Image quality with iterative reconstruction techniques in CT of the lungs?A phantom study

Background Iterative reconstruction techniques for reducing radiation dose and improving image quality in CT have proved to work differently for different patient sizes, dose levels, and anatomical areas. Purpose This study aims to compare image quality in CT of the lungs between four high-end CT scanners using the recommended reconstruction techniques at different dose levels and patient sizes. Material and methods A lung phantom and an image quality phantom were scanned with four high-end scanners at fixed dose levels. Images were reconstructed with and without iterative reconstruction. Contrast-to-noise ratio, modulation transfer function, and peak frequency of the noise power spectrum were measured. Results IMR1 Sharp+ and VEO improved contrast-to-noise ratio to a larger extent than the other iterative techniques, while maintaining spatial resolution. IMR1 Sharp+ also maintained noise texture. Conclusions IMR1 Sharp+ was the only reconstruction technique in this study which increased CNR to a large extent, while maintaining all other image quality parameters measured in this study.publishedVersion© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/)

100 days with scans of the same Catphan phantom on the same CT scanner

Quality control (QC) of CT scanners is important to evaluate image quality and radiation dose. Different QC phantoms for testing image quality parameters on CT are commercially available, and Catphan phantoms are widely used for this purpose. More data from measured image quality parameters on CT are necessary to assess test methods, tolerance levels, and test frequencies. The aim of this study was to evaluate the stability of essential image quality parameters for axial and helical scans on one CT scanner over time. A Catphan 600 phantom was scanned on a Philips Ingenuity CT scanner for 100 days over a period of 6 months. At each day of testing, one helical scan covering the entire phantom and four axial scans covering four different modules in the phantom were performed. All images were uploaded into Image Owl for automatic analysis of CT numbers, modular transfer function (MTF), low‐contrast resolution, noise, and uniformity. In general, the different image quality parameters for both scan techniques were stable over time compared to given tolerance levels. Average measured CT numbers differed between axial and helical scans, while MTF was almost identical for helical and axial scans. Axial scans had better low‐contrast resolution and less noise than helical scans. The uniformity was relatively similar for axial and helical scans. Most standard deviations of measured values were larger for helical scans compared to axial scans. Test results in this study were stable over time for both scan techniques, but further studies on different CT scanners are required to confirm that this also holds true for other systems

Image texture and radiation dose properties in CT

The aim of this study was to compare image noise properties of GE Discovery HD 750 and Toshiba Aquilion ONE. The uniformity section of a Catphan 600 image quality assurance phantom was scanned with both scanners, at different dose levels and with extension rings simulating patients of different sizes. 36 datasets were obtained and analyzed in terms of noise power spectrum. All the results prove that introduction of extension rings significantly altered the image quality with respect to noise properties. Without extension rings, the Toshiba scanner had lower total visible noise than GE (with GE as reference: FC18 had 82% and FC08 had 80% for 10 mGy, FC18 had 77% and FC08 74% for 15 mGy, FC18 had 80% and FC08 77% for 20 mGy). The total visible noise (TVN) for 20 and 15 mGy were similar for the phantom with the smallest additional extension ring, while Toshiba had higher TVN than GE for the 10 mGy dose level (120% FC18, 110% FC08). For the second and third ring, the GE images had lower TVN than Toshiba images for all dose levels (Toshiba TVN is greater than 155% for all cases). The results indicate that GE potentially has less image noise than Toshiba for larger patients. The Toshiba FC18 kernel had higher TVN than the Toshiba FC08 kernel with additional beam hardening correction for all dose levels and phantom sizes (120%, 107%, and 106% for FC18 compared to 110%, 98%, and 97%, for FC08, for 10, 15 and 20 mGy doses, respectively)

Image texture and radiation dose properties in CT

The aim of this study was to compare image noise properties of GE Discovery HD 750 and Toshiba Aquilion ONE. The uniformity section of a Catphan 600 image quality assurance phantom was scanned with both scanners, at different dose levels and with extension rings simulating patients of different sizes. 36 datasets were obtained and analyzed in terms of noise power spectrum. All the results prove that introduction of extension rings significantly altered the image quality with respect to noise properties. Without extension rings, the Toshiba scanner had lower total visible noise than GE (with GE as reference: FC18 had 82% and FC08 had 80% for 10 mGy, FC18 had 77% and FC08 74% for 15 mGy, FC18 had 80% and FC08 77% for 20 mGy). The total visible noise (TVN) for 20 and 15 mGy were similar for the phantom with the smallest additional extension ring, while Toshiba had higher TVN than GE for the 10 mGy dose level (120% FC18, 110% FC08). For the second and third ring, the GE images had lower TVN than Toshiba images for all dose levels (Toshiba TVN is greater than 155% for all cases). The results indicate that GE potentially has less image noise than Toshiba for larger patients. The Toshiba FC18 kernel had higher TVN than the Toshiba FC08 kernel with additional beam hardening correction for all dose levels and phantom sizes (120%, 107%, and 106% for FC18 compared to 110%, 98%, and 97%, for FC08, for 10, 15 and 20 mGy doses, respectively)

Improved Liver Lesion Conspicuity With Iterative Reconstruction in Computed Tomography Imaging.

Studies on iterative reconstruction techniques on computed tomographic (CT) scanners show reduced noise and changed image texture. The purpose of this study was to address the possibility of dose reduction and improved conspicuity of lesions in a liver phantom for different iterative reconstruction algorithms. An anthropomorphic upper abdomen phantom, specially designed for receiver operating characteristic analysis was scanned with 2 different CT models from the same vendor, GE CT750 HD and GE Lightspeed VCT. Images were obtained at 3 dose levels, 5, 10, and 15mGy, and reconstructed with filtered back projection (FBP), and 2 different iterative reconstruction algorithms; adaptive statistical iterative reconstruction and Veo. Overall, 5 interpreters evaluated the images and receiver operating characteristic analysis was performed. Standard deviation and the contrast to noise ratio were measured. Veo image reconstruction resulted in larger area under curves compared with those adaptive statistical iterative reconstruction and FBP image reconstruction for given dose levels. For the CT750 HD, iterative reconstruction at the 10mGy dose level resulted in larger or similar area under curves compared with FBP at the 15mGy dose level (0.88-0.95 vs 0.90). This was not shown for the Lightspeed VCT (0.83-0.85 vs 0.92). The results in this study indicate that the possibility for radiation dose reduction using iterative reconstruction techniques depends on both reconstruction technique and the CT scanner model used

Three-stage segmentation of lung region from CT images using deep neural networks

Background: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. Methods: We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. Results: The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. Conclusion: The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement

Three-stage segmentation of lung region from CT images using deep neural networks

Abstract Background Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. Methods We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. Results The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. Conclusion The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement

Quantitative Measurements Versus Receiver Operating Characteristics and Visual Grading Regression in CT Images Reconstructed with Iterative Reconstruction. A Phantom Study

Rationale and Objectives: This study aimed to evaluate the correlation of quantitative measurements with visual grading regression (VGR) and receiver operating characteristics (ROC) analysis in computed tomography (CT) images reconstructed with iterative reconstruction. Materials and Methods: CT scans on a liver phantom were performed on CT scanners from GE, Philips, and Toshiba at three dose levels. Images were reconstructed with filtered back projection (FBP) and hybrid iterative techniques (ASiR, iDose, and AIDR 3D of different strengths). Images were visually assessed by five readers using a four- and five-grade ordinal scale for liver low contrast lesions and for 10 image quality criteria. The results were analyzed with ROC and VGR. Standard deviation, signal-to-noise ratios, and contrast-to-noise ratios were measured in the images. Results: All data were compared to FBP. The results of the quantitative measurements were improved for all algorithms. ROC analysis showed improved lesion detection with ASiR and AIDR and decreased lesion detection with iDose. VGR found improved noise properties for all algorithms, increased sharpness with iDose and AIDR, and decreased artifacts from the spine with AIDR, whereas iDose increased the artifacts from the spine. The contrast in the spine decreased with ASiR and iDose. Conclusions: Improved quantitative measurements in images reconstructed with iterative reconstruction compared to FBP are not equivalent to improved diagnostic image accuracy