to noise and systematic errors in reconstructed CT images warrants further investigation in more complex phantom geometries. The investigated statistical reconstruction algorithm, AM, reduced random measurement uncertainty relative to FBP owing to improved noise performance. These early results also support efforts to increase DE spectral separation, which can further reduce the pDECT sensitivity to measurement uncertainty.

Effect of Tube Voltage on CT Noise Levels in Different Phantom Sizes

OBJECTIVE The purpose of this study was to determine the effect of lowering tube voltage on dose and noise in cylindric water phantoms to optimize quality and decrease the radiation dose for body CT. MATERIALS AND METHODS We performed CT on cylindric water phantoms with diameters of 10, 20, 25, and 30 cm, simulating the abdomen of an infant, child, adolescent, and adult. We used tube voltages of 120, 100, and 80 kVp. The CT dose index (32-cm reference) ranged from 1 to 10 mGy in 10- and 20-cm phantoms and from 2 to 20 mGy in the 25- and 30-cm phantoms. The noise was measured at the center and periphery of the scans. Central and peripheral doses were measured in 16- and 32-cm CT dose index phantoms, and the ratio of central to peripheral doses was calculated. RESULTS At the same noise levels, there was no significant increase in dose in 10-cm cylindric water phantoms when tube voltage was decreased to either 80 or 100 kVp. In 20-, 25-, and 30-cm phantoms, there was a 1-6% increase in dose when tube voltage was decreased to 100 kVp. Central-to-peripheral noise ratios increased 7-37% with increased phantom size. The measured peripheral dose increased as much as 5%. CONCLUSION Our findings support the practice of lowering tube voltage to 80 kVp for imaging of infants and to 100 kVp for imaging of older children. The increase in peripheral dose with decreased tube voltage is minimal and is unlikely to cause substantial change in the effective dose.

Individually optimized contrast-enhanced 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinoma.

PURPOSE To develop an individually optimized contrast-enhanced (CE) 4D-computed tomography (CT) for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA). METHODS Ten PDA patients were enrolled. Each underwent three CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three physicians contoured the tumor and pancreatic tissues. Image quality scores, tumor volume, motion, tumor-to-pancreas contrast, and contrast-to-noise ratio (CNR) were compared in the three CTs. Interobserver variations were also evaluated in contouring the tumor using simultaneous truth and performance level estimation. RESULTS Average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, respectively; P = 0.082), and both were significantly better than that for 4D-CT (2.6, P < 0.001). Tumor-to-pancreas contrast results were comparable in CE 3D-CT and CE 4D-CT (15.5 and 16.7 Hounsfield units (HU), respectively; P = 0.21), and the latter was significantly higher than in 4D-CT (9.2 HU, P = 0.001). Image noise in CE 3D-CT (12.5 HU) was significantly lower than in CE 4D-CT (22.1 HU, P = 0.013) and 4D-CT (19.4 HU, P = 0.009). CNRs were comparable in CE 3D-CT and CE 4D-CT (1.4 and 0.8, respectively; P = 0.42), and both were significantly better in 4D-CT (0.6, P = 0.008 and 0.014). Mean tumor volumes were significantly smaller in CE 3D-CT (29.8 cm3, P = 0.03) and CE 4D-CT (22.8 cm3, P = 0.01) than in 4D-CT (42.0 cm3). Mean tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, P = 0.17). Interobserver variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%, respectively) and were worse for 4D-CT (55.6%) than CE 3D-CT. CONCLUSIONS CE 4D-CT demonstrated characteristics comparable to CE 3D-CT, with high potential for simultaneously delineating the tumor and quantifying tumor motion with a single scan.

Individually optimized uniform contrast enhancement in CT angiography for the diagnosis of pulmonary thromboembolic disease—A simulation study

PURPOSE To improve the diagnostic quality of CT pulmonary angiography (CTPA) by individually optimizing a biphasic contrast injection function to achieve targeted uniform contrast enhancement. To compare the results against a previously reported discrete Fourier transform (DFT) approach. METHODS This simulation study used the CTPA datasets of 27 consecutive patients with pulmonary thromboembolic disease (PE). An optimization approach was developed consisting of (1) computation of the impulse enhancement function (IEF) based on a test bolus scan, and (2) optimization of a biphasic contrast injection function using the IEF in order to achieve targeted uniform enhancement. The injection rates and durations of a biphasic contrast injection function are optimized by minimizing the difference between the resulting contrast enhancement curve and the targeted uniform enhancement curve, while conforming to the clinical constraints of injection rate and total contrast volume. The total contrast volume was limited first to the clinical standard of 65 ml, and then to the same amount used in the DFT approach for comparison. The optimization approach and the DFT approach were compared in terms of the root mean square error (RMSE) and total contrast volume used. RESULTS When the total contrast volume was limited to 65 ml, the optimization approach produced significantly better contrast enhancement (closer to the targeted uniform contrast enhancement) than the DFT approach (RMSE 17 HU vs 56 HU, p < 0.00001). On average, the optimization approach used 63 ml contrast, while the DFT approach used 50 ml with four patients exceeding 65 ml. When equivalent total contrast volume was used for individual patient, the optimization approach still generated significantly better contrast enhancement (RMSE 44 HU vs 56 HU, p < 0.01). Constraints for the injection function could be easily accommodated into the optimization process when searching for the optimal biphasic injection function. CONCLUSIONS The optimization approach generated individually optimized biphasic injection functions yielding significantly better contrast enhancement compared to the DFT approach. This new approach has the potential to improve the diagnostic quality of CTPA for PE.

Dosimetric impact of orthopedic metal artifact reduction (O-MAR) on Spine SBRT patients

The dosimetric impact of orthopedic metal artifact reduction (O-MAR) on spine SBRT patients has not been comprehensively studied, particularly with spinal prostheses in high-dose gradient regions. Using both phantom and patient datasets, we investigated dosimetric effects of O-MAR in combination of various metal locations and dose calculation algorithms. A physical phantom, with and without a titanium insert, was scanned. A clinical patient plan was applied to the artifact-free reference, non-O-MAR, and O-MAR phantom images with the titanium located either inside or outside of the tumor. Subsequently, five clinical patient plans were calculated with pencil beam and Monte Carlo (iPlan) on non-O-MAR and O-MAR patient images using an extended CT-density table. The dose differences for phantom plans and patient plans were analyzed using dose distributions, dose-volume histograms (DVHs), gamma index, and selected dosimetric endpoints. From both phantom plans and patient plans, O-MAR did not affect dose distributions and DVHs while minimizing metal artifacts. Among patient plans, we found that, when the same dose calculation method was used, the difference in the dosimetric endpoints between non-O-MAR and O-MAR datasets were small. In conclusion, for spine SBRT patients with spinal prostheses, O-MAR image reconstruction does not affect dose calculation accuracy while minimizing metal artifacts. Therefore, O-MAR images can be safely used for clinical spine SBRT treatment planning. PACS numbers: 87.53.Bn, 87.55.K-, 87.57.Q-, 87.57.cp.The dosimetric impact of orthopedic metal artifact reduction (O‐MAR) on spine SBRT patients has not been comprehensively studied, particularly with spinal prostheses in high‐dose gradient regions. Using both phantom and patient datasets, we investigated dosimetric effects of O‐MAR in combination of various metal locations and dose calculation algorithms. A physical phantom, with and without a titanium insert, was scanned. A clinical patient plan was applied to the artifact‐free reference, non‐O‐MAR, and O‐MAR phantom images with the titanium located either inside or outside of the tumor. Subsequently, five clinical patient plans were calculated with pencil beam and Monte Carlo (iPlan) on non‐O‐MAR and O‐MAR patient images using an extended CT‐density table. The dose differences for phantom plans and patient plans were analyzed using dose distributions, dose‐volume histograms (DVHs), gamma index, and selected dosimetric endpoints. From both phantom plans and patient plans, O‐MAR did not affect dose distributions and DVHs while minimizing metal artifacts. Among patient plans, we found that, when the same dose calculation method was used, the difference in the dosimetric endpoints between non‐O‐MAR and O‐MAR datasets were small. In conclusion, for spine SBRT patients with spinal prostheses, O‐MAR image reconstruction does not affect dose calculation accuracy while minimizing metal artifacts. Therefore, O‐MAR images can be safely used for clinical spine SBRT treatment planning. PACS numbers: 87.53.Bn, 87.55.K‐, 87.57.Q‐, 87.57.cp

Effect of body size on dose reduction with longitudinal tube current modulation in pediatric patients

OBJECTIVE The purpose of the study was to evaluate whether dose reduction by tube current modulation in pediatric abdominal CT depends on patient body size. MATERIALS AND METHODS A 12-month (February 2012 through January 2013) retrospective evaluation of consecutive abdominal 128-MDCT examinations was performed. All studies were performed with longitudinal (z-axis) tube current modulation. Dose reduction from tube current modulation in every CT acquisition was recorded and compared with body weight. In addition, 100 randomized CT abdominal scans were evaluated for average and SD of the water-equivalent diameter along the z-axis. RESULTS The results include 466 abdominal CT scans of 369 children (172 girls, 197 boys; age range, 3 weeks-18 years; average, 9.2 years; body weight range, 3.5-130 kg; average, 31 kg). The average tube current-time reduction was 19%. Dose reduction was least effective (p<0.05; average, 11%) for body weight less than 20 kg. The least variability (SD/average) of water-equivalent diameter along the z-axis was found for body weights greater than 20 kg (5.0%) and 20-40 kg (5.9%) (p<0.05). Dose reduction was most effective (p<0.05; average, 30%) at the body weight range of 60-100 kg. CONCLUSION Dose reduction with automated tube modulation depends on body weight and is less effective in children with a small body size. One of the reasons for this phenomenon could be a closer to uniform water-equivalent diameter along the z-axis in children with a small body size.

SU-E-I-75: Evaluation of An Orthopedic Metal Artifact Reduction (O-MAR) Algorithm On Patients with Spinal Prostheses Near Spinal Tumors

PURPOSE To evaluate the impact of a commercial orthopedic metal artifact reduction (O-MAR) algorithm on CT image quality and dose calculation for patients with spinal prostheses near spinal tumors. METHODS A CT electron density phantom was scanned twice: with tissue-simulating inserts only, and with a titanium insert replacing solid water. A patient plan was mapped to the phantom images in two ways: with the titanium inside or outside of the spinal tumor. Pinnacle and Eclipse were used to evaluate the dosimetric effects of O-MAR on 12-bit and 16-bit CT data, respectively. CT images from five patients with spinal prostheses were reconstructed with and without O-MAR. Two observers assessed the image quality improvement from O-MAR. Both pencil beam and Monte Carlo dose calculation in iPlan were used for the patient study. The percentage differences between non-OMAR and O-MAR datasets were calculated for PTV_min, PTV_max, PTV_mean, PTV_V100, PTV_D90, OAR_V10Gy, OAR_max, and OAR_D0.1cc. RESULTS O-MAR improved image quality but did not significantly affect the dose distributions and DVHs for both 12-bit and 16- bit CT phantom data. All five patient cases demonstrated some degree of image quality improvement from O-MAR, ranging from small to large metal artifact reduction. For pencil beam, the largest discrepancy was observed for OARV_10Gy at 5.4%, while the other seven parameters were ≤0.6%. For Monte Carlo, the differences between non-O-MAR and O-MAR datasets were ≤3.0%. CONCLUSION Both phantom and patient studies indicated that O-MAR can substantially reduce metal artifacts on CT images, allowing better visualization of the anatomical structures and metal objects. The dosimetric impact of O-MAR was insignificant regardless of the metal location, image bit-depth, and dose calculation algorithm. O-MAR corrected images are recommended for radiation treatment planning on patients with spinal prostheses because of the improved image quality and no need to modify current dose constraints. This work was supported by a research grant from Philips Healthcare. Paul Klahr is an employee of Philips Healthcare.

Machine learning-based dual-energy CT parametric mapping

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.