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

Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study

OBJECTIVE

The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CAD v ) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR), mode-based iterative reconstruction (MBIR), and deep learning reconstruction (DLR) at a phantom study.

METHODS

A commercially available anthropomorphic abdominal phantom was scanned five times with a 320-detector row CT at 600 mA, 400 mA, 200 mA, and 100 mA and reconstructed by four methods. Signal-to-noise ratios (SNRs) of all lesions within the arterial and portal-venous phase inserts were calculated, and SNR of the lesion phantom was compared with that of all reconstruction methods by means of Tukey's honestly significant difference (HSD) test. Then, tumor volume ( V ) of each nodule was automatically measured using commercially available CAD v software. To compare dose reduction capability for each reconstruction method at both phases, mean differences between measured V and standard references were compared by Tukey's honestly significant difference test among the four different reconstruction methods on CT obtained at each of the four tube currents.

RESULTS

With each of the tube currents, SNRs for MBIR and DLR were significantly higher than those for FBP and hybrid-type IR ( p < 0.05). At the arterial phase, the mean difference in V for the CT protocol obtained at 600 or 100 mA and reconstructed with DLR was significantly smaller than that for others ( p < 0.05). At the portal-venous phase, the mean differences in V for the CT protocol obtained at 100 mA and reconstructed with hybrid-type IR, MBIR, and DLR were significantly smaller than that for FBP ( p < 0.05).

CONCLUSIONS

Findings of our phantom study show that reconstruction method had influence on CAD v merits for abdominal CT with not only standard but also reduced dose examinations and that DLR can potentially yield better image quality and CAD v measurements than FBP, hybrid-type IR, or MBIR in this setting.

Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction

RATIONALE AND OBJECTIVE

To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions.

MATERIALS AND METHODS

This was a prospective study with patient consent and included 56 patients with suspected pulmonary nodules. Patients were examined by both standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with adaptive statistical iterative reconstruction-V 40% (ASIR-V40%) (group A), while ULDCT images were reconstructed using ASIR-V40% (group B) and high-strength DLIR (DLIR-H) (group C). The three image sets were analyzed using a commercial computer aided diagnosis (CAD) software. Parameters such as nodule length, width, density, volume, risk, and classification were measured. The CAD quantitative data of different nodule types (solid, calcified, and subsolid nodules) and nodule image quality scores evaluated by two physicians on a 5-point scale were compared.

RESULT

The radiation dose in ULDCT was 0.25 ± 0.08mSv, 7.2% that of the 3.48 ± 1.08mSv in SDCT (P < 0.001). 104 pulmonary nodules were detected (51/53 solid, 26/24 calcified and 27/27 subsolid in Groups A and (B&C), respectively). Group B had lower density for solid, calcified nodules, and lower volume and risk for subsolid nodules than Group A, while Group C had lower density for calcified nodules (P < 0.05), There were no significant differences in other parameters among the three groups (P > 0.05). Group A and C had similar image quality for nodules and were higher than Group B (P < 0.05).

CONCLUSION

DLIR-H significantly improves image quality than ASIR-V40% and maintains similar nodule detection and characterization with CAD in ULDCT compared to SDCT.

Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study

PURPOSE

This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR).

METHODS

This phantom study assessed the object-specific spatial resolution (task-based transfer function [TTF]), noise characteristics (noise power spectrum [NPS]), and low-contrast detectability (low-contrast object-specific contrast-to-noise ratio [CNRLO]) at three different output doses (standard: 10 mGy; low: 3.9 mGy; ultralow: 2.0 mGy). The processing strength of DLIPFBP with A1, A4, and A9 was compared with those of FBP, MBIR, and DLR.

RESULT

The standard dose with high-contrast TTFs of DLIPFBP exceeded that of FBP. Low-contrast TTFs were comparable to or lower than that of FBP. The NPS peak frequency (fP) of DLIPFBP shifts to low spatial frequencies of up to 8.6% at ultralow doses compared to the standard FBP dose. MBIR shifted the most fP compared to FBP-a marked shift of up to 49%. DLIPFBP showed a CNRLO equal to or greater than that of DLR in standard or low doses. In contrast, the CNRLO of the DLIPFBP was equal to or lower than that of the DLR in ultralow doses.

CONCLUSION

DLIPFBP reduced image noise while maintaining a resolution similar to commercially available MBIR and DLR. The slight spatial frequency shift of fP in DLIPFBP contributed to the noise texture degradation suppression. The NPS suppression in the low spatial frequency range effectively improved the low-contrast detectability.

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.

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

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.

Can optimized model-based iterative reconstruction improve the contrast of liver lesions in CT?

BACKGROUND

Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability.

PURPOSE

To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection.

MATERIAL AND METHODS

Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion.

RESULTS

A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRST_MILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRST_MILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRST_STR showing the closest results (P < 0.001).

CONCLUSION

Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.

Evaluation and Management of Indeterminate Pulmonary Nodules on Chest Computed Tomography in Asymptomatic Subjects: The Principles of Nodule Guidelines

With the rapidly increasing number of chest computed tomography (CT) examinations, the question of how to manage lung nodules found in asymptomatic patients has become increasingly important. Several nodule management guidelines have been developed that can be applied to incidentally found lung nodules (the Fleischner Society guideline), nodules found during lung cancer screening (International Early Lung Cancer Action Program protocol [I-ELCAP] and Lung CT Screening Reporting and Data System [Lung-RADS]), or both (American College of Chest Physicians guideline [ACCP], British Thoracic Society guideline [BTS], and National Comprehensive Cancer Network guideline [NCCN]). As the radiologic nodule type (solid, part-solid, and pure ground glass) and size are significant predictors of a nodule's nature, most guidelines categorize nodules in terms of these characteristics. Various methods exist for measuring the size of nodules, and the method recommended in each guideline should be followed. The diameter can be manually measured as a single maximal diameter or as an average of two-dimensional diameters, and software can be used to obtain volumetric measurements. It is important to properly evaluate and measure nodules and familiarize ourselves with the relevant guidelines to appropriately utilize medical resources and minimize unnecessary radiation exposure to patients.

Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease

OBJECTIVES

To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR).

METHODS

Seventy ILD patients were prospectively enrolled and underwent HRCT (120 kVp, automatic tube current) and LDCT (120 kVp, 30 mAs) scans. HRCT images were reconstructed with hybrid-IR (Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D], standard-setting); LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine [AiCE], lung/bone, mild/standard/strong setting). Image noise, streak artifact, overall image quality, and visualization of normal and abnormal features of ILD were evaluated.

RESULTS

The mean radiation dose of LDCT was 38% of HRCT. Objective image noise of reconstructed LDCT images was 33.6 to 111.3% of HRCT, and signal-to-noise ratio (SNR) was 0.9 to 3.1 times of the latter (p < 0.001). LDCT-AiCE was not significantly different from or even better than HRCT in overall image quality and visualization of normal lung structures. LDCT-AiCE (lung, mild/standard/strong) showed progressively better recognition of ground glass opacity than HRCT-AIDR3D (p < 0.05, p < 0.01, p < 0.001), and LDCT-AiCE (lung, mild/standard/strong; bone, mild) was superior to HRCT-AIDR3D in visualization of architectural distortion (p < 0.01, p < 0.01, p < 0.01; p < 0.05). LDCT-AiCE (bone, strong) was better than HRCT-AIDR3D in the assessment of bronchiectasis and/or bronchiolectasis (p < 0.05). LDCT-AiCE (bone, mild/standard/strong) was significantly better at the visualization of honeycombing than HRCT-AIDR3D (p < 0.05, p < 0.05, p < 0.01).

CONCLUSION

Deep learning reconstruction could effectively reduce radiation dose and keep image quality in ILD patients compared to HRCT with hybrid-IR.

KEY POINTS

• Deep learning reconstruction was a novel image reconstruction algorithm based on deep convolutional neural networks. It was applied in chest CT studies and received auspicious results. • HRCT plays an essential role in the whole process of diagnosis, treatment efficacy evaluation, and follow-ups for interstitial lung disease patients. However, cumulative radiation exposure could increase the risks of cancer. • Deep learning reconstruction method could effectively reduce the radiation dose and keep the image quality compared with HRCT reconstructed with hybrid iterative reconstruction in patients with interstitial lung disease.

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.

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study

OBJECTIVES

The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting.

METHODS

Artificial ground-glass nodules (6 mm and 10 mm diameters, -660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements.

RESULTS

DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement.

CONCLUSIONS

DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT.

ADVANCES IN KNOWLEDGE

DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

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.

CT Volumetry of Convoluted Objects-A Simple Method Using Volume Averaging

Accurate measurement of object volumes using computed tomography is often important but can be challenging, especially for finely convoluted objects with severe marginal blurring from volume averaging. We aimed to test the accuracy of a simple method for volumetry by constructing, scanning and analyzing a phantom object with these characteristics which consisted of a cluster of small lucite beads embedded in petroleum jelly. Our method involves drawing simple regions of interest containing the entirety of the object and a portion of the surrounding material and using its density, along with the densities of pure lucite and petroleum jelly and the slice thickness to calculate the volume of the object in each slice. Comparison of our results with the object’s true volume showed the technique to be highly accurate, irrespective of slice thickness, image noise, reconstruction planes, spatial resolution and variations in regions of interest. We conclude that the method can be easily used for accurate volumetry in clinical and research scans without the need for specialized volumetry computer programs.

The future of CT: deep learning reconstruction

There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstruction algorithm to be introduced, which harnesses advances in artificial intelligence (AI) and affordable supercomputer technology to achieve the previously elusive triad of high image quality, low radiation dose, and fast reconstruction speeds. The dose reductions achieved with DLR are redefining ultra-low-dose into the realm of plain radiographs whilst maintaining image quality. This review aims to demonstrate the advantages of DLR over other reconstruction methods in terms of dose reduction and image quality in addition to being able to tailor protocols to specific clinical situations. DLR is the future of CT technology and should be considered when procuring new scanners.

Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases

PURPOSE

To evaluate the capability ML-based CT texture analysis for improving interobserver agreement and accuracy of radiological finding assessment in patients with COPD, interstitial lung diseases or infectious diseases.

MATERIALS AND METHODS

Training cases (n = 28), validation cases (n = 17) and test cases (n = 89) who underwent thin-section CT at a 320-detector row CT with wide volume scan and two 64-detector row CTs with helical scan were enrolled in this study. From 89 CT data, a total of 350 computationally selected ROI including normal lung, emphysema, nodular lesion, ground-glass opacity, reticulation and honeycomb were evaluated by three radiologists as well as by the software. Inter-observer agreements between consensus reading with and without using the software or software alone and standard references determined by consensus of pulmonologists and chest radiologists were determined using κ statistics. Overall distinguishing accuracies were compared among all methods by McNemar's test.

RESULTS

Agreements for consensus readings obtained with and without the software or the software alone with standard references were determined as significant and substantial or excellent (with the software: κ = 0.91, p < 0.0001; without the software: κ = 0.81, p < 0.0001; the software alone: κ = 0.79, p < 0.0001). Overall differentiation accuracy of consensus reading using the software (94.9 [332/350] %) was significantly higher than that of consensus reading without using the software (84.3 [295/350] %, p < 0.0001) and the software alone (82.3 [288/350] %, p < 0.0001).

CONCLUSION

ML-based CT texture analysis software has potential for improving interobserver agreement and accuracy for radiological finding assessments in patients with COPD, interstitial lung diseases or infectious diseases.

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.

Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

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.

Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements Using Radiomics

Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.

Low-Radiation-Dose Stress Myocardial Perfusion Measurement Using First-Pass Analysis Dynamic Computed Tomography: A Preliminary Investigation in a Swine Model

OBJECTIVES

The aim of this study was to assess the feasibility of a prospective first-pass analysis (FPA) dynamic computed tomography (CT) perfusion technique for accurate low-radiation-dose global stress perfusion measurement.

MATERIALS AND METHODS

The prospective FPA technique was evaluated in 10 swine (42 ± 12 kg) by direct comparison to a previously validated retrospective FPA technique. Of the 10 swine, 3 had intermediate stenoses with fractional flow reserve severities of 0.70 to 0.90. In each swine, contrast and saline were injected peripherally followed by dynamic volume scanning with a 320-slice CT scanner. Specifically, for the reference standard retrospective FPA technique, volume scans were acquired continuously at 100 kVp and 200 mA over 15 to 20 seconds, followed by systematic selection of only 2 volume scans for global perfusion measurement. For the prospective FPA technique, only 2 volume scans were acquired at 100 kVp and 50 mA for global perfusion measurement. All prospective global stress perfusion measurements were then compared with the corresponding reference standard retrospective global stress perfusion measurements through regression analysis. The CTDIvol and size-specific dose estimate of the prospective FPA technique were also determined.

RESULTS

All prospective global stress perfusion measurements (PPRO) at 50 mA were in good agreement with the reference standard retrospective global stress perfusion measurements (PREF) at 200 mA (PPRO = 1.07 PREF -0.09, r = 0.94; root-mean-square error = 0.30 mL/min per gram). The CTDIvol and size-specific dose estimate of the prospective FPA technique were 2.3 and 3.7 mGy, respectively.

CONCLUSIONS

Accurate low-radiation-dose global stress perfusion measurement is feasible using a prospective FPA dynamic CT perfusion technique.

The Application of a New Model-Based Iterative Reconstruction in Low-Dose Upper Abdominal CT

RATIONALE AND OBJECTIVES

To compare upper abdominal computed tomography (CT) image quality of new model-based iterative reconstruction (MBIR) with low-contrast resolution preference (MBIR_NR40), conventional MBIR (MBIR_c), and adaptive statistical iterative reconstruction (ASIR) at low dose with ASIR at routine-dose.

MATERIALS AND METHODS

Study included phantom and 60 patients who had initial and follow-up CT scans. For patients, the delay phase was acquired at routine-dose (noise index = 10 HU) for the initial scan and low dose (noise index = 20 HU) for the follow-up. The low-dose CT was reconstructed with 40% and 60% ASIR, MBIR_c, and MBIR_NR40, while routine-dose CT was reconstructed with 40% ASIR. CT value and noise measurements of the subcutaneous fat, back muscle, liver, and spleen parenchyma were compared using one-way ANOVA. Two radiologists used semiquantitative 7-scale (-3 to +3) to rate image quality and artifacts.

RESULTS

The phantom study revealed superior low-contrast resolution with MBIR_NR40. For patient scans, the CT dose index for the low-dose CT was 3.00 ± 1.32 mGy, 75% lower than the 11.90 ± 4.75 mGy for the routine-dose CT. Image noise for the low-dose MBIR_NR40 images was significantly lower than the low-dose MBIR_c and ASIR images, and routine-dose ASIR images (p < 0.05). Subjective ratings showed higher image quality for low-dose MBIR_NR40, with lower noise, better low-contrast resolution for abdominal structures, and finer lesion contours than those of low-dose MBIR_c and ASIR images, and routine-dose ASIR images (p < 0.05).

CONCLUSION

MBIR_NR40 with low-contrast resolution preference provides significantly lower noise and better image quality than MBIR_c and ASIR in low-dose abdominal CT; significantly better objective and subjective image quality than the routine-dose ASIR with 75% dose reduction.

The usefulness of full-iterative reconstruction algorithm for the visualization of cystic artery on CT angiography

PURPOSE

To evaluate the potential of full-iterative reconstruction (IR) for improving image quality of the cystic artery on CT angiography and to assess observer performance.

METHODS

Thirty patients who underwent both liver dynamic CT and conventional angiography were included in this retrospective study. All CT data were reconstructed through filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR3D), and forward-projected, model-based, iterative reconstruction solution (FIRST), respectively. In objective study, we analyzed mean ΔCT numbers (the difference between the HU peak of the vessel and the background) and full-width at tenth-maximum (FWTM) of three parts of the cystic artery by profile curve method comparing the three reconstructions. Subjectively, visualization was evaluated using a four-point scale performed by two blinded observers. ANOVA was used for statistical analysis.

RESULTS

In all parts of the cystic artery, the mean ΔCT number of FIRST was shown to be significantly better than that of FBP and AIDR3D (p < 0.05). FWTM in FIRST was the smallest in all of the vessels. The mean visualization score was significantly better with FIRST than with other CT reconstructions (p < 0.05).

CONCLUSIONS

The FIRST algorithm led to improved CTA visualization of the cystic artery.

Ultra-high-resolution subtraction CT angiography in the follow-up of treated intracranial aneurysms

In subtraction CT angiography (CTA), a non-contrast CT acquisition is subtracted from a contrast-enhanced CTA acquisition. Subtraction CTA can be applied in the detection, classification, and follow-up of intracranial aneurysms and is advantageous over conventional angiography because of its non-invasive nature, shorter examination time, and lower costs. Recently, an ultra-high-resolution CT scanner has been introduced in clinical practice offering an in-plane spatial resolution of up to 0.234 mm, approaching the resolution as seen during conventional invasive digital subtraction angiography (DSA). The twofold increase in spatial resolution as compared to a conventional CT scanner could improve the evaluation of small vascular structures and, coupled with dedicated post-processing techniques, further reduce metal artifacts. Technical considerations using a state-of-the-art high-resolution subtraction CTA protocol are discussed for application in the follow-up of surgical and endovascular treated intracranial aneurysms.

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.

Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

Association of Focal Radiation Dose Adjusted on Cross Sections with Subsolid Nodule Visibility and Quantification on Computed Tomography Images Using AIDR 3D: Comparison Among Scanning at 84, 42, and 7 mAs

RATIONALE AND OBJECTIVES

The objectives of this study were to compare the visibility and quantification of subsolid nodules (SSNs) on computed tomography (CT) using adaptive iterative dose reduction using three-dimensional processing between 7 and 42 mAs and to assess the association of size-specific dose estimate (SSDE) with relative measured value change between 7 and 84 mAs (RMVC_7-84) and relative measured value change between 42 and 84 mAs (RMVC_42-84).

MATERIALS AND METHODS

As a Japanese multicenter research project (Area-detector Computed Tomography for the Investigation of Thoracic Diseases [ACTIve] study), 50 subjects underwent chest CT with 120 kV, 0.35 second per location and three tube currents: 240 mA (84 mAs), 120 mA (42 mAs), and 20 mA (7 mAs). Axial CT images were reconstructed using adaptive iterative dose reduction using three-dimensional processing. SSN visibility was assessed with three grades (1, obscure, to 3, definitely visible) using CT at 84 mAs as reference standard and compared between 7 and 42 mAs using t test. Dimension, mean CT density, and particular SSDE to the nodular center of 71 SSNs and volume of 58 SSNs (diameter >5 mm) were measured. Measured values (MVs) were compared using Wilcoxon signed-rank tests among CTs at three doses. Pearson correlation analyses were performed to assess the association of SSDE with RMVC_7-84: 100 × (MV at 7 mAs - MV at 84 mAs)/MV at 84 mAs and RMVC_42-84.

RESULTS

SSN visibilities were similar between 7 and 42 mAs (2.76 ± 0.45 vs 2.78 ± 0.40) (P = .67). For larger SSNs (>8 mm), MVs were similar among CTs at three doses (P > .05). For smaller SSNs (<8 mm), dimensions and volumes on CT at 7 mAs were larger and the mean CT density was smaller than 42 and 84 mAs, and SSDE had mild negative correlations with RMVC_7-84 (P < .05).

CONCLUSIONS

Comparable quantification was demonstrated irrespective of doses for larger SSNs. For smaller SSNs, nodular exaggerating effect associated with decreased SSDE on CT at 7 mAs compared to 84 mAs could result in comparable visibilities to CT at 42 mAs.

Tradeoff between noise reduction and inartificial visualization in a model-based iterative reconstruction algorithm on coronary computed tomography angiography

We aimed to evaluate the image quality performance of coronary CT angiography (CTA) under the different settings of forward-projected model-based iterative reconstruction solutions (FIRST).Thirty patients undergoing coronary CTA were included. Each image was reconstructed using filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR-3D), and 2 model-based iterative reconstructions including FIRST-body and FIRST-cardiac sharp (CS). CT number and noise were measured in the coronary vessels and plaque. Subjective image-quality scores were obtained for noise and structure visibility.In the objective image analysis, FIRST-body produced the significantly highest contrast-to-noise ratio. Regarding subjective image quality, FIRST-CS had the highest score for structure visibility, although the image noise score was inferior to that of FIRST-body.In conclusion, FIRST provides significant improvements in objective and subjective image quality compared with FBP and AIDR-3D. FIRST-body effectively reduces image noise, but the structure visibility with FIRST-CS was superior to FIRST-body.

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.

Dynamic CT angiography for the diagnosis of patients with thoracic outlet syndrome: Correlation with patient symptoms

INTRODUCTION

Vasculo-nervous structures serving the upper limbs may be compressed as they pass through three areas: the inter-scalene triangle (IST), the costo-clavicular space (CCS) and the retropectoralis minor space (RMS). The diagnosis of thoracic outlet syndrome (TOS) is essentially clinical, but requires imaging to specify the site of compression, its grade and the existence of predisposing anatomical factors, in order to guide the treatment and eliminate the main differential diagnoses.

MATERIAL AND METHODS

Images from 141 patients who underwent dynamic CT angiography of the thoracic outlets from June 2008 to January 2015 were analyzed retrospectively. Patients had unilateral or bilateral vascular, neurological, mixed or atypical symptoms. We studied the degree of stenosis of the subclavian artery with the following grading system: 1 (0-<25%), 2 (25-<50%), 3 (50-<75%), 4 (75-100%). The site of stenosis and the presence of underlying anatomical predisposing factors were also taken in account.

RESULTS

A total of 221 thoracic outlets were analyzed. Symptoms were neurological, mixed, vascular and atypical in 30%, 28%, 13% and 12%, respectively. Among patients with bilateral acquisitions, 38 outlets were asymptomatic; 40% of symptomatic outlets and only 5% of asymptomatic ones had grade 3 or 4 stenosis. 63% of the stenosis were in the CCS and 37% in the IST; 21% had a predisposing anatomical factor most often a costo-clavicular anomaly, associated with significant stenosis in 50% of cases.

CONCLUSION

Vascular stenosis of more than 50% on dynamic CT angiography is strongly associated with TOS. Predisposing factors were present in 21% of cases, causing significant vascular stenosis in half, underscoring the need for functional evaluation.

Generation of realistic virtual nodules based on three-dimensional spatial resolution in lung computed tomography: A pilot phantom study

PURPOSE

The aim of this feasibility study using phantoms was to propose a novel method for obtaining computer-generated realistic virtual nodules in lung computed tomography (CT).

METHODS

In the proposed methodology, pulmonary nodule images obtained with a CT scanner are deconvolved with the point spread function (PSF) in the scan plane and slice sensitivity profile (SSP) measured for the scanner; the resultant images are referred to as nodule-like object functions. Next, by convolving the nodule-like object function with the PSF and SSP of another (target) scanner, the virtual nodule can be generated so that it has the characteristics of the spatial resolution of the target scanner. To validate the methodology, the authors applied physical nodules of 5-, 7- and 10-mm-diameter (uniform spheres) included in a commercial CT test phantom. The nodule-like object functions were calculated from the sphere images obtained with two scanners (Scanner A and Scanner B); these functions were referred to as nodule-like object functions A and B, respectively. From these, virtual nodules were generated based on the spatial resolution of another scanner (Scanner C). By investigating the agreement of the virtual nodules generated from the nodule-like object functions A and B, the equivalence of the nodule-like object functions obtained from different scanners could be assessed. In addition, these virtual nodules were compared with the real (true) sphere images obtained with Scanner C. As a practical validation, five types of laboratory-made physical nodules with various complicated shapes and heterogeneous densities, similar to real lesions, were used. The nodule-like object functions were calculated from the images of these laboratory-made nodules obtained with Scanner A. From them, virtual nodules were generated based on the spatial resolution of Scanner C and compared with the real images of laboratory-made nodules obtained with Scanner C.

RESULTS

Good agreement of the virtual nodules generated from the nodule-like object functions A and B of the phantom spheres was found, suggesting the validity of the nodule-like object functions. The virtual nodules generated from the nodule-like object function A of the phantom spheres were similar to the real images obtained with Scanner C; the root mean square errors (RMSEs) between them were 10.8, 11.1, and 12.5 Hounsfield units (HU) for 5-, 7-, and 10-mm-diameter spheres, respectively. The equivalent results (RMSEs) using the nodule-like object function B were 15.9, 16.8, and 16.5 HU, respectively. These RMSEs were small considering the high contrast between the sphere density and background density (approximately 674 HU). The virtual nodules generated from the nodule-like object functions of the five laboratory-made nodules were similar to the real images obtained with Scanner C; the RMSEs between them ranged from 6.2 to 8.6 HU in five cases.

CONCLUSIONS

The nodule-like object functions calculated from real nodule images would be effective to generate realistic virtual nodules. The proposed method would be feasible for generating virtual nodules that have the characteristics of the spatial resolution of the CT system used in each institution, allowing for site-specific nodule generation.