G Lasio

Statistical reconstruction for x-ray computed tomography using energy-integrating detectors.

Statistical image reconstruction (SR) algorithms have the potential to significantly reduce x-ray CT image artefacts because they use a more accurate model than conventional filtered backprojection and can incorporate effects such as noise, incomplete data and nonlinear detector response. Most SR algorithms assume that the CT detectors are photon-counting devices and generate Poisson-distributed signals. However, actual CT detectors integrate energy from the x-ray beam and exhibit compound Poisson-distributed signal statistics. This study presents the first assessment of the impact on image quality of the resultant mismatch between the detector and signal statistics models assumed by the sinogram data model and the reconstruction algorithm. A 2D CT projection simulator was created to generate synthetic polyenergetic transmission data assuming (i) photon-counting with simple Poisson-distributed signals and (ii) energy-weighted detection with compound Poisson-distributed signals. An alternating minimization (AM) algorithm was used to reconstruct images from the data models (i) and (ii) for a typical abdominal scan protocol with incident particle fluence levels ranging from 10(5) to 1.6 x 10(6) photons/detector. The images reconstructed from data models (i) and (ii) were compared by visual inspection and image-quality figures of merit. The reconstructed image quality degraded significantly when the means were mismatched from the assumed model. However, if the signal means are appropriately modified, images from data models (i) and (ii) did not differ significantly even when SNR is very low. While data-mean mismatches characteristic of the difference between particle-fluence and energy-fluence transmission can cause significant streaking and cupping artefacts, the mismatch between the actual and assumed CT detector signal statistics did not significantly degrade image quality once systematic data means mismatches were corrected.

The delayed pulmonary syndrome following acute high-dose irradiation: a rhesus macaque model.

AbstractSeveral radiation dose- and time-dependent tissue sequelae develop following acute high-dose radiation exposure. One of the recognized delayed effects of such exposures is lung injury, characterized by respiratory failure as a result of pneumonitis that may subsequently develop into lung fibrosis. Since this pulmonary subsyndrome may be associated with high morbidity and mortality, comprehensive treatment following high-dose irradiation will ideally include treatments that mitigate both the acute hematologic and gastrointestinal subsyndromes as well as the delayed pulmonary syndrome. Currently, there are no drugs approved by the Food and Drug Administration to counteract the effects of acute radiation exposure. Moreover, there are no relevant large animal models of radiation-induced lung injury that permit efficacy testing of new generation medical countermeasures in combination with medical management protocols under the FDA animal rule criteria. Herein is described a nonhuman primate model of delayed lung injury resulting from whole thorax lung irradiation. Rhesus macaques were exposed to 6 MV photon radiation over a dose range of 9.0–12.0 Gy and medical management administered according to a standardized treatment protocol. The primary endpoint was all-cause mortality at 180 d. A comparative multiparameter analysis is provided, focusing on the lethal dose response relationship characterized by a lethal dose50/180 of 10.27 Gy [9.88, 10.66] and slope of 1.112 probits per linear dose. Latency, incidence, and severity of lung injury were evaluated through clinical and radiographic parameters including respiratory rate, saturation of peripheral oxygen, corticosteroid requirements, and serial computed tomography. Gross anatomical and histological analyses were performed to assess radiation-induced injury. The model defines the dose response relationship and time course of the delayed pulmonary sequelae and consequent morbidity and mortality. Therefore, it may provide an effective platform for the efficacy testing of candidate medical countermeasures against the delayed pulmonary syndrome.

The MCART radiation physics core: the quest for radiation dosimetry standardization.

AbstractDose-related radiobiological research results can only be compared meaningfully when radiation dosimetry is standardized. To this purpose, the National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Medical Countermeasures Against Radiological Threats (MCART) consortium recently created a Radiation Physics Core (RPC) as an entity to assume responsibility of standardizing radiation dosimetry practices among its member laboratories. The animal research activities in these laboratories use a variety of ionizing photon beams from several irradiators such as 250–320 kVp x-ray generators, 137Cs irradiators, 60Co teletherapy machines, and medical linear accelerators (LINACs). In addition to this variety of sources, these centers use a range of irradiation techniques and make use of different dose calculation schemes to conduct their experiments. An extremely important objective in these research activities is to obtain a Dose Response Relationship (DRR) appropriate to their respective organ-specific models of acute and delayed radiation effects. A clear and unambiguous definition of the DRR is essential for the development of medical countermeasures. It is imperative that these DRRs are transparent between centers. The MCART RPC has initiated the establishment of standard dosimetry practices among member centers and is introducing a Remote Dosimetry Monitoring Service (RDMS) to ascertain ongoing quality assurance. This paper will describe the initial activities of the MCART RPC toward implementing these standardization goals. It is appropriate to report a summary of initial activities with the intent of reporting the full implementation at a later date.

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT

To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n=38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation.

SU-F-BRF-02: Automated Lung Segmentation Method Using Atlas-Based Sparse Shape Composition with a Shape Constrained Deformable Model

PURPOSE To develop an automated lung segmentation method, which combines the atlas-based sparse shape composition with a shape constrained deformable model in thoracic CT for patients with compromised lung volumes. METHODS Ten thoracic computed tomography scans for patients with large lung tumors were collected and reference lung ROIs in each scan was manually segmented to assess the performance of the method. We propose an automated and robust framework for lung tissue segmentation by using single statistical atlas registration to initialize a robust deformable model in order to perform fine segmentation that includes compromised lung tissue. First, a statistical image atlas with sparse shape composition is constructed and employed to obtain an approximate estimation of lung volume. Next, a robust deformable model with shape prior is initialized from this estimation. Energy terms from ROI edge potential and interior ROI region based potential as well as the initial ROI are combined in this model for accurate and robust segmentation. RESULTS The proposed segmentation method is applied to segment right lung on three CT scans. The quantitative results of our segmentation method achieved mean dice score of (0.92-0.95), mean accuracy of (0.97,0.98), and mean relative error of (0.10,0.16) with 95% CI. The quantitative results of previously published RASM segmentation method achieved mean dice score of (0.74,0.96), mean accuracy of (0.66,0.98), and mean relative error of (0.04, 0.38) with 95% CI. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance compared with a robust active shape model method. CONCLUSION The atlas-based segmentation approach achieved relatively high accuracy with less variance compared to RASM in the sample dataset and the proposed method will be useful in image analysis applications for lung nodule or lung cancer diagnosis and radiotherapy assessment in thoracic computed tomography.

Tomographic image via background subtraction using an x-ray projection image and a priori computed tomography

Kilovoltage x-ray projection images (kV images for brevity) are increasingly available in image guided radiotherapy (IGRT) for patient positioning. These images are two-dimensional (2D) projections of a three-dimensional (3D) object along the x-ray beam direction. Projecting a 3D object onto a plane may lead to ambiguities in the identification of anatomical structures and to poor contrast in kV images. Therefore, the use of kV images in IGRT is mainly limited to bony landmark alignments. This work proposes a novel subtraction technique that isolates a slice of interest (SOI) from a kV image with the assistance of a priori information from a previous CT scan. The method separates structural information within a preselected SOI by suppressing contributions to the unprocessed projection from out-of-SOI-plane structures. Up to a five-fold increase in the contrast-to-noise ratios (CNRs) was observed in selected regions of the isolated SOI, when compared to the original unprocessed kV image. The tomographic image via background subtraction (TIBS) technique aims to provide a quick snapshot of the slice of interest with greatly enhanced image contrast over conventional kV x-ray projections for fast and accurate image guidance of radiation therapy. With further refinements, TIBS could, in principle, provide real-time tumor localization using gantry-mounted x-ray imaging systems without the need for implanted markers.

Evaluation of scatter mitigation strategies for x-ray cone-beam CT: impact of scatter subtraction and anti-scatter grids on contrast-to-noise ratio

The large contribution of scatter to cone-beam computed tomography (CBCT) x-ray projections significantly degrades image quality, both through streaking and cupping artifacts and by loss of low contrast boundary detectability. The goal of this investigation is to compare the efficacy of three widely used scatter mitigation methods: subtractive scatter correction (SSC); anti-scatter grids (ASG); and beam modulating with bowtie filters; for improving signal-to-noise ratio (SNR), contrast, contrast-to-noise ratio (CNR) and cupping artifacts. A simple analytic model was developed to predict scatter-to-primary ratio (SPR) and CNR as a function of cylindrical phantom thickness. In addition, CBCT x-ray projections of a CatPhan QA phantom were measured, using a Varian CBCT imaging system, and computed, using an inhouse Monte Carlo photon-transport code to more realistically evaluate the impact of scatter mitigation techniques. Images formed with uncorrected sinograms acquired without ASGs and bow-tie filter show pronounced cupping artifacts and loss of contrast. Subtraction of measured scatter profiles restores image uniformity and CT number accuracy, but does not improve CNR, since the improvement in contrast almost exactly offset by the increase in relative x-ray noise. ASGs were found to modestly improve CNR (up to 20%, depending ASG primary transmission and selectivity) only in body scans, while they can reduce CNR for head phantoms where SPR is low.

Efficient deformable model with sparse shape composition prior on compromised right lung segmentation in CT

We developed an automated lung segmentation method, which uses deformable model with sparse shape composition prior for patients with compromised lung volumes with severe pathologies in CT. Fifteen thoracic computed tomography scans for patients with lung tumors were collected and reference lung ROIs in each scan was manually segmented to assess the performance of the method. First, sparse shape composition model is constructed using training dataset. Next, the deformable model with SSC prior will be initialized according to the rough segmented right lung ROI. Then, the right lung with compromised lung volumes is segmented using the robust deformable model. Energy terms from ROI edge potential and interior ROI region based potential are combined in this model for accurate and robust segmentation. The quantitative results of our segmentation method achieved mean dice score of (0.86, 0.97) with 95% CI, mean accuracy of (0.93, 0.98) with 95% CI, and mean relative error of (0.07, 0.17) with 95% CI. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance compared with a robust active shape model method (RASM). The proposed method will be useful in radiotherapy assessment in thoracic computed tomography and image analysis applications for lung nodule or lung cancer diagnosis.

SU-E-J-265: Practical Issues and Solutions in Reconstructing and Using 4DCT for Radiotherapy Planning of Lung Cancer

PURPOSE To report practical issues and solutions in reconstructing and using 4DCT to account for respiratory motion in radiotherapy planning. METHODS Quiet breathing 4DCT was used to account for respiratory motion for patients with lung or upper abdomen tumor. A planning CT and a 4DCT were acquired consecutively with a Philips Brilliance CT scanner and Varian RPM System. The projections were reconstructed into 10 phases. In Pinnacle RTP system, we contour a GTV in each phase and unite all 10 GTVs as ITV. The ITV is then mapped to the planning CT. We describe practical issues, their causes, our solutions and reasoning during this process. RESULTS In 6 months, 9 issues were reported for 8 patients with lung cancer. For two patients, part of the GTV (∼50% and 10%) in planning CT fell outside the ITV in 4DCT. There was a 7 mm variation in first patient back position because less restricted immobilization had to be used. The second discrepancy was due to moderate variation in breathing amplitude. We extended the ITV to include the GTV since both variations may likely happen during treatment. A LUL tumor showed no motion due to a 10-s long no-breathing period. An RLL tumor appeared double due to an abnormally deeper breath at the tumor region. We repeated 4DCT reiterating the importance of quiet, regular breathing. One patient breathed too light to generate RPM signal. Two issues (no motion in lung, incomplete images in 90% phase) were due to incorrect tag positions. Two unexplainable errors disappeared when repeating reconstruction. In summary, 5 issues were patient-related and 4 were technique issues. CONCLUSION Improving breathing regularity avoided large artifacts in 4DCT. One needs to closely monitor patient breathing. For uncontrollable variations, larger PTVs are necessary which requires appropriate communication between physics and the treating physician.

SU-E-T-169: Characterization of Pacemaker/ICD Dose in SAVI HDR Brachytherapy

Purpose: It is important to estimate dose to pacemaker (PM)/Implantable Cardioverter Defibrillator (ICD) before undertaking Accelerated Partial Breast Treatment using High Dose Rate (HDR) brachytherapy. Kim et al. have reported HDR PM/ICD dose using a single-source balloon applicator. To the authors knowledge, there have so far not been any published PM/ICD dosimetry literature for the Strut Adjusted Volume Implant (SAVI, Cianna Medical, Aliso Viejo, CA). This study aims to fill this gap by generating a dose look up table (LUT) to predict maximum dose to the PM/ICD in SAVI HDR brachytherapy. Methods: CT scans for 3D dosimetric planning were acquired for four SAVI applicators (6−1-mini, 6−1, 8−1 and 10−1) expanded to their maximum diameter in air. The CT datasets were imported into the Elekta Oncentra TPS for planning and each applicator was digitized in a multiplanar reconstruction window. A dose of 340 cGy was prescribed to the surface of a 1 cm expansion of the SAVI applicator cavity. Cartesian coordinates of the digitized applicator were determined in the treatment leading to the generation of a dose distribution and corresponding distance-dose prediction look up table (LUT) for distances from 2 to 15 cm (6-mini) and 2 to 20 cm (10–1).The deviation between the LUT doses and the dose to the cardiac device in a clinical case was evaluated. Results: Distance-dose look up table were compared to clinical SAVI plan and the discrepancy between the max dose predicted by the LUT and the clinical plan was found to be in the range (−0.44%, 0.74%) of the prescription dose. Conclusion: The distance-dose look up tables for SAVI applicators can be used to estimate the maximum dose to the ICD/PM, with a potential usefulness for quick assessment of dose to the cardiac device prior to applicator placement.