S Young

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.

Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features

Lack of classifier robustness is a barrier to widespread adoption of computer-aided diagnosis systems for computed tomography (CT). We propose a novel Robustness-Driven Feature Selection (RDFS) algorithm that preferentially selects features robust to variations in CT technical factors. We evaluated RDFS in CT classification of fibrotic interstitial lung disease using 3D texture features. CTs were collected for 99 adult subjects separated into three datasets: training, multi-reconstruction, testing. Two thoracic radiologists provided cubic volumes of interest corresponding to six classes: pulmonary fibrosis, ground-glass opacity, honeycombing, normal lung parenchyma, airway, vessel. The multi-reconstruction dataset consisted of CT raw sinogram data reconstructed by systematically varying slice thickness, reconstruction kernel, and tube current (using a synthetic reduced-tube-current algorithm). Two support vector machine classifiers were created, one using RDFS (“with-RDFS”) and one not (“without-RDFS”). Classifier robustness was compared on the multi-reconstruction dataset, using Cohens kappa to assess classification agreement against a reference reconstruction. Classifier performance was compared on the testing dataset using the extended g-mean (EGM) measure. With-RDFS exhibited superior robustness (kappa 0.899-0.989) compared to without-RDFS (kappa 0.827-0.968). Both classifiers demonstrated similar performance on the testing dataset (EGM 0.778 for with-RDFS; 0.785 for without-RDFS), indicating that RDFS does not compromise classifier performance when discarding nonrobust features. RDFS is highly effective at improving classifier robustness against slice thickness, reconstruction kernel, and tube current without sacrificing performance, a result that has implications for multicenter clinical trials that rely on accurate and reproducible quantitative analysis of CT images collected under varied conditions across multiple sites, scanners, and timepoints.

The Effect of Radiation Dose Reduction on Computer-Aided Detection (CAD) Performance in a Low-Dose Lung Cancer Screening Population

Purpose Lung cancer screening with low‐dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer‐aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population. Methods The raw projection data files were collected from 481 patients who underwent low‐dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard‐sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced‐dose simulation software to create simulated reduced‐dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi‐automatically, and all images and segmentations were input to an in‐house CAD algorithm trained on higher dose scans (75–300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule‐ and patient‐level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub‐analyses were performed. Results From the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty‐seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient‐level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient‐level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false‐positive rates were 3, 5, and 13 per case. Conclusions CAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high‐noise screening exams.

Technical Note: FreeCT_wFBP: A robust, efficient, open‐source implementation of weighted filtered backprojection for helical, fan‐beam CT

PURPOSE With growing interest in quantitative imaging, radiomics, and CAD using CT imaging, the need to explore the impacts of acquisition and reconstruction parameters has grown. This usually requires extensive access to the scanner on which the data were acquired and its workflow is not designed for large-scale reconstruction projects. Therefore, the authors have developed a freely available, open-source software package implementing a common reconstruction method, weighted filtered backprojection (wFBP), for helical fan-beam CT applications. METHODS FreeCT_wFBP is a low-dependency, GPU-based reconstruction program utilizing c for the host code and Nvidia CUDA C for GPU code. The software is capable of reconstructing helical scans acquired with arbitrary pitch-values, and sampling techniques such as flying focal spots and a quarter-detector offset. In this work, the software has been described and evaluated for reconstruction speed, image quality, and accuracy. Speed was evaluated based on acquisitions of the ACR CT accreditation phantom under four different flying focal spot configurations. Image quality was assessed using the same phantom by evaluating CT number accuracy, uniformity, and contrast to noise ratio (CNR). Finally, reconstructed mass-attenuation coefficient accuracy was evaluated using a simulated scan of a FORBILD thorax phantom and comparing reconstructed values to the known phantom values. RESULTS The average reconstruction time evaluated under all flying focal spot configurations was found to be 17.4 ± 1.0 s for a 512 row × 512 column × 32 slice volume. Reconstructions of the ACR phantom were found to meet all CT Accreditation Program criteria including CT number, CNR, and uniformity tests. Finally, reconstructed mass-attenuation coefficient values of water within the FORBILD thorax phantom agreed with original phantom values to within 0.0001 mm(2)/g (0.01%). CONCLUSIONS FreeCT_wFBP is a fast, highly configurable reconstruction package for third-generation CT available under the GNU GPL. It shows good performance with both clinical and simulated data.

Estimating lesion volume in low-dose chest CT: How low can we go?

Purpose: To examine the potential for dose reduction in chest CT studies where lesion volume is the primary output (e.g. in therapy-monitoring applications). Methods: We added noise to the raw sinogram data from 15 chest exams with lung lesions to simulate a series of reduced-dose scans for each patient. We reconstructed the reduced-dose data on the clinical workstation and imported the resulting image series into our quantitative imaging database for lesion contouring. One reader contoured the lesions (one per patient) at the clinical reference dose (100%) and 8 simulated fractions of the clinical dose (50, 25, 15, 10, 7, 5, 4, and 3%). Dose fractions were hidden from the reader to reduce bias. We compared clinical and reduced-dose volumes in terms of bias error and variability (4x the standard deviation of the percent differences). Results: Averaging over all lesions, the bias error ranged from -0.6% to 10.6%. Variability ranged from 92% at 3% of clinical dose to 54% at 50% of clinical dose. Averaging over only the smaller lesions (<1cm equivalent diameter), bias error ranged from -9.2% to 14.1% and variability ranged from 125% at 3% dose to 33.9% at 50% dose. Conclusions: The reader’s variability decreased with dose, especially for smaller lesions. However, these preliminary results are limited by potential recall bias, a small patient cohort, and an overly-simplified task. Therapy monitoring often involves checking for new lesions, which may influence the reader’s clinical dose threshold for acceptable performance.

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.

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.

Effects of CT dose and nodule characteristics on lung-nodule detectability in a cohort of 90 national lung screening trial patients

Lung cancer screening CT is already performed at low dose. There are many techniques to reduce the dose even further, but it is not clear how such techniques will affect nodule detectability. In this work, we used an in-house CAD algorithm to evaluate detectability. 90348 patients and their raw CT data files were drawn from the National Lung Screening Trial (NLST) database. All scans were acquired at ~2 mGy CTDIvol with fixed tube current, 1 mm slice thickness, and B50 reconstruction kernel on a Sensation 64 scanner (Siemens Healthcare). We used the raw CT data to simulate two additional reduced-dose scans for each patient corresponding to 1 mGy (50%) and 0.5 mGy (25%). Radiologists’ findings on the NLST reader forms indicated 65 nodules in the cohort, which we subdivided based on LungRADS criteria. For larger category 4 nodules, median sensitivities were 100% at all three dose levels, and mean sensitivity decreased with dose. For smaller nodules meeting the category 2 or 3 criteria, the dose dependence was less obvious. Overall, mean patient-level sensitivity varied from 38.5% at 100% dose to 40.4% at 50% dose, a difference of only 1.9%. However, the false-positive rate quadrupled from 1 per case at 100% dose to 4 per case at 25% dose. Dose reduction affected lung-nodule detectability differently depending on the LungRADS category, and the false-positive rate was very sensitive at sub-screening dose levels. Thus, care should be taken to adapt CAD for the very challenging noise characteristics of screening.