M McNitt‐Gray

Pediatric organ dose measurements in axial and helical multislice CT

An anthropomorphic pediatric phantom (5-yr-old equivalent) was used to determine organ doses at specific surface and internal locations resulting from computed tomography (CT) scans. This phantom contains four different tissue-equivalent materials: Soft tissue, bone, brain, and lung. It was imaged on a 64-channel CT scanner with three head protocols (one contiguous axial scan and two helical scans [pitch = 0.516 and 0.984]) and four chest protocols (one contiguous axial scan and three helical scans [pitch = 0.516, 0.984, and 1.375]). Effective mA s [= (tube current x rotation time)/pitch] was kept nearly constant at 200 effective mA s for head and 290 effective mA s for chest protocols. Dose measurements were acquired using thermoluminescent dosimeter powder in capsules placed at locations internal to the phantom and on the phantom surface. The organs of interest were the brain, both eyes, thyroid, sternum, both breasts, and both lungs. The organ dose measurements from helical scans were lower than for contiguous axial scans by 0% to 25% even after adjusting for equivalent effective mA s. There was no significant difference (p > 0.05) in organ dose values between the 0.516 and 0.984 pitch values for both head and chest scans. The chest organ dose measurements obtained at a pitch of 1.375 were significantly higher than the dose values obtained at the other helical pitches used for chest scans (p < 0.05). This difference was attributed to the automatic selection of the large focal spot due to a higher tube current value. These findings suggest that there may be a previously unsuspected radiation dose benefit associated with the use of helical scan mode during computed tomography scanning.

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features

Purpose: To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. Methods: This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. Results: The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. Conclusions: As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

MO‐A‐ValB‐01: Tradeoffs in Image Quality and Radiation Dose for CT

In CT scanning,image quality has many components and is influenced by many technical parameters. While image quality has always been a concern for the physics community, clinically acceptable image quality has become even more of an issue as strategies to reduce radiation dose — to all patients, but especially to pediatric patients— has become a focus in many radiology practices. The purpose of this presentation will be to first describe several of the components of CTimage quality — noise, slice thickness (Z‐axis resolution), low contrast resolution and high contrast resolution— as well as radiation dose and to describe how each of these may be affected by technical parameter selection. This presentation will pay particular attention to the tradeoffs that exist between different aspects of image quality, especially when the reduction of radiation dose is one of the objectives. The presentation will then explore several mechanisms that can be used to reduce radiation dose in CT exams and the implications for the diagnostic image quality of the exam. Specifically, the implications of varying the tube current*time product (mAs), pitch or tablespeed (or for axial imaging, the table increment), slice thickness, beam energy (kVp), patient (or phantom) size and dose reduction options (such as tube current modulation) will be described for both radiation dose and diagnostic image quality. Finally, this presentation will emphasize that the tradeoffs between radiation dose and image quality are clinical‐task dependent; that is, the goals of the clinically indicated exam dictate what aspect of image quality may be emphasized for that exam (low contrast resolution or high contrastspatial resolution, etc.) and this will have implications for the amount of radiation dose reduction that is acceptable. This will be illustrated with examples from selected diagnostic imaging exams. Educational Objectives: 1. Understand key components of image quality in CT scanning as well as reinforce CT radiation dose concepts. 2. Understand the impact that technical parameter selection has on the various aspects of image quality and radiation dose. 3. Examine the tradeoffs between various aspects of image quality and radiation dose. 4. Examine the impact of these tradeoffs on a few clinical imaging protocols and illustrate the task‐dependence of image quality requirements.

Estimating surface radiation dose from multidetector CT: cylindrical phantoms, anthropomorphic phantoms, and Monte Carlo simulations

Multidetector CT (MDCT) systems offer larger coverage and wider z-axis beams, resulting in larger cone angles. One impact on radiation dose is that while radiation profiles at isocenter are constant when contiguous axial scans are performed, the increased beam divergence from the larger cone angle results in significant surface dose variation. The purpose of this work was to measure the magnitude of this effect. Contiguous axial scans were acquired using an MDCT for two sizes of cylindrical phantoms and an anthropomorphic phantom. Film dosimetry and/or radiation detector measurements were performed on the surface of each phantom. Detailed mathematical models were developed for the MDCT scanner and all phantoms. Monte Carlo simulations of contiguous axial scans were performed for each phantom model. From cylindrical phantoms, film dosimetry at the surface showed differences between peak and valley that reached 50%. From the anthropomorphic phantom, measured values ranged from 7.9 to 16.2 mGy at the phantom surface. Monte Carlo simulations demonstrated these variations in both cylindrical and anthropomorphic phantoms. The magnitude of variation was also related to object size. Even when contiguous axial scans are performed on MDCT, surface radiation profiles show considerable variation. This variation will increase as MDCT cone angles increase and when non-contiguous scans (e.g. pitch > 1) are acquired. The variation is also a function of object size. While average surface doses may remain constant, peak doses may increase, which may be significant for radiation sensitive organs at or near the surface (e.g. breast, thyroid).

Evaluation of patient dose using a virtual CT scanner: Applications to 4DCT Simulation and Kilovoltage Cone-Beam Imaging

This work evaluates the effects of patient size on radiation dose from simulation imaging studies such as four-dimensional computed tomography (4DCT) and kilovoltage cone- beam computed tomography (kV-CBCT). 4DCT studies are scans that include temporal information, frequently incorporating highly over-sampled imaging series necessary for retrospective sorting as a function of respiratory phase. This type of imaging study can result in a significant dose increase to the patient due to the slower table speed as compared with a conventional axial or helical scan protocol. Kilovoltage cone-beam imaging is a relatively new imaging technique that requires an on-board kilovoltage x-ray tube and a flat-panel detector. Instead of porting individual reference fields, the kV tube and flat-panel detector are rotated about the patient producing a cone-beam CT data set (kV-CBCT). To perform these investigations, we used Monte Carlo simulation methods with detailed models of adult patients and virtual source models of multidetector computed tomography (MDCT) scanners. The GSF family of three-dimensional, voxelized patient models, were implemented as input files using the Monte Carlo code MCNPX. The adult patient models represent a range of patient sizes and have all radiosensitive organs previously identified and segmented. Simulated 4DCT scans of each voxelized patient model were performed using a multi-detector CT source model that includes scanner specific spectra, bow-tie filtration, and helical source path. Standard MCNPX tally functions were applied to each model to estimate absolute organ dose based upon an air- kerma normalization measurement for nominal scanner operating parameters.

Assessing nodule detection on lung cancer screening CT: the effects of tube current modulation and model observer selection on detectability maps

Lung cancer screening using low dose CT has been shown to reduce lung cancer related mortality and been approved for widespread use in the US. These scans keep radiation doses low while maximizing the detection of suspicious lung lesions. Tube current modulation (TCM) is one technique used to optimize dose, however limited work has been done to assess TCM’s effect on detection tasks. In this work the effect of TCM on detection is investigated throughout the lung utilizing several different model observers (MO). 131 lung nodules were simulated at 1mm intervals in each lung of the XCAT phantom. A Sensation 64 TCM profile was generated for the XCAT phantom and 2500 noise realizations were created using both TCM and a fixed TC. All nodules and noise realizations were reconstructed for a total of 262 (left and right lungs) nodule reconstructions and 10 000 XCAT lung reconstructions. Single-slice Hotelling (HO) and channelized Hotelling (CHO) observers, as well as a multislice CHO were used to assess area-under-the-curve (AUC) as a function of nodule location in both the fixed TC and TCM cases. As expected with fixed TC, nodule detectability was lowest through the shoulders and leveled off below mid-lung; with TCM, detectability was unexpectedly highest through the shoulders, dropping sharply near the mid-lung and then increasing into the abdomen. Trends were the same for all model observers. These results suggest that TCM could be further optimized for detection and that detectability maps present exciting new opportunities for TCM optimization on a patient-specific level.

TU-H-207A-08: Estimating Radiation Dose From Low-Dose Lung Cancer Screening CT Exams Using Tube Current Modulation

PURPOSE The purpose of this work is to estimate effective and lung doses from a low-dose lung cancer screening CT protocol using Tube Current Modulation (TCM) across patient models of different sizes. METHODS Monte Carlo simulation methods were used to estimate effective and lung doses from a low-dose lung cancer screening protocol for a 64-slice CT (Sensation 64, Siemens Healthcare) that used TCM. Scanning parameters were from the AAPM protocols. Ten GSF voxelized patient models were used and had all radiosensitive organs identified to facilitate estimating both organ and effective doses. Predicted TCM schemes for each patient model were generated using a validated method wherein tissue attenuation characteristics and scanner limitations were used to determine the TCM output as a function of table position and source angle. The water equivalent diameter (WED) was determined by estimating the attenuation at the center of the scan volume for each patient model. Monte Carlo simulations were performed using the unique TCM scheme for each patient model. Lung doses were tallied and effective doses were estimated using ICRP 103 tissue weighting factors. Effective and lung dose values were normalized by scanspecific 32 cm CTDIvol values based upon the average tube current across the entire simulated scan. Absolute and normalized doses were reported as a function of WED for each patient. RESULTS For all ten patients modeled, the effective dose using TCM protocols was below 1.5 mSv. Smaller sized patient models experienced lower absolute doses compared to larger sized patients. Normalized effective and lung doses showed some dependence on patient size (R2 = 0.77 and 0.78, respectively). CONCLUSION Effective doses for a low-dose lung screening protocol using TCM were below 1.5 mSv for all patient models used in this study. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.

SU‐E‐I‐35: Development of Stand‐Alone Filtered Backprojection and Iterative Reconstruction Methods Using the Raw CT Data Exported From Clinical Lung Screening Scans

Purpose: We are developing a research pipeline for generating CT image series that represent a wide variety of acquisition and reconstruction conditions. As part of this effort, we need stand-alone filtered backprojection (FBP) and iterative reconstruction methods that: (1) can operate on the raw CT data from clinical scans and (2) can be integrated into an acquisition/reconstruction pipeline for evaluating effects of acquisition and reconstruction settings on Quantitative Imaging metrics and CAD algorithms. Methods: Two reconstruction methods were developed: (1) a weighted FBP method, and (2) an iterative method based on sequential minimization of a penalized least-squares objective function (i.e. iterative coordinate descent). Both methods were adapted from previously-published algorithms. Using information about the raw CT data format obtained through a research agreement with Siemens Healthcare, we extracted the sinogram from a low-dose lung screening case acquired on a Sensation 64 scanner as part of the National Lung Screening Trial. We reconstructed the raw data on the scanner with a B50 kernel and again with each of our standalone reconstruction methods. A relatively sharp kernel was used in our FBP method to match the appearance of the B50 kernel. The iterative method used a regularization parameter of 1 and a stopping criterion of 200 iterations. The reconstructed field of view was 29 cm for all methods. Results: Reconstructed images from our FBP method agreed very well with images reconstructed at the scanner. Computation speed was a limiting factor for the iterative method, but initial downsampled results and images of a thin slab of the scanned volume demonstrated substantial potential. Various artifacts should be addressed before direct comparisons of image quality can be made. Conclusion: Our stand-alone FBP and iterative reconstruction methods show potential for developing a general acquisition/reconstruction research pipeline that can be applied to Quantitative Imaging and CAD applications. NCI grant U01 CA181156 (Quantitative Imaging Network) and Tobacco Related Disease Research Project grant 22RT-0131.

TU-G-204-05: The Effects of CT Acquisition and Reconstruction Conditions On Computed Texture Feature Values of Lung Lesions.

PURPOSE: Texture features have been investigated as a biomarker of response and malignancy. Because these features reflect local differences in density, they may be influenced by acquisition and reconstruction parameters. The purpose of this study was to investigate the effects of radiation dose level and reconstruction method on features derived from lung lesions. METHODS: With IRB approval, 33 lung tumor cases were identified from clinically indicated thoracic CT scans in which the raw projection (sinogram) data were available. Based on a previously-published technique, noise was added to the raw data to simulate reduced-dose versions of each case at 25%, 10% and 3% of the original dose. Original and simulated reduced dose projection data were reconstructed with conventional and two iterative-reconstruction settings, yielding 12 combinations of dose/recon conditions. One lesion from each case was contoured. At the reference condition (full dose, conventional recon), 17 lesions were randomly selected for repeat contouring (repeatability). For each lesion at each dose/recon condition, 151 texture measures were calculated. A paired differences approach was employed to compare feature variation from repeat contours at the reference condition to the variation observed in other dose/recon conditions (reproducibility). The ratio of standard deviation of the reproducibility to repeatability was used as the variation measure for each feature. RESULTS: The mean variation (standard deviation) across dose levels and kernel was significantly different with a ratio of 2.24 (±5.85) across texture features (p=0.01). The mean variation (standard deviation) across dose levels with conventional recon was also significantly different with 2.30 (7.11) (p=0.025). The mean variation across reconstruction settings of original dose has a trend in showing difference with 1.35 (2.60) among all features (p=0.09). CONCLUSION: Texture features varied considerably with variations in dose and reconstruction condition. Care should be taken to standardize these conditions when using texture as a quantitative feature. This effort supported in part by a grant from the National Cancer Institutes Quantitative Imaging Network (QIN): U01 CA181156; The UCLA Department of Radiology has a Master Research Agreement with Siemens Healthcare; Dr. McNitt-Gray has previously received research support from Siemens Healthcare.

TU-G-204-07: A Research Pipeline to Simulate a Wide Range of CT Image Acquisition and Reconstruction Parameters and Assess the Performance of Quantitative Imaging and CAD Systems

PURPOSE: Quantitative Imaging and CAD tasks performed with CT (e.g. lung nodule detection, assessment of tumor size, etc.) may be sensitive to image acquisition and reconstruction parameters such as dose level, image thickness and reconstruction method (FBP, iterative, etc.). The purpose of this work was to develop a research pipeline for generating CT image series that represent a wide variety of acquisition and reconstruction conditions under which CAD and Quantitative Imaging performance would be evaluated. METHODS: With IRB approval, we have collected the raw CT data from hundreds of patients. These raw sinogram files serve as the input to the research pipeline. To simulate a wide range of dose levels, we developed software which adds noise to the sinogram. Multiple reduced-dose sinograms can be generated for a single patient, and those reduced-dose sinograms are then fed either to the scanners reconstruction engine or to our in-house reconstruction engine; each has conventional filtered back projection (FBP) and iterative reconstruction methods. After generating image series across a range of dose levels and reconstruction methods, we can evaluate the performance of various quantitative imaging or CAD tools in tasks such as automated and semi-automated lesion segmentation, assessment of lesion size, and measurement of density or texture. RESULTS: We have successfully applied this pipeline across a range of clinical CT applications, including: (1) chest oncology, where the pipeline was used to quantify the effects of dose and reconstruction method on nodule volumetry, and (2) lung cancer screening, where the pipeline is being used to measure the robustness of an automated CAD algorithm with respect to dose. CONCLUSION: The acquisition/reconstruction pipeline shows promise for investigating and quantifying the effects of dose and reconstruction method on various clinical CT applications. NCI grant U01 CA181156 (Quantitative Imaging Network); Tobacco Related Disease Research Project grant 22RT-0131.