Adaptive iterative dose reduction using 3D processing for reduced- and low-dose pulmonary CT: comparison with standard-dose CT for image noise reduction and radiological findings

Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10-2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy.

Image quality assessment in low-dose COVID-19 chest CT examinations

BACKGROUND

Low-dose thoracic protocols were developed massively during the COVID-19 outbreak.

PURPOSE

To study the impact on image quality (IQ) and the diagnosis reliability of COVID-19 low-dose chest computed tomography (CT) protocols.

MATERIAL AND METHODS

COVID-19 low-dose protocols were implemented on third- and second-generation CT scanners considering two body mass index (BMI) subgroups (<25 kg/m2 and >25 kg/m2). Contrast-to-noise ratios (CNR) were compared with a Catphan phantom. Next, two radiologists retrospectively assessed IQ for 243 CT patients using a 5-point Linkert scale for general IQ and diagnostic criteria. Kappa score and Wilcoxon rank sum tests were used to compare IQ score and CTDIvol between radiologists, protocols, and scanner models.

RESULTS

In vitro analysis of Catphan inserts showed in majority significantly decreased CNR for the low dose versus standard acquisition protocols on both CT scanners. However, in vivo, there was no impact on the diagnosis: sensitivity and specificity were ≥0.8 for all protocols and CT scanners. The third-generation scanner involved a significantly lower dose compared to the second-generation scanner (CTDIvol of 1.8 vs. 2.6 mGy for BMI <25 kg/m2 and 3.3 vs. 4.6 mGy for BMI >25 kg/m2). Still, the third-generation scanner showed a significantly higher IQ with the low-dose protocol compared to the second-generation scanner (30.9 vs. 28.1 for BMI <25 kg/m2 and 29.9 vs. 27.8 for BMI >25 kg/m2). Finally, the two radiologists had good global inter-reader agreement (kappa ≥0.6) for general IQ.

CONCLUSION

Low-dose protocols provided sufficient IQ independently of BMI subgroups and CT models without any impact on diagnosis reliability.

Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images

PURPOSE

Deep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with other reconstruction methods. The purpose of this study was to compare the capabilities of DLR for image quality improvement and lung texture evaluation with those of hybrid-type iterative reconstruction (IR) for standard-, reduced- and ultra-low-dose CTs (SDCT, RDCT and ULDCT) obtained with high-definition CT (HDCT) and reconstructed at 0.25-mm, 0.5-mm and 1-mm section thicknesses with 512 × 512 or 1024 × 1024 matrixes for patients with various pulmonary diseases.

MATERIALS AND METHODS

Forty age-, gender- and body mass index-matched patients with various pulmonary diseases underwent SDCT (CT dose index volume : mean ± standard deviation, 9.0 ± 1.8 mGy), RDCT (CTDIvol: 1.7 ± 0.2 mGy) and ULDCT (CTDIvol: 0.8 ± 0.1 mGy) at a HDCT. All CT data set were then reconstructed with 512 × 512 or 1024 × 1024 matrixes by means of hybrid-type IR and DLR. SNR of lung parenchyma and probabilities of all lung textures were assessed for each CT data set. SNR and detection performance of each lung texture reconstructed with DLR and hybrid-type IR were then compared by means of paired t tests and ROC analyses for all CT data at each section thickness.

RESULTS

Data for each radiation dose showed DLR attained significantly higher SNR than hybrid-type IR for each of the CT data (p < 0.0001). On assessments of all findings except consolidation and nodules or masses, areas under the curve (AUCs) for ULDCT with hybrid-type IR for each section thickness (0.91 ≤ AUC ≤ 0.97) were significantly smaller than those with DLR (0.97 ≤ AUC ≤ 1, p < 0.05) and the standard protocol (0.98 ≤ AUC ≤ 1, p < 0.05).

CONCLUSION

DLR is potentially more effective for image quality improvement and lung texture evaluation than hybrid-type IR on all radiation dose CTs obtained at HDCT and reconstructed with each section thickness with both matrixes for patients with a variety of pulmonary diseases.

Evaluation of three-dimensional dual-energy CT cholangiopancreatography image quality in patients with pancreatobiliary dilatation: Comparison with conventional single-energy CT

Objective

This study aimed to evaluate three-dimensional (3D) negative-contrast CT cholangiopancreatography (nCTCP) image quality using dual-energy CT (DECT) with iterative reconstruction (IR) technique in patients with pancreatobiliary dilatation compared with single-energy CT (SECT).

Methods

Of the patients, 67 and 56 underwent conventional SECT (SECT set) and DECT with IR technique (DECT set), respectively. All patients were retrospectively analyzed during the portal phase to compare objective image quality and other data including patient demographics, hepatic and pancreatic parenchymal enhancement, noise, and attenuation difference (AD) between dilated ducts and enhanced hepatic parenchyma, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and CT volume dose index (CTDIvol). Two radiologists used the five-point Likert scale to evaluate the subjective image quality of 3D nCTCP regarding image noise, sharpness of dilated ducts, and overall image quality. Statistical analyses used the Mann-Whitney U test.

Results

No significant difference in patient demographics in either CT set was showed during objective evaluation (p > 0.05). However, higher hepatic and pancreatic parenchymal enhancement, AD, SNR, and CNR and lower hepatic and pancreatic noise (p < 0.005) as well as CTDIvol (p = 0.005) on DECT than on SECT were observed. Higher mean grades on DECT than on SECT were showed for image noise (4.65 vs 3.92), sharpness of dilated ducts (4.52 vs 3.94), and overall image quality (4.45 vs 3.91; p < 0.001), respectively during subjective evaluation.

Conclusion

A higher overall image quality and lower radiation dose on 3D nCTCP can be obtained by DECT with IR technique than with conventional SECT in patients with pancreatobiliary dilatation.

Area-Detector Computed Tomography for Pulmonary Functional Imaging

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program

We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists’ subjective evaluation criteria according to the Korea institute for accreditation of medical imaging (KIAMI) guidelines, where it judges whether a scan range is beyond landmarks’ criterion. The algorithm consists of three stages: deep learning-based landmark segmentation, rule-based logical operations, and overscan determination. A total of 210 cases from a single institution (internal data) and 50 cases from 47 institutions (external data) were utilized for performance evaluation. Area under the receiver operating characteristic (AUROC), accuracy, sensitivity, specificity, and Cohen’s kappa were used as evaluation metrics. Fisher’s exact test was performed to present statistical significance for the overscan detectability, and univariate logistic regression analyses were performed for validation. Furthermore, an excessive effective dose was estimated by employing the amount of overscan and the absorbed dose to effective dose conversion factor. The algorithm presented AUROC values of 0.976 (95% confidence interval [CI]: 0.925–0.987) and 0.997 (95% CI: 0.800–0.999) for internal and external dataset, respectively. All metrics showed average performance scores greater than 90% in each evaluation dataset. The AI-assisted overscan decision and the radiologist’s manual evaluation showed a statistically significance showing a p-value less than 0.001 in Fisher’s exact test. In the logistic regression analysis, demographics (age and sex), data source, CT vendor, and slice thickness showed no statistical significance on the algorithm (each p-value > 0.05). Furthermore, the estimated excessive effective doses were 0.02 ± 0.01 mSv and 0.03 ± 0.05 mSv for each dataset, not a concern within slight deviations from an acceptable scan range. We hope that our proposed overscan decision algorithm enables the retrospective scan range monitoring in LDCT for lung cancer screening program, and follows an as low as reasonably achievable (ALARA) principle.

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy

Simple Summary

Successful surgery in robot-assisted partial nephrectomy (RAPN), especially for highly complex tumors, relies on a detailed understanding of the anatomical relations of the tumor absolute and relative to the urinary tract and the vascular structures, including the renal pedicle. Intraoperative navigation with accurate information regarding tumor position relative to the surrounding urinary vascular structures undoubtedly assists the surgeon during RAPN. In this report, we performed RAPN with intraoperative navigation using a novel computed tomography scanner (UHR-CT) and compared its perioperative and short-term functional outcomes to those of area-detector CT (ADCT). We found that this novel navigation system using UHR-CT provided a shorter warm ischemia time and lower estimated blood loss than ADCT, and concluded this could be a useful tool for patients who undergo RAPN. This is the first report to evaluate the feasibility and usefulness of UHR-CT for intraoperative navigation during RAPN.

Abstract

To assess the perioperative and short-term functional outcomes of robot-assisted partial nephrectomy (RAPN) with intraoperative navigation using an ultra-high-resolution computed tomography (UHR-CT) scanner, we retrospectively analyzed 323 patients who underwent RAPN using an UHR-CT or area-detector CT (ADCT). Perioperative outcomes and the postoperative preservation ratio of estimated glomerular filtration rate (eGFR) were compared. After the propensity score matching, we evaluated 99 patients in each group. Although the median warm ischemia time (WIT) was less than 25 min in both groups, it was significantly shorter in the UHR-CT group than in the ADCT group (15 min vs. 17 min, p = 0.032). Moreover, the estimated blood loss (EBL) was significantly lower in the UHR-CT group than in the ADCT group (33 mL vs. 50 mL, p = 0.028). However, there were no significant intergroup differences in the postoperative preservation ratio of eGFR at 3 or 6 months of follow-up (ADCT 91.8% vs. UHR-CT 93.5%, p = 0.195; and ADCT 91.7% vs. UHR-CT 94.0%, p = 0.160, respectively). Although no differences in short-term renal function were observed in intraoperative navigation for RAPN in this propensity score–matched cohort, this study is the first to demonstrate that UHR-CT resulted in a shorter WIT and lower EBL than ADCT.

Impact of multidetector computed tomography scan parameters, novel reconstruction settings, and lung nodule characteristics on nodule diameter measurements: A Phantom Study

PURPOSE

Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V) in combination with various CT scan parameters on the semi-automated quantification using various lung nodules.

METHODS

A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (β) were reported to demonstrate effect size and directionality.

RESULTS

The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (β = 9.000, p<0.001). APE had an inverse relationship with nodule diameter (mm; β = -3.499, p<0.001) and tube current (mA; β = -0.006, p<0.001). MAPE did not vary based on the ASIR-V level (range: 5.7-13.1%).

CONCLUSION

Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction. This article is protected by copyright. All rights reserved.

Emerging Preclinical and Clinical Applications of Theranostics for Nononcological Disorders

Studies in nuclear medicine have shed light on molecular imaging and therapeutic approaches for oncological and nononcological conditions. Using the same radiopharmaceuticals for diagnosis and therapeutics of malignancies, the theranostics approach, has improved clinical management of patients. Theranostic approaches for nononcological conditions are recognized as emerging topics of research. This review focuses on preclinical and clinical studies of nononcological disorders that include theranostic strategies. Theranostic approaches are demonstrated as possible in the clinical management of infections and inflammations. There is an emerging need for randomized trials to specify the factors affecting validity and efficacy of theranostic approaches in nononcological diseases.

Usefulness of Model-Based Iterative Reconstruction in Brain CT as Compared With Hybrid Iterative Reconstruction

OBJECTIVE

The aim of this study was to compare the contrast of gray to white matter between forward-projected model-based iterative reconstruction solution (FIRST) and hybrid iterative reconstruction (IR) by measuring computed tomography value of brain parenchyma.

METHODS

Computed tomography values of the gray and white matter in 15 areas of 21 patients (7 males, 14 females; average age, 49.5 ± 10.7 years) were measured and compared between FIRST and hybrid IR with filtered back projection (FBP) using 2 different reconstruction kernels FC21 and FC26.

RESULTS

The ratio of gray to white matter obtained using FIRST (1.25 ± 0.08) was significantly higher than that obtained using FBP with both kernel FC21 (1.13 ± 0.03) and kernel FC26 (1.22 ± 0.06).

CONCLUSIONS

FIRST increases the contrast between the gray and white matter, and decreases noise in brain computed tomography compared with FBP with hybrid IR.

Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images

Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon's statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTF_Task), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNR_LO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTF_Taskwas measured at four contrast levels, and CNR_LOwas measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

OBJECTIVES

This study aimed to evaluate the image quality of 7 iterative reconstruction (IR) algorithms in comparison to filtered back-projection (FBP) algorithm.

METHODS

An anthropomorphic chest phantom was scanned on 4 computed tomography scanners and reconstructed with FBP and IR algorithms. Image quality of anatomical details-large/medium-sized pulmonary vessels, small pulmonary vessels, thoracic wall, and small and large lesions-was scored. Furthermore, general impression of noise, image contrast, and artifacts were evaluated. Visual grading regression was used to analyze the data. Standard deviations were measured, and the noise power spectrum was calculated.

RESULTS

Iterative reconstruction algorithms showed significantly better results when compared with FBP for these criteria (regression coefficients/P values in parentheses): vessels (FIRST: -1.8/0.05, AIDR Enhanced: <-2.3/0.01, Veo: <-0.1/0.03, ADMIRE: <-2.1/0.04), lesions (FIRST: <-2.6/0.01, AIDR Enhanced: <-1.9/0.03, IMR1: <-2.7/0.01, Veo: <-2.4/0.02, ADMIRE: -2.3/0.02), image noise (FIRST: <-3.2/0.004, AIDR Enhanced: <-3.5/0.002, IMR1: <-6.1/0.001, iDose: <-2.3/0.02, Veo: <-3.4/0.002, ADMIRE: <-3.5/0.02), image contrast (FIRST: -2.3/0.01, AIDR Enhanced: -2.5/0.01, IMR1: -3.7/0.001, iDose: -2.1/0.02), and artifacts (FIRST: <-3.8/0.004, AIDR Enhanced: <-2.7/0.02, IMR1: <-2.6/0.02, iDose: -2.1/0.04, Veo: -2.6/0.02). The iDose algorithm was the only IR algorithm that maintained the noise frequencies.

CONCLUSIONS

Iterative reconstruction algorithms performed differently on all evaluated criteria, showing the importance of careful implementation of algorithms for diagnostic purposes.

A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.

Urodynamic 4D-CT evaluation: 320-row area detector CT scanner combined with PhyZiodynamics software analysis provides an innovative system to evaluate urinary flow and outlet obstructions

Background

Evaluation of the morphology of the lower urinary tract as well as the movements associated with urination are required for the symptomatic diagnosis of lower urinary tract obstruction as well as the assessment of postoperative adaptation. However, no tool currently exists for direct and easy patient evaluation.

Purpose

To evaluate lower urinary tract obstruction and postoperative adaptation using a four-dimensional (4D) virtual reality urination image (urodynamic 4D-CT image).

Material and Methods

We used a 320-row area detector CT scanner and PhyZiodynamics image analysis software to perform 197 urodynamic 4D-CT examinations on 175 first-time patients between January 2014 and March 2017.

Results

A comparison of the obtained images before and after holmium laser enucleation of the prostate revealed the morphological changes due to prostate enucleation and enabled visualization of the ideal urination conditions, showing that the anatomical structural changes during urination and the opening of the urethra play a major role in improving voiding function.

Conclusion

Using low-dosage radiation, the sharply defined moving image obtained via urodynamic 4D-CT examination can be utilized as a physiological diagnostic tool to evaluate a series of urinary movements from any angle between the prostate, urethra, and bladder in a unitary manner with the time axis added. There was negligible patient impact. This technique could provide new opportunities for the diagnosis of lower urinary tract symptoms and post-surgical adaptation assessment.

Image quality of early postoperative CT angiography with reduced contrast material and radiation dose using model-based iterative reconstruction for screening of renal pseudoaneurysms after partial nephrectomy

PURPOSE

To evaluate the image quality of early postoperative CT angiography with low contrast material and radiation dose using model-based iterative reconstruction (FIRST) for screening pseudoaneurysms after partial nephrectomy.

METHODS

CT angiography was obtained before surgery using conventional iterative dose reduction reconstruction (AIDR 3D) with 120 kVp and 600 mgI/kg of contrast material and obtained after partial nephrectomy using FIRST with 80-100 kVp and 360 mgI/kg in 35 patients. Contrast-to-noise ratio, visual image quality scores using a 5-point scale, and longest length of the unaffected renal arteries on maximum intensity projection images were retrospectively compared between FIRST and AIDR 3D.

RESULTS

No significant differences existed in contrast-to-noise ratio or image quality scores of the renal arteries between FIRST and AIDR 3D (25.8 ± 6.6 vs. 25.4 ± 7.0, p = 0.991 and 4.8 ± 0.4 vs. 4.5 ± 0.9, p = 0.515, respectively). Visualization scores and longest length of the peripheral renal arteries in FIRST were significantly superior to those of AIDR 3D (4.3 ± 0.8 vs. 3.5 ± 1.0, p < 0.001 and 100.4 ± 14.9 mm vs. 90.2 ± 15.7 mm, p = 0.010, respectively). The dose-length product with FIRST was significantly lower than that with AIDR 3D (566.1 ± 217.4 mGy.cm vs. 829.8 ± 324.9 mGy.cm, p < 0.001).

CONCLUSION

FIRST can improve visualization of the peripheral renal arteries with contrast material and radiation dose reduced by approximately 30 % compared with AIDR 3D, which enables adequate evaluation of pseudoaneurysms after partial nephrectomy.

Role of thoracic imaging in the management of lymphangioleiomyomatosis

Lymphangioleiomyomatosis (LAM) is a rare diffuse lung cystic disease (DLCD) that occurs sporadically or in association with Tuberous Sclerosis Complex (TSC). The diagnostic pathway is tracked on the identification of the disease hallmarks on chest High-Resolution Computed Tomography (HRCT). Aim of this review is to discuss the thoracic HRCT pathognomonic features, essential to rule out other DLCD. It also examines the new evidences emerging from Computed Tomography (CT) quantitative studies that, by demonstrating a specific cysts distribution and a pathological aspect of the parenchyma near the cysts, could improve our understanding of this rare disorder and supply pulmonologists with a new tool for a more appropriate long-term management. Finally, the contribution of other image techniques as low dose chest CT, Magnetic Resonance Imaging (MRI) and Ultrasound (US) is discussed.

Use of CT Imaging to Quantify Progression and Response to Treatment in Lymphangioleiomyomatosis

BACKGROUND

In lymphangioleiomyomatosis (LAM), infiltration of the lungs with smooth muscle-like LAM cells results in cystic destruction and decline in lung function, effects stabilized by sirolimus therapy. LAM lung disease is followed, in part, by high-resolution CT scans. To obtain further information from these scans, we quantified changes in lung parenchyma by analyzing image "texture."

METHODS

Twenty-six texture properties were quantified by analyzing the distribution and intensity of pixels with a computer-aided system. Both cross-sectional and longitudinal studies were performed to examine the relationships between texture properties, cyst score (percentage of lung occupied by cysts), FEV₁, and diffusion capacity for carbon monoxide (Dlco), and to determine the effect of sirolimus treatment.

RESULTS

In the cross-sectional study, 18 texture properties showed significant positive correlations with cyst score. Cyst score and 13 of the 18 texture properties showed significant differences in rates of change after sirolimus treatment; 11 also significantly predicted FEV₁ and Dlco.

CONCLUSIONS

Increased cyst score was associated with increased texture degradation near cysts. Sirolimus treatment improved lung texture surrounding cysts and stabilized cyst score. Eleven texture properties were associated with FEV₁, Dlco, cyst score, and response to sirolimus. Texture analysis may be valuable in evaluating LAM severity and treatment response.

Radiation dose reduction techniques for chest CT: Principles and clinical results

Computer tomography plays a major role in the evaluation of thoracic diseases, especially since the advent of the multidetector-row CT (MDCT) technology. However, the increase use of this technique has raised some concerns about the resulting radiation dose. In this review, we will present the various methods allowing limiting the radiation dose exposure resulting from chest CT acquisitions, including the options of image filtering and iterative reconstruction (IR) algorithms. The clinical applications of reduced dose protocols will be reviewed, especially for lung nodule detection and diagnosis of pulmonary thromboembolism. The performance of reduced dose protocols for infiltrative lung disease assessment will also be discussed. Lastly, the influence of using IR algorithms on computer-aided detection and volumetry of lung nodules, as well as on quantitative and functional assessment of chest diseases will be presented and discussed.

Forward-Projected Model-Based Iterative Reconstruction in Screening Low-Dose Chest CT: Comparison With Adaptive Iterative Dose Reduction 3D

OBJECTIVE

The objective of this study is to compare forward-projected model-based iterative reconstruction solution (FIRST), a newer fully iterative CT reconstruction method, with adaptive iterative dose reduction 3D (AIDR 3D) in low-dose screening CT for lung cancer. Differences in image noise, image quality, and pulmonary nodule detection, size, and characterization were specifically evaluated.

MATERIALS AND METHODS

Low-dose chest CT images obtained for 50 consecutive patients between December 2015 and January 2016 were retrospectively reviewed. Images were reconstructed using FIRST and AIDR 3D for both lung and soft-tissue reconstruction. Images were independently reviewed to assess image noise, subjective image quality (with use of a 5-point Likert scale, with 1 denoting far superior image quality; 2, superior quality; 3, equivalent quality; 4, inferior quality; and 5, far inferior quality), pulmonary nodule count, size of the largest pulmonary nodule, and characterization of the largest pulmonary nodule (i.e., solid, part solid, or ground glass).

RESULTS

Across all 50 cases, measured image noise was lower with FIRST than with AIDR 3D (lung window, 44% reduction, 41 ± 7 vs 74 ± 8 HU, respectively; soft-tissue window, 32% reduction, 11 ± 2 vs 16 ± 2 HU, respectively). Readers subjectively rated images obtained with FIRST as comparable to images obtained with AIDR 3D (mean [± SD] Likert score for FIRST vs AIDR 3D, 3.2 ± 0.3 for soft-tissue reconstructions and 3.0 ± 0.3 for lung reconstructions). For each reader, very good agreement regarding nodule count was noted between FIRST and AIDR 3D (interclass correlation coefficient [ICC], 0.83 for reader 1 and 0.78 for reader 2). Excellent agreement regarding nodule size (ICC, 0.99 for reader 1 and 0.99 for reader 2) and characterization of the largest nodule (kappa value, 0.92 for reader 1 and 0.82 for reader 2) also existed.

CONCLUSION

Images reconstructed with FIRST are superior to those reconstructed AIDR 3D with regard to image noise and are equivalent with regard to subjective image quality, pulmonary nodule count, and nodule characterization.

Quantitative and qualitative evaluation of hybrid iterative reconstruction, with and without noise power spectrum models: A phantom study

The purpose of this phantom study was to investigate the feasibility of dose reduction with hybrid iterative reconstruction, with and without a noise power spectrum (NPS) model, using both quantitative and qualitative evaluations. Standard dose (SD), three‐quarter dose (TQD), and half‐dose (HD) of radiation were used. Images were reconstructed with filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR 3D) (MILD, STR), and AIDR 3D enhanced (eAIDR 3D) (eMILD, eSTR). An NPS analysis, task‐based modulation transfer function (MTF_task) analysis, and comparisons of low‐contrast detectability and image texture were performed. Although the eAIDR 3D had a higher NPS value in the high‐frequency range and improved image texture and resolution as compared with AIDR 3D at the same radiation dose and iteration levels, it yielded higher noise than AIDR 3D. Additionally, although there was no statistically significant difference between SD‐FBP and the TQD series in the comparison of the mean area under the curve (AUC), the mean AUC was statistically significantly different between SD‐FBP and the HD series. NPS values in the high‐frequency range, 10% MTF_task values, low‐contrast detectability, and image textures of TQD‐eMILD were comparable to those of SD‐FBP. Our findings suggested that using eMILD can reduce the radiation dose by 25%, while potentially maintaining diagnostic performance, spatial resolution, and image texture; this could support selecting the appropriate protocol in a clinical setting.

Automated image quality assessment for chest CT scans

PURPOSE

Medical image quality needs to be maintained at standards sufficient for effective clinical reading. Automated computer analytic methods may be applied to medical images for quality assessment.

METHODS

For chest CT scans in a lung cancer screening context, an automated quality assessment method is presented that characterizes image noise and image intensity calibration. This is achieved by image measurements in three automatically segmented homogeneous regions of the scan: external air, trachea lumen air, and descending aorta blood. Profiles of CT scanner behavior are also computed.

RESULTS

The method has been evaluated on both phantom and real low-dose chest CT scans and results show that repeatable noise and calibration measures may be realized by automated computer algorithms. Noise and calibration profiles show relevant differences between different scanners and protocols.

CONCLUSIONS

Automated image quality assessment may be useful for quality control for lung cancer screening and may enable performance improvements to automated computer analysis methods.

Comparison of computer-aided detection (CADe) capability for pulmonary nodules among standard-, reduced- and ultra-low-dose CTs with and without hybrid type iterative reconstruction technique

PURPOSE

To directly compare the effect of a reconstruction algorithm on nodule detection capability of the computer-aided detection (CADe) system using standard-dose, reduced-dose and ultra-low dose chest CTs with and without adaptive iterative dose reduction 3D (AIDR 3D).

MATERIALS AND METHODS

Our institutional review board approved this study, and written informed consent was obtained from each patient. Standard-, reduced- and ultra-low-dose chest CTs (250 mA, 50 mA and 10 mA) were used to examine 40 patients, 21 males (mean age ± standard deviation: 63.1 ± 11.0 years) and 19 females (mean age, 65.1 ± 12.7 years), and reconstructed as 1 mm-thick sections. Detection of nodule equal to more than 4 mm in dimeter was automatically performed by our proprietary CADe software. The utility of iterative reconstruction method for improving nodule detection capability, sensitivity and false positive rate (/case) of the CADe system using all protocols were compared by means of McNemar's test or signed rank test.

RESULTS

Sensitivity (SE: 0.43) and false-positive rate (FPR: 7.88) of ultra-low-dose CT without AIDR 3D was significantly inferior to those of standard-dose CTs (with AIDR 3D: SE, 0.78, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.80, p < .0001, FPR: 2.63, p < .0001), reduced-dose CTs (with AIDR 3D: SE, 0.81, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.62, p < .0001, FPR: 2.95, p < .0001) and ultra-low-dose CT with AIDR 3D (SE, 0.79, p < .0001, FPR, 4.88, p = .0001).

CONCLUSION

The AIDR 3D has a significant positive effect on nodule detection capability of the CADe system even when radiation dose is reduced.

Implementation of ultra-low-dose lung protocols in CT-guided lung biopsies: feasibility and safety in the clinical setting

Objective

To evaluate the use of ultra-low-dose computed tomography (ULDCT) for CT-guided lung biopsy versus standard-dose CT (SDCT).

Methods

CT-guided lung biopsies from 115 patients (50 ULDCT, 65 SDCT) were analyzed retrospectively. SDCT settings were 120 kVp with automatic mAs modulation. ULDCT settings were 80 kVp with fixed exposure (20 mAs). Two radiologists evaluated image quality (i.e., needle artifacts, lesion contouring, vessel recognition, visibility of interlobar fissures). Complications and histological results were also evaluated.

Results

ULDCT was considered feasible for all lung interventions, showing the same diagnostic accuracy as SDCT. Its mean total radiation dose (dose–length product) was significantly reduced to 34 mGy-cm (SDCT 426 mGy-cm). Image quality and complication rates (P = 0.469) were consistent.

Conclusions

ULDCT for CT-guided lung biopsies appears safe and accurate, with a significantly reduced radiation dose. We therefore recommend routine clinical use of ULDCT for the benefit of patients and interventionalists.

A phantom study for ground-glass nodule detectability using chest digital tomosynthesis with iterative reconstruction algorithm by ten observers: association with radiation dose and nodular characteristics

OBJECTIVE

To compare detectability of simulated ground-glass nodules (GGNs) on chest digital tomosynthesis (CDT) among 12 images obtained at 6 radiation doses using 2 reconstruction algorithms and to analyze its association with nodular size and density.

METHODS

74 simulated GGNs [5, 8 and 10 mm in diameter/-630 and -800 Hounsfield units (HU) in density] were placed in a chest phantom in 14 nodular distribution patterns. 12 sets of coronal images were obtained using CDT at 6 radiation doses: 120 kV-10 mA/20 mA/80 mA/160 mA, 100 kV-80 mA and 80 kV-320 mA with and without iterative reconstruction (IR). 10 radiologists recorded GGN presence and locations by continuously distributed rating. GGN detectability was compared by receiver operating characteristic analysis among 12 images and detection sensitivities (DS) were compared among 12 images in subgroups classified by nodular diameters and densities.

RESULTS

GGN detectability at 120 kV-160 mA with IR was similar to that at 120 kV-80 mA with IR (0.614 mSv), as area under receiver operating characteristic curve was 0.798 ± 0.024 and 0.788 ± 0.025, respectively, and higher than six images acquired at 120 kV (p < 0.05). For nodules of -630 HU/8 mm, DS at 120 kV-10 mA without IR was 73.5 ± 6.0% and was similar to that by the other 11 data acquisition methods (p = 0.157). For nodules of -800 HU/10 mm, DS both at 120 kV-80 mA and 120 kV-160 mA without IR was improved by IR (56.3 ± 11.9%) (p < 0.05).

CONCLUSION

CDT demonstrated sufficient detectability for larger more-attenuated GGNs (>8 mm) even in the lowest radiation dose (0.17 mSv) and improved detectability for less-attenuated GGNs with the diameter of 10 mm at submillisievert with IR. Advances in knowledge: IR improved detectability for larger less-attenuated simulated GGNs on CDT.

State-of-the-Art Imaging of the Lung for Connective Tissue Disease (CTD)

Involvement of the respiratory system is common in connective tissue diseases (CTDs), and the resultant lung injury can affect every part of the lung: the pleura, alveoli, interstitium, vasculature, lymphatic tissue, and large and/or small airways. Most of the parenchymal manifestations of CTD are similar to those found in interstitial lung diseases (ILDs), especially idiopathic interstitial pneumonias, and can be classified using the same system. Although there is some overlap, each CTD is associated with a characteristic pattern of pulmonary involvement. For this reason, thin-section CT as well as pulmonary function tests and serum markers are utilized for diagnosis, disease severity assessment, and therapeutic efficacy evaluation of ILD associated with CTD. In addition, newly developed pulmonary magnetic resonance imaging (MRI) procedures have been recommended as useful alternative imaging options for patients with CTD. This review article will (1) address radiological findings for chest radiography and conventional or thin-section CT currently used for six major types of CTD, rheumatoid arthritis, scleroderma (progressive systemic sclerosis), polymyositis/dermatomyositis, systemic lupus erythematosus, Sjögren syndrome and mixed connective tissue disease; (2) briefly deal with radiation dose reduction for thin-section CT examination; and (3) discuss clinically applicable or state-of-the-art MR imaging for CTD patients.

Adaptive iterative dose reduction 3D (AIDR 3D) vs. filtered back projection: radiation dose reduction capabilities of wide volume and helical scanning techniques on area-detector CT in a chest phantom study

BACKGROUND

Computed tomography (CT) has important roles for lung cancer screening, and therefore radiation dose reduction by using iterative reconstruction technique and scanning methods receive widespread attention.

PURPOSE

To evaluate the effect of two reconstruction techniques (filtered back projection [FBP] and adaptive iterative dose reduction using three-dimensional processing [AIDR 3D]) and two acquisition techniques (wide-volume scan [WVS] and helical scan as 64-detector-row CT [64HS]) on the lung nodule identifications of using a chest phantom.

MATERIAL AND METHODS

A chest CT phantom including lung nodules was scanned using WVS and 64HS at nine different tube currents (TCs; range, 270-10 mA). All CT datasets were reconstructed with AIDR 3D and FBP. Standard deviation (SD) measurements by region of interest placement and qualitative nodule identifications were statistically compared. 64HS and WVS were evaluated separately, and FBP images acquired with 270 mA was defined as the standard reference.

RESULTS

SDs of all datasets with AIDR 3D showed no significant differences (P > 0.05) with standard reference. When comparing nodule identifications, area under the curve on WVS with AIDR 3D with TC <30 mA, on 64HS with AIDR 3D with TC <40 mA, and on reconstructions with FBP and each scan method with TC <60 mA was significantly lower than with standard reference (P < 0.05). With the same TC and reconstruction, SDs and nodule identifications of WVS were not significantly different from 64HS (P > 0.05).

CONCLUSION

In term of SD of lung parenchyma and nodule identification, AIDR 3D can achieve more radiation dose reduction than FBP and there is no significant different between WVS and 64HS.

Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study

PURPOSE

To directly compare the capability of three reconstruction methods using, respectively, forward projected model-based iterative reconstruction (FIRST), adaptive iterative dose reduction using three dimensional processing (AIDR 3D) and filter back projection (FBP) for radiation dose reduction and accuracy of computer-aided volumetry (CADv) measurements on chest CT examination in a phantom study.

MATERIALS AND METHODS

An anthropomorphic thoracic phantom with 30 simulated nodules of three density types (100, -630, and -800 HU) and five different diameters was scanned with an area-detector CT at tube currents of 270, 200, 120, 80, 40, 20, and 10mA. Each scanned data set was reconstructed as thin-section CT with three methods, and all simulated nodules were measured with CADv software. For comparison of the capability for CADv at each tube current, Tukey's HSD test was used to compare the percentage of absolute measurement errors for all three reconstruction methods. Absolute percentage measurement errors were then compared by means of Dunett's test for each tube current at 270mA (standard tube current).

RESULTS

Mean absolute measurement errors of AIDR 3D and FIRST methods for each nodule type were significantly lower than those of the FBP method at 20mA and 10mA (p<0.05). In addition, absolute measurement errors of the FBP method at 20mA and 10mA was significantly higher than that at 270mA for all nodule types (p<0.05).

CONCLUSION

The FIRST and AIDR 3D methods are more effective than the FBP method for radiation dose reduction, while yielding better measurement accuracy of CADv for chest CT examination.

Emphysema Quantification Using Ultralow-Dose CT With Iterative Reconstruction and Filtered Back Projection

OBJECTIVE

The purpose of this study was to evaluate agreement between standard-dose CT (SDCT) and ultralow-dose CT (ULDCT) findings with respect to emphysema quantification. ULDCT images were reconstructed with and without iterative reconstruction (IR). Adaptive iterative dose reduction with 3D processing was used for IR.

MATERIALS AND METHODS

Fifty patients who underwent SDCT and ULDCT were included. The tube current for SDCT was 250 mA, and that for ULDCT was 10 mA. SDCT, ULDCT without IR, and ULDCT with IR were used for emphysema quantification. The low-attenuation volume percentage (LAV%) in the lungs at four thresholds (-970, -950, -930, and -910 HU), mean lung attenuation, and total lung volume were computed. Concordance correlation coefficients (CCC) were used to assess the agreement of emphysema quantification between SDCT and ULDCT.

RESULTS

The LAV% CCC values were 0.310-0.789 between SDCT and ULDCT without IR and 0.934-0.966 between SDCT and ULDCT with IR. The agreement of LAV% improved when IR was used for ULDCT. The mean lung attenuation CCC value between SDCT and ULDCT without IR was substantial (0.957), whereas that between SDCT and ULDCT with IR was poor (0.890). The total lung volume CCC values were substantial (0.982 with IR, 0.983 without IR).

CONCLUSION

ULDCT with and without IR can substitute for SDCT in emphysema quantification.

Effect of reconstruction methods and x-ray tube current-time product on nodule detection in an anthropomorphic thorax phantom: A crossed-modality JAFROC observer study

Purpose:

To evaluate nodule detection in an anthropomorphic chest phantom in computed tomography (CT) images reconstructed with adaptive iterative dose reduction 3D (AIDR^3D) and filtered back projection (FBP) over a range of tube current–time product (mAs).

Methods:

Two phantoms were used in this study: (i) an anthropomorphic chest phantom was loaded with spherical simulated nodules of 5, 8, 10, and 12 mm in diameter and +100, −630, and −800 Hounsfield units electron density; this would generate CT images for the observer study; (ii) a whole-body dosimetry verification phantom was used to ultimately estimate effective dose and risk according to the model of the BEIR VII committee. Both phantoms were scanned over a mAs range (10, 20, 30, and 40), while all other acquisition parameters remained constant. Images were reconstructed with both AIDR^3D and FBP. For the observer study, 34 normal cases (no nodules) and 34 abnormal cases (containing 1–3 nodules, mean 1.35 ± 0.54) were chosen. Eleven observers evaluated images from all mAs and reconstruction methods under the free-response paradigm. A crossed-modality jackknife alternative free-response operating characteristic (JAFROC) analysis method was developed for data analysis, averaging data over the two factors influencing nodule detection in this study: mAs and image reconstruction (AIDR^3D or FBP). A Bonferroni correction was applied and the threshold for declaring significance was set at 0.025 to maintain the overall probability of Type I error at α = 0.05. Contrast-to-noise (CNR) was also measured for all nodules and evaluated by a linear least squares analysis.

Results:

For random-reader fixed-case crossed-modality JAFROC analysis, there was no significant difference in nodule detection between AIDR^3D and FBP when data were averaged over mAs [F(1, 10) = 0.08, p = 0.789]. However, when data were averaged over reconstruction methods, a significant difference was seen between multiple pairs of mAs settings [F(3, 30) = 15.96, p < 0.001]. Measurements of effective dose and effective risk showed the expected linear dependence on mAs. Nodule CNR was statistically higher for simulated nodules on images reconstructed with AIDR^3D (p < 0.001).

Conclusions:

No significant difference in nodule detection performance was demonstrated between images reconstructed with FBP and AIDR^3D. mAs was found to influence nodule detection, though further work is required for dose optimization.

CT Imaging of Interstitial Lung Diseases

Until today, computed tomography (CT) is the most important and valuable radiological modality to detect, analyze, and diagnose diffuse interstitial lung diseases (DILD), based on the unsurpassed morphological detail provided by high-resolution CT technique.

In the past decade, there has been a shift from an isolated histopathological diagnosis to a multidisciplinary acquired diagnosis consensus that is nowadays regarded to provide the highest level of diagnostic accuracy in patients with diffuse interstitial lung diseases. The 2002 ATS/ERS statement on classification of idiopathic interstitial pneumonias assigned a central role to high-resolution CT (HRCT) in the diagnostic workup of idiopathic interstitial pneumonias (ATS/ERS consensus classification 2002). The more recent 2013 ERS/ATS statement reinforced that combined clinical data (presentation, exposures, smoking status, associated diseases, lung function, and laboratory findings) and radiological findings are essential for a multidisciplinary diagnosis (Travis et al., Am J Respir Crit Care Med 188(6):733–748, 2013).

The traditional HRCT consisted of discontinuous 1 mm high-resolution axial slices. The primary focus was on visual pattern analysis demanding for the highest possible spatial resolution. Because of the intrinsic high structural contrast of the lung, it has been possible to substantially reduce dose without losing diagnostic information. This development has been supported by new detection and reconstruction techniques. Not only detection of subtle disease and visual comparison of disease stage but also disease classification and quantification nowadays take advantage of continuous volumetric data acquisition provided by multidetector row (MD) CT technique. The following book chapter will focus on acquisition technique with special emphasis on dose and reconstruction, advantages, and new diagnostic options of volumetric MDCT technique for interstitial lung diseases. Based on evidence from the literature, certain diseases will be covered more specifically, but it has to be noted that for the pattern analysis of the various interstitial lung diseases, the plethora of other publications and books is recommended.

Hybrid Type iterative reconstruction method vs. filter back projection method: Capability for radiation dose reduction and perfusion assessment on dynamic first-pass contrast-enhanced perfusion chest area-detector CT

PURPOSE

To directly compare the capability of hybrid-type iterative reconstruction (i.e., adaptive iterative dose reduction using 3D processing: AIDR 3D) and filter back projection (FBP) for radiation dose reduction during dynamic contrast-enhanced (CE-) perfusion area-detector CT (ADCT) for lung and nodule perfusion assessment.

MATERIALS AND METHODS

Thirty-six patients with lung cancers who underwent perfusion ADCT (SD-ADCT) at 120 mA and were enrolled in this study. ADCT data at 80 mA (reduced-dose ADCT: RD-ADCT), 60 mA (low-dose ADCT: LD-ADCT) and 40 mA (very low-dose ADCT: VLD-ADCT) were computationally simulated using SD-ADCT data, and reconstructed with and without AIDR 3D. Image noise and lung and nodule perfusion parameters were evaluated using ROI measurements. To determine the utility of AIDR 3D for dose reduction, image noise was compared between each protocol with and without AIDR 3D by means of the t-test. Correlations and limits of agreement for parameters obtained with SD-ADCT and other protocols were also evaluated.

RESULTS

Image noise of all protocols with AIDR 3D was significantly lower than that of LD-ADCT and VLD-ADCT without AIDR 3D (p<0.05). Significant correlations for image noise between SD-ADCT and all protocols with AIDR 3D (0.45 ≤ r ≤ 0.99, p<0.0001) were equal to or better than that without AIDR 3D (0.28 ≤ r ≤ 0.99, p<0.0001). The limits of agreement for perfusion parameters with AIDR 3D were smaller than those without AIDR 3D for each tube current.

CONCLUSION

AIDR 3D is more effective than FBP for dose reduction of perfusion ADCT while maintaining image quality and reducing measurement errors.

Ultra-Low-Dose CT of the Thorax Using Iterative Reconstruction: Evaluation of Image Quality and Radiation Dose Reduction

OBJECTIVE

The purpose of this study is to assess the image quality and radiation dose reduction of ultra-low-dose CT using sinogram-affirmed iterative reconstruction (SAFIRE).

SUBJECTS AND METHODS

This prospective study enrolled 25 patients who underwent three consecutive unenhanced CT scans including low-dose CT (120 kVp and 30 mAs) and two ultra-low-dose CT protocols (protocol A, 100 kVp and 20 mAs; protocol B, 80 kVp and 30 mAs) with image reconstruction using SAFIRE. The image quality and radiation dose reduction were assessed.

RESULTS

The mean (± SD) effective radiation dose was 1.06 ± 0.11, 0.44 ± 0.05, and 0.31 ± 0.03 mSv for low-dose CT, ultra-low-dose CT protocol A, and ultra-low-dose CT protocol B, respectively. Overall image quality was determined as diagnostic in 100% of low-dose CT scans, 96% of ultra-low-dose CT protocol A scans, and 88% of ultra-low-dose CT protocol B scans. All patients with nondiagnostic quality images had a body mass index (weight in kilograms divided by the square of height in meters) greater than 25. There was no statistically significant difference in detection frequencies of 14 lesion types among the three CT protocols, but pulmonary emphysema was detected in fewer patients (3/25) in ultra-low-dose CT protocol B scans compared with ultra-low-dose CT protocol A scans (5/25) or low-dose CT scans (6/25). We measured the longest dimensions of 33 small solid nodules (3.8-12.4 mm in long diameter) and found no statistically significant difference in the values afforded by the three CT protocols (p = 0.135).

CONCLUSION

Iterative reconstruction allows ultra-low-dose CT and affords acceptable image quality, allowing size measurements of solid pulmonary nodules to be made.

Achievable dose reduction using iterative reconstruction for chest computed tomography: A systematic review

OBJECTIVES

Iterative reconstruction (IR) allows for dose reduction with maintained image quality in CT imaging. In this systematic review the reported effective dose reductions for chest CT and the effects on image quality are investigated.

METHODS

A systematic search in PubMed and EMBASE was performed. Primary outcome was the reported local reference and reduced effective dose and secondary outcome was the image quality with IR. Both non contrast-enhanced and enhanced studies comparing reference dose with reduced dose were included.

RESULTS

24 studies were included. The median number of patients per study was 66 (range 23-200) with in total 1806 patients. The median reported local reference dose of contrast-enhanced chest CT with FBP was 2.6 (range 1.5-21.8) mSv. This decreased to 1.4 (range 0.4-7.3) mSv at reduced dose levels using IR. With non contrast-enhanced chest CT the dose decreased from 3.4 (range 0.7-7.8) mSv to 0.9 (range 0.1-4.5) mSv. Objective mage quality and diagnostic confidence and acceptability remained the same or improved with IR compared to FBP in most studies while data on diagnostic accuracy was limited.

CONCLUSION

Radiation dose can be reduced to less than 2 mSv for contrast-enhanced chest CT and non contrast-enhanced chest CT is possible at a submillisievert dose using IR algorithms.

Low-dose CT screening using hybrid iterative reconstruction: confidence ratings of diagnoses of simulated lesions other than lung cancer

OBJECTIVE

To evaluate the confidence ratings of diagnoses of simulated lesions other than lung cancer on low-dose screening CT with hybrid iterative reconstruction (IR).

METHODS

Simulated lesions (emphysema, mediastinal masses and interstitial pneumonia) in a chest phantom were scanned by a 320-row area detector CT. The scans were performed by 64-row and 160-row helical scans at various dose levels and were reconstructed by filtered back projection (FBP) and IR. Emphysema, honeycombing and reticular opacity were visually scored on a four-point scale by six thoracic radiologists. The ground-glass opacity as a percentage of total lung volume (%GGO), CT value and contrast-to-noise ratio (CNR) of mediastinal masses were calculated. These scores and values were compared between FBP and IR. Wilcoxon's signed-rank test was used (p < 0.05). Interobserver agreements were evaluated by κ statistics.

RESULTS

There were no significant differences in visual assessment. Interobserver agreement was almost perfect. CT values were almost equivalent between FBP and IR, whereas CNR with IR was significantly higher than that with FBP. %GGO significantly increased at low-dose levels with FBP; however, IR suppressed the elevation.

CONCLUSION

The confidence ratings of diagnoses of simulated lesions other than lung cancer on low-dose CT screening were not degraded with hybrid IR compared with FBP.

ADVANCES IN KNOWLEDGE

Hybrid IR did not degrade the confidence ratings of diagnoses on visual assessment and differential diagnoses based on CT value of mediastinal masses, and it showed the advantage of higher GGO conspicuity at low-dose level. Radiologists can analyse images of hybrid IR alone on low-dose CT screening for lung cancer.

T2 mapping of CT remodelling patterns in interstitial lung disease

OBJECTIVES

To evaluate lung T2 mapping for quantitative characterization and differentiation of ground-glass opacity (GGO), reticulation (RE) and honeycombing (HC) in usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP).

METHODS

Twelve patients with stable UIP or NSIP underwent thin-section multislice CT and 1.5-T MRI of the lung. A total of 188 regions were classified at CT into normal (n = 29) and pathological areas, including GGO (n = 48), RE (n = 60) and HC (n = 51) predominant lesions. Entire lung T2 maps based on multi-echo single shot TSE sequence (TE: 20, 40, 79, 140, 179 ms) were generated from each subject with breath-holds at end-expiration and ECG-triggering.

RESULTS

The median T2 relaxation of GGO was 67 ms (range 60-72 ms). RE predominant lesions had a median relaxation of 74 ms (range 69-79 ms), while for HC pattern this was 79 ms (range 74-89 ms). The median T2 relaxation for normal lung areas was 41 ms (ranged 38-49 ms), and showed significant difference to pathological areas (p < 0.001). A statistical difference was found between the T2 relaxation of GGO, RE and HC (p < 0.05).

CONCLUSIONS

The proposed method provides quantitative information for pattern differentiation, potentially allowing for monitoring of progression and response to treatment, in interstitial lung disease.

KEY POINTS

• Multi-echo single shot TSE sequence allows for entire lung T2 mapping. • Lung remodelling patterns in ILD show different T2 relaxation. • Quantitative T2 mapping may provide information for monitoring of ILD.

Quantitative lung perfused blood volume imaging on dual-energy CT: capability for quantitative assessment of disease severity in patients with acute pulmonary thromboembolism

BACKGROUND

Regional iodine distribution assessment on dual-energy computed tomography (DECT) has been suggested as useful for management of acute pulmonary thromboembolism (APTE) patients. However, no reported studies have made a direct comparison between quantitatively assessed DECT and right-to-left ventricular (RV/LV) ratio on CT for differentiation of right heart dysfunction (RHD) from non-right heart dysfunction (NRHD) in APTE patients.

PURPOSE

To determine the capability of DECT for differentiation of RHD from NRHD in APTE patients.

MATERIAL AND METHODS

Thirteen APTE patients underwent DECT and echocardiography at onset of APTE. Patients were divided into RHD (n = 7) and NRHD (n = 6) groups based on echocardiography. A normalized lung perfused blood volume map was generated, and two kinds of overall perfusion (OP) index were determined, one placed over each lung field (OP index A) and as the average from six regions of interest (ROIs) placed over each lung field (OP index B). The heterogeneity index was also determined as the standard deviation for the six ROIs. RV/LV ratio evaluations were also performed. To assess differences between the two groups, each index was statistically compared with the Mann-Whitney U test. The receiver-operating curve-based positive test was then performed to determine the feasible threshold value for dividing patients into the two groups. Finally, differentiation capabilities of the indexes were compared using McNemar's test.

RESULTS

Significant differences between the two groups were found for both OP indexes and RV/LV ratio (P < 0.05). For each of the feasible threshold values, accuracy of each OP index with and without RV/LV ratio was better than that of the RV/LV ratio.

CONCLUSION

Quantitative DECT has good potential for differentiation of APTE patients with and without right heart dysfunction.

Iterative reconstruction for quantitative computed tomography analysis of emphysema: consistent results using different tube currents

Purpose

To assess the advantages of iterative reconstruction for quantitative computed tomography (CT) analysis of pulmonary emphysema.

Materials and methods

Twenty-two patients with pulmonary emphysema underwent chest CT imaging using identical scanners with three different tube currents: 240, 120, and 60 mA. Scan data were converted to CT images using Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) and a conventional filtered-back projection mode. Thus, six scans with and without AIDR3D were generated per patient. All other scanning and reconstruction settings were fixed. The percent low attenuation area (LAA%; < −950 Hounsfield units) and the lung density 15th percentile were automatically measured using a commercial workstation. Comparisons of LAA% and 15th percentile results between scans with and without using AIDR3D were made by Wilcoxon signed-rank tests. Associations between body weight and measurement errors among these scans were evaluated by Spearman rank correlation analysis.

Results

Overall, scan series without AIDR3D had higher LAA% and lower 15th percentile values than those with AIDR3D at each tube current (P<0.0001). For scan series without AIDR3D, lower tube currents resulted in higher LAA% values and lower 15th percentiles. The extent of emphysema was significantly different between each pair among scans when not using AIDR3D (LAA%, P<0.0001; 15th percentile, P<0.01), but was not significantly different between each pair among scans when using AIDR3D. On scans without using AIDR3D, measurement errors between different tube current settings were significantly correlated with patients’ body weights (P<0.05), whereas these errors between scans when using AIDR3D were insignificantly or minimally correlated with body weight.

Conclusion

The extent of emphysema was more consistent across different tube currents when CT scans were converted to CT images using AIDR3D than using a conventional filtered-back projection method.