Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. AJR Am J Roentgenol. 2010;194:191-199. [PMID: 20028923 DOI: 10.2214/ajr.09.2953]

Deep learning and iterative image reconstruction for head CT: Impact on image quality and radiation dose reduction-Comparative study

Background and purpose: This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image quality and reduce radiation dose compared to the established standard of Adaptive Statistical Iterative Reconstruction-V (ASIR-V), in unenhanced head computed tomography (CT). Materials and methods: A retrospective analysis of 163 consecutive unenhanced head CTs was conducted. Image quality assessment was computed on the objective parameters of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), derived from 5 regions of interest (ROI). The evaluation of DLIR dose reduction abilities was based on the analysis of the PACS derived parameters of dose length product and computed tomography dose index volume (CTDIvol). Results: Following the application of rigorous criteria, the study comprised 35 patients. Significant image quality improvement was achieved with the implementation of DLIR, as evidenced by up to a 145% and 160% increase in SNR in supra- and infratentorial regions, respectively. CNR measurements further confirmed the superiority of DLIR over ASIR-V, with an increase of 171.5% in the supratentorial region and a 59.3% increase in the infratentorial region. Despite the signal improvement and noise reduction DLIR facilitated radiation dose reduction of up to 44% in CTDIvol. Conclusion: Implementation of DLIR in head CT scans enables significant image quality improvement and dose reduction abilities compared to standard ASIR-V. However, the dose reduction feature was proven insufficient to counteract the lack of gantry angulation in wide-detector scanners.

Establishing the effect of computed tomography reconstruction kernels on the measure of bone mineral density in opportunistic osteoporosis screening

Opportunistic computed tomography (CT) scans, which can assess relevant bones of interest, offer a potential solution for identifying osteoporotic individuals. However, it has been well documented that image protocol parameters, such as reconstruction kernel, impact the quantitative analysis of volumetric bone mineral density (vBMD) from CT scans. The purpose of this study was to investigate the impact that CT reconstruction kernels have on quantitative results for vBMD from clinical CT scans using phantom and internal calibration. 45 clinical CT scans were reconstructed using the standard kernel and seven alternative kernels: soft, chest, detail, edge, bone, bone plus and lung [GE HealthCare]. Two methods of image calibration, internal and phantom, were used to calibrate the scans. The total hip and fourth lumbar vertebra (L4) were extracted from the scans via deep learning segmentation. Integral vBMD was calculated based on both calibration techniques from CT scans reconstructed with the eight kernels. Linear regression and Bland-Altman analyses were used to determine the coefficient of determination [Formula: see text] and to quantify the agreement between the different kernels. Differences between the reconstruction kernels were determined using paired t tests, and mean differences from the standard were computed. Using internal calibration, the smoothest kernel (soft) yielded a mean difference of -0.95 mg/cc (-0.33%) compared to the reference standard at the L4 vertebra and 2.07 mg/cc (0.51%) at the left femur. The sharpest kernel (lung) yielded a mean difference of 25.36 mg/cc (9.63%) at the L4 vertebra and -25.10 mg/cc (-5.98%) at the left femur. Alternatively, using phantom calibration soft yielded higher mean differences than internal calibration at both locations, with mean differences of 1.21 mg/cc (0.42%) at the L4 vertebra and 2.53 mg/cc (0.65%) at the left femur. The most error-prone results stemmed from the use of the lung kernel, as this kernel displayed a mean difference of -21.90 mg/cc (-7.38%) and -17.24 mg/cc (-4.34%) at the L4 vertebra and femur, respectively. These results indicate when performing opportunistic CT analysis, errors due to interchanging smoothing kernels soft, chest and detail are negligible, but that interchanging between sharpening kernels (lung, bone, bone plus, edge) results in large errors that can significantly impact vBMD measures for osteoporosis screening and diagnosis.

Direct comparison of photon counting-CT and conventional CT in image quality of lung nodules: A systematic review and meta-analysis

PURPOSE

To evaluate and compare lung nodules' image quality and radiation dose exposure using photon-counting computed tomography (PC-CT) and conventional energy-integrating detector computed tomography (EID-CT).

METHODS

Protocol pre-registration was performed a priori at (https://osf.io/krj5y/). We searched PubMed, Web of Science, Embase, and Cochrane Library for studies until April 10, 2024. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and QUADAS-C. The imaging modalities were compared with Likert scores of lung nodules and radiation dose exposure (measured in mGy and mS). Certainty of evidence was evaluated using Grading of Recommendations, Assessment, Development, and Evaluations (GRADE).

RESULTS

Thirteen studies were included with 718 patients and 362 lung nodules. PC-CT had a significantly higher image quality score of + 0.45 (CI = 0.12 to 0.79) than the EID-CT. Furthermore, 54.0 % (CI = 21.2 % to 86.8 %) of nodules were qualitatively identified as having better image quality in PC-CT than in EID-CT, while 1.9 % (CI = 0 % to 4.9 %) had lower image quality. In terms of radiation dose exposure, PC-CT showed a 30.4 % (CI = 19.1 % to 41.7 %) reduction in radiation dose exposure compared to EID-CT.

CONCLUSION

The as low as reasonably achievable (ALARA) principle emphasizes minimizing ionizing radiation exposure whenever possible. PC-CT has become an up-and-coming imaging method for chest, providing enhanced spatial resolution and less radiation exposure. Integrating PC-CT into daily medical practice and lung cancer screening may enhance the visibility of lung nodules and improve diagnostic accuracy.

Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?

The WHO grading of pancreatic neuroendocrine neoplasms (PanNENs) is essential in patient management and an independent prognostic factor for patient survival. Radiomics features from CE-CT images hold promise for the outcome and tumor grade prediction. However, variations in reconstruction parameters can impact the predictive value of radiomics. 127 patients with histopathologically confirmed PanNENs underwent CT scans with filtered back projection (B20f) and iterative (I26f) reconstruction kernels. 3190 radiomic features were extracted from tumors and pancreatic volumes. Wilcoxon paired tests assessed the impact of reconstruction kernels and ComBat harmonization efficiency. SVM models were employed to predict tumor grade using the entire set of radiomics features or only those identified as harmonizable. The models' performance was assessed on an independent dataset of 36 patients. Significant differences, after correction for multiple testing, were observed in 69% of features in the pancreatic volume and 51% in the tumor volume with B20f and I26f kernels. SVM models demonstrated accuracy ranging from 0.67 (95%CI: 0.50-0.81) to 0.83 (95%CI: 0.69-0.94) in distinguishing grade 1 cases from higher grades. Reconstruction kernels alter radiomics features and iterative kernel models trended towards higher performance. ComBat harmonization mitigates kernel impacts but addressing this effect is crucial in studies involving data from different kernels.

Coronary atherosclerotic plaque characterization with silicon-based photon-counting computed tomography (CT): A simulation-based feasibility study

BACKGROUND

Recent photon-counting computed tomography (PCCT) development brings great opportunities for plaque characterization with much-improved spatial resolution and spectral imaging capability. While existing coronary plaque PCCT imaging results are based on CZT- or CdTe-materials detectors, deep-silicon photon-counting detectors offer unique performance characteristics and promise distinct imaging capabilities.

PURPOSE

This study aims to numerically investigate the feasibility of characterizing plaques with a deep-silicon PCCT scanner and to demonstrate its potential performance advantages over traditional CT scanners using energy-integrating detectors (EID).

METHODS

We conducted a systematic simulation study of a deep-silicon PCCT scanner using a newly developed digital plaque phantom with clinically relevant geometrical and chemical properties. Through qualitative and quantitative evaluations, this study investigates the effects of spatial resolution, noise, and motion artifacts on plaque imaging.

RESULTS

Noise-free simulations indicated that PCCT imaging could delineate the boundary of necrotic cores with a much finer resolution than EID-CT imaging, achieving a structural similarity index metric (SSIM) score of 0.970 and reducing the root mean squared error (RMSE) by two-thirds. Measuring necrotic core area errors were reduced from 91.5% to 24%, and fibrous cap thickness errors were reduced from 349.8% to 33.3%. In the presence of noise, the optimal reconstruction was achieved using 0.25 mm voxels and a soft reconstruction kernel, yielding the highest contrast-to-noise ratio (CNR) of 3.48 for necrotic core detection and the best image quality metrics among all choices. However, the ultrahigh resolution of PCCT increased sensitivity to motion artifacts, which could be mitigated by keeping residual motion amplitude below 0.4 mm.

CONCLUSIONS

The findings suggest that deep-silicon PCCT scanner can offer sufficient spatial resolution and tissue contrast for effective plaque characterization, potentially improving diagnostic accuracy in cardiovascular imaging, provided image noise and motion blur can be mitigated using advanced algorithms. This simulation study involves several simplifications, which may result in some idealized outcomes that do not directly translate to clinical practice. Further validation studies with physical scans are necessary and will be considered for future work.

Development and validation of the effective CNR analysis method for evaluating the contrast resolution of CT images

Contrast resolution is an important index for evaluating the signal detectability of computed tomographic (CT) images. Recently, various noise reduction algorithms, such as iterative reconstruction (IR) and deep learning reconstruction (DLR), have been proposed to reduce the image noise in CT images. However, these algorithms cause changes in the image noise texture and blurred image signals in CT images. Furthermore, the contrast-to-noise ratio (CNR) cannot be accurately evaluated in CT images reconstructed using noise reduction methods. Therefore, in this study, we devised a new method, namely, "effective CNR analysis," for evaluating the contrast resolution of CT images. We verified whether the proposed algorithm could evaluate the effective contrast resolution based on the signal detectability of CT images. The findings showed that the effective CNR values obtained using the proposed method correlated well with the subjective visual impressions of CT images. To investigate whether signal detectability was appropriately evaluated using effective CNR analysis, the conventional CNR analysis method was compared with the proposed method. The CNRs of the IR and DLR images calculated using conventional CNR analysis were 13.2 and 10.7, respectively. By contrast, those calculated using the effective CNR analysis were estimated to be 0.7 and 1.1, respectively. Considering that the signal visibility of DLR images was superior to that of IR images, our proposed effective CNR analysis was shown to be appropriate for evaluating the contrast resolution of CT images.

Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction

Background

We assessed and compared the image quality of normal and pathologic structures as well as the image noise in chest computed tomography images using "adaptive statistical iterative reconstruction-V" (ASiR-V) or deep learning reconstruction "TrueFidelity".

Methods

Forty consecutive patients with suspected lung disease were evaluated. The 1.25-mm axial images and 2.0-mm coronal multiplanar images were reconstructed under the following three conditions: (i) ASiR-V, lung kernel with 60% of ASiR-V; (ii) TF-M, standard kernel, image filter (Lung) with TrueFidelity at medium strength; and (iii) TF-H, standard kernel, image filter (Lung) with TrueFidelity at high strength. Two radiologists (readers) independently evaluated the image quality of anatomic structures using a scale ranging from 1 (best) to 5 (worst). In addition, readers ranked their image preference. Objective image noise was measured using a circular region of interest in the lung parenchyma. Subjective image quality scores, total scores for normal and abnormal structures, and lesion detection were compared using Wilcoxon's signed-rank test. Objective image quality was compared using Student's paired t-test and Wilcoxon's signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of P < 0.016.

Results

Both readers rated TF-M and TF-H images significantly better than ASiR-V images in terms of visualization of the centrilobular region in axial images. The preference score of TF-M and TF-H images for reader 1 were better than that of ASiR-V images, and the preference score of TF-H images for reader 2 were significantly better than that of ASiR-V and TF-M images. TF-M images showed significantly lower objective image noise than ASiR-V or TF-H images.

Conclusion

TrueFidelity showed better image quality, especially in the centrilobular region, than ASiR-V in subjective and objective evaluations. In addition, the image texture preference for TrueFidelity was better than that for ASiR-V.

Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application

OBJECTIVE

The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative reconstruction (IR) algorithms. Our prospective study sought to explore the added value of deep-learning image reconstruction (DLIR) in comparison with a hybrid IR algorithm (adaptive statistical iterative reconstruction-veo [ASiR-V]) in CCTA, even in clinical challenging scenarios, as obesity, heavily calcified vessels and coronary stents.

METHODS

We prospectively included 103 consecutive patients who underwent CCTA. Data sets were reconstructed with ASiR-V and DLIR. For each reconstruction signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was calculated, and qualitative assessment was made with a four-point Likert scale by two independent and blinded radiologists with different expertise.

RESULTS

Both SNR and CNR were significantly higher in DLIR (SNR-DLIR median value [interquartile range] of 13.89 [11.06-16.35] and SNR-ASiR-V 25.42 [22.46-32.22], P < 0.001; CNR-DLIR 16.84 [9.83-27.08] vs CNR-ASiR-V 10.09 [5.69-13.5], P < 0.001).Median qualitative score was 4 for DLIR images versus 3 for ASiR-V ( P < 0.001), with a good interreader reliability [intraclass correlation coefficient(2,1)e intraclass correlation coefficient(3,1) 0.60 for DLIR and 0.62 and 0.73 for ASiR-V].In the obese and in the "calcifications and stents" groups, DLIR showed significantly higher values of SNR (24.23 vs 11.11, P < 0.001 and 24.55 vs 14.09, P < 0.001, respectively) and CNR (16.08 vs 8.04, P = 0.008 and 17.31 vs 10.14, P = 0.003) and image quality.

CONCLUSIONS

Deep-learning image reconstruction in CCTA allows better SNR, CNR, and qualitative assessment than ASiR-V, with an added value in the most challenging clinical scenarios.

A Novel Artificial Intelligence Based Denoising Method for Ultra-Low Dose CT Used for Lung Cancer Screening

RATIONALE AND OBJECTIVES

To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer.

MATERIALS AND METHODS

This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images.

RESULTS

ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p = 0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p = 0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC = 0.924), dULD (ICC = 0.903), and for ULD (ICC = 0.817) scans.

CONCLUSION

A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

BACKGROUND

CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality.

OBJECTIVE

To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V).

METHODS

This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR).

RESULTS

Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001).

CONCLUSION

DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V.

CLINICAL IMPACT

Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.

Milestones in CT: Past, Present, and Future

In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for a 180° rotation to today's 0.24-second scan for a 360° rotation, CT technology continues to reinvent itself. This article describes key historical milestones in CT technology from the earliest days of CT to the present, with a look toward the future of this essential imaging modality. After a review of the beginnings of CT and its early adoption, the technical steps taken to decrease scan times-both per image and per examination-are reviewed. Novel geometries such as electron-beam CT and dual-source CT have also been developed in the quest for ever-faster scans and better in-plane temporal resolution. The focus of the past 2 decades on radiation dose optimization and management led to changes in how exposure parameters such as tube current and tube potential are prescribed such that today, examinations are more customized to the specific patient and diagnostic task than ever before. In the mid-2000s, CT expanded its reach from gray-scale to color with the clinical introduction of dual-energy CT. Today's most recent technical innovation-photon-counting CT-offers greater capabilities in multienergy CT as well as spatial resolution as good as 125 μm. Finally, artificial intelligence is poised to impact both the creation and processing of CT images, as well as automating many tasks to provide greater accuracy and reproducibility in quantitative applications.

Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination

In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain's gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head.

Investigating the Feasibility of Using DenseNet to Improve Coronary Calcification Detection in CT

OBJECTIVES

Interscan reproducibility of coronary artery calcium (CAC) scoring can be improved by using a smaller slice thickness but at the cost of higher image noise. This study aimed to investigate the feasibility of using densely connected convolutional network (DenseNet) to reduce the image noise in CAC scans reconstructed with slice thickness < 3 mm for improving coronary calcification detection in CT.

METHODS

Phantom data acquired with QRM and CIRS phantoms were used for model training and testing, where the DenseNet model adopted in this work was a convolutional neural network (CNN) designed for super resolution recovery. After phantom study, the proposed method was evaluated in terms of its ability to improve calcification detection using patient data. The CNN input images (IMGinput) were CAC scans reconstructed with 0.5-, 1.0- and 1.5-mm slice thickness, while CNN label images were CAC scans reconstructed with 3-mm slice thickness (IMG3mm). Region of interest (ROI) analysis was carried out on IMG3mm, IMGinput and CNN output images (IMGoutput). Two-sample t test was used to compare the difference in Hounsfield Unit (HU) values within ROI between IMG3mm and IMGoutput.

RESULTS

For the calcifications in QRM phantoms, no statistically significant difference was found when comparing the HU values of 400- and 800-HA calcifications identified on IMG3mm to those on IMGoutput with slice thickness of 0.5, 1.0 or 1.5 mm. On the other hand, statistically significant difference was found when comparing the HU values of 200-HA calcifications identified on IMG3mm to those on IMGoutput with a slice thickness of 0.5 and 1.0 mm. Meanwhile, no statistically significant difference was found when comparing the HU values of 200-HA calcifications identified on IMG3mm to those on IMGoutput with a slice thickness of 1.5 mm. As for the rod inserts in CIRS phantoms simulating 9 different tissue types in human body, there was no statistically significant difference between IMG3mm and IMGoutput with slice thickness of 1.5 mm, and all the p values were larger than 0.10. With regards to patient study, more calcification pixels were detected on IMGoutput with a slice thickness of 1.5 mm than on IMG3mm, so calcifications were more clear on the denoised images.

CONCLUSION

According to our results, the CNN-based denoising method could reduce statistical noise in IMGinput with a slice thickness of 1.5 mm without causing significant texture change or variation in HU values. The proposed method could improve cardiovascular risk prediction by detecting small and soft calcifications that are barely identified on 3-mm slice images used in conventional CAC scans.

Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis

PURPOSE

Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative.

METHODS

Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen's Kappa, Mc-Nemar's and Wilcoxon tests.

RESULTS

We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7-0.8 to 0.79, CI95% 0.7-0.8; P=.04) and ER2 (from 0.77 CI95% 0.7-0.8 to 0.80 CI95% 0.8-0.8, P=.02) radiograph-AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33-47% of cases with unequivocal opacities in radiograph, new opacities in 2-6% of normal radiographs and reduced equivocal opacities by 13-16%. Kappa increased from 0.64 (CI95% 0.6-0.8) to 0.7 (CI95% 0.7-0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6-0.7) to 0.76 (CI95% 0.7-0.8) for pneumonic extension.

CONCLUSION

DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.

Trade-off between breast dose and image quality using composite bismuth shields in computed tomography: A phantom study

INTRODUCTION

Many researchers have suggested that bismuth composite shields (BCS) reduce breast dose remarkably; however, the level of this reduction and its impact on image quality has not been assessed. This study aimed to evaluate the efficiency of nano- and micro- BCS in reducing the dose and image quality during chest computed tomography (CT) scans.

MATERIALS AND METHODS

Bismuth shields composed of 15 weighting percentage (wt%) and 20 wt% bismuth oxide (Bi₂O₃) nano- and micro-particles mixed in silicon rubber polymer were constructed in 1 and 1.5 mm thicknesses. The physical properties of nanoparticles were assessed using a scanning electron microscope (SEM), X-ray diffraction (XRD), and energy-dispersive X-ray (EDX). Breast radiation doses were measured experimentally during chest CT using PMMA standard dosimetry phantom (body phantom, 76-419-4150, Fluke Biomedical) in the presence of the shields. The image quality was assessed by calculating signal and noise values in different regions.

RESULTS

The SEM images showed that the average size of Bi₂O₃ nano- and micro-particles was about 70 nm and 150 μm, respectively. The breast doses were reduced by increasing the shield thickness/bismuth weight percentage. The maximum dose reduction was related to the 20% weight of Bi₂O₃ nano-particles and a thickness of 1.5 mm. The minimum dose reduction was related to the 15% weight of Bi₂O₃ micro-particles with a thickness of 1 mm. The mean noise was higher in nano-particle bismuth shields than in micro-particles.

CONCLUSION

Composite shields containing bismuth nano- and micro-particles can reduce the breast dose during chest CT examinations while negatively impacting diagnostic image quality. Several critical factors, such as bismuth concentration, particle size, and shield thickness, directly affect the efficiency.

Absolute Grüneisen parameter measurement in deep tissue based on X-ray-induced acoustic computed tomography

The Grüneisen parameter is a primary parameter of the initial sound pressure signal in the photoacoustic effect, which can provide unique biological information and is related to the temperature change information of an object. The accurate measurement of this parameter is of great significance in biomedical research. Combining X-ray-induced acoustic tomography and conventional X-ray computed tomography, we proposed a method to obtain the absolute Grüneisen parameter. The theory development, numerical simulation, and biomedical application scenarios are discussed. The results reveal that our method not only can determine the Grüneisen parameter but can also obtain the body internal temperature distribution, presenting its potential in the diagnosis of a broad range of diseases.

Can ultra-low-dose computed tomography reliably diagnose and classify maxillofacial fractures in the clinical routine?

Purpose

Maxillofacial trauma predominantly affects young adults between 20 and 40 years of age. Although radioprotection is a legal requirement, the significant potential of dose reduction in computed tomography (CT) is still underused in the clinical routine. The objective of this study was to evaluate whether maxillofacial fractures can be reliably detected and classified using ultra-low-dose CT.

Materials and Methods

CT images of 123 clinical cases with maxillofacial fractures were classified by two readers using the AOCOIAC software and compared with the corresponding results from post-treatment images. In group 1, consisting of 97 patients with isolated facial trauma, pre-treatment CT images at different dose levels (volumetric computed tomography dose index: ultra-low dose, 2.6 mGy; low dose, <10 mGy; and regular dose, <20 mGy) were compared with post-treatment cone-beam computed tomography (CBCT). In group 2, consisting of 31 patients with complex midface fractures, pre-treatment shock room CT images were compared with post-treatment CT at different dose levels or CBCT. All images were presented in random order and classified by 2 readers blinded to the clinical results. All cases with an unequal classification were re-evaluated.

Results

In both groups, ultra-low-dose CT had no clinically relevant effect on fracture classification. Fourteen cases in group 2 showed minor differences in the classification code, which were no longer obvious after comparing the images directly to each other.

Conclusion

Ultra-low-dose CT images allowed the correct diagnosis and classification of maxillofacial fractures. These results might lead to a substantial reconsideration of current reference dose levels.

Low-Dose CT Imaging of the Pelvis in Follow-up Examinations-Significant Dose Reduction and Impact of Tin Filtration: Evaluation by Phantom Studies and First Systematic Retrospective Patient Analyses

OBJECTIVES

Low-dose (LD) computed tomography (CT) is still rarely used in musculoskeletal (MSK) radiology. This study evaluates the potentials of LD CT for follow-up pelvic imaging with special focus on tin filtration (Sn) technology for normal and obese patients with and without metal implants.

MATERIALS AND METHODS

In a phantom study, 5 different LD and normal-dose (ND) CT protocols with and without tin filtration were tested using a normal and an obese phantom. Iterative reconstruction (IR) and filtered back projection (FBP) were used for CT image reconstruction. In a subsequent retrospective patient study, ND CT images of 45 patients were compared with follow-up tin-filtered LD CT images with a 90% dose reduction. Sixty-four percent of patients contained metal implants at the follow-up examination. Computed tomography images were objectively (image noise, contrast-to-noise ratio [CNR], dose-normalized contrast-to-noise ratio [CNRD]) and subjectively, using a 6-point Likert score, evaluated. In addition, the figure of merit was calculated. For group comparisons, paired t tests, Wilcoxon signed rank test, analysis of variance, or Kruskal-Wallis tests were used, where applicable.

RESULTS

The LD Sn protocol with 67% dose reduction resulted in equal values in qualitative (Likert score) and quantitative image analysis (image noise) compared with the ND protocol in the phantom study. For follow-up examinations, dose could be reduced up to 90% by using Sn LD CT scans without impairment in the clinical study. However, metal implants resulted in a mild impairment of Sn LD as well as ND CT images. Cancellous bone ( P < 0.001) was assessed worse and cortical bone ( P = 0.063) equally in Sn LD CT images compared with ND CT images. Figure of merit values were significant ( P ≤ 0.02) lower and hence better in Sn LD as in ND protocols. Obese patients benefited in particular from tin filtration in LD MSK imaging in terms of image noise and CNR ( P ≤ 0.05).

CONCLUSIONS

Low-dose CT scans with tin filtration allow maximum dose reduction while maintaining high image quality for certain clinical purposes, for example, follow-up examinations, especially metal implant position, material loosening, and consolidation controls. Overweight patients benefit particularly from tin filter technology. Although metal implants decrease image quality in ND as well as in Sn LD CT images, this is not a relevant limitation for assessability.

Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue

The aim of the study was to compare the accuracy of the classification pertaining to the results of two types of soft tissue and bone reconstructions of the spinal CT in detecting the porosity of L1 vertebral body spongy tissue. The dataset for each type of reconstruction (high-resolution bone reconstruction and soft tissue reconstruction) included 400 sponge tissue images from 50 healthy patients and 50 patients with osteoporosis. Texture feature descriptors were calculated based on the statistical analysis of the grey image histogram, autoregression model, and wavelet transform. The data dimensional reduction was applied by feature selection using nine methods representing various approaches (filter, wrapper, and embedded methods). Eleven methods were used to build the classifier models. In the learning process, hyperparametric optimization based on the grid search method was applied. On this basis, the most effective model and the optimal subset of features for each selection method used were determined. In the case of bone reconstruction images, four models achieved a maximum accuracy of 92%, one of which had the highest sensitivity of 95%, with a specificity of 89%. For soft tissue reconstruction images, five models achieved the highest testing accuracy of 95%, whereas the other quality indices (TPR and TNR) were also equal to 95%. The research showed that the images derived from soft tissue reconstruction allow for obtaining more accurate values of texture parameters, which increases the accuracy of the classification and offers better possibilities for diagnosing osteoporosis.

A phantom study on usefulness of modifying image parameters to reduce radiation exposure and maintain image quality in chest HRCT

PURPOSE

To investigate the feasibility of reducing radiation dose by modifying tube voltage, window settings, and algorithm while maintaining image quality, based on the qualitative evaluation of its quality and the radiation dose, using raw data acquired in chest high-resolution computed tomography (HRCT).

METHODS

Radiation exposure was measured using a Fluke dosimeter while modifying the tube voltage to 80 and 100 from 120 kVp in a 64-slice multi-detector computed tomography for comparison and analysis. Changes in image quality as a result of the different tube voltage settings, 3 different window settings (-550, -600, and -700), and 2 algorithms (standard and edge) were analyzed using ImageJ.

RESULTS

Relative to 120kVp, the dose decreased by approximately 67.8% and 36.9% at 80 and 100 kVp, respectively. Image quality assessment showed that changing the window setting to -700 (window level) after scanning with the tube voltage set at 100 kVp and applying the edge algorithm reduced the radiation dose while maintaining the image quality.

CONCLUSIONS

The findings are significant with respect to the reduction of scan dose in that they demonstrate how radiation exposure can be reduced in a clinical scenario by altering the settings on an existing HRCT apparatus. Additional clinical trials and image assessments should be conducted on human participants to confirm the feasibility of altering HRCT settings for reducing scan doses.

Ultra-low-dose computed tomography and its utility in wrist trauma in the emergency department

BACKGROUND

The use and frequency of computed tomography (CT) are increasing day by day in emergency departments (ED). This increases the amount of radiation exposed.

PURPOSE

To evaluate the image quality obtained by ultra-low-dose CT (ULDCT) in patients with suspected wrist fractures in the ED and to investigate whether it is an alternative to standard-dose CT (SDCT).

MATERIAL AND METHODS

This is a study prospectively examining 336 patients who consulted the ED for wrist trauma. After exclusion criteria were applied, the patients were divided into the study and control groups. Then, SDCT (120 kVp and 100 mAs) and ULDCT (80 kVp and 5 mAs) wrist protocols were applied simultaneously. The images obtained were evaluated for image quality and fracture independently by a radiologist and an emergency medical specialist using a 5-point scale.

RESULTS

The effective radiation dose calculated for the control group scans was 41.1 ± 2.1 µSv, whereas the effective radiation dose calculated for the study group scans was 0.5 ± 0.0 µSv. The effective radiation dose of the study group was significantly lower than that of the control group (P < 0.01). The CT images in the study group showed no significant differences in the mean image quality score between observer 1 and observer 2 (3.4 and 4.3, respectively; P = 0.58). Both observers could detect all fractures using the ULDCT images.

CONCLUSION

ULDCT provides high-quality images in wrist traumas while reducing the radiation dose by approximately 98% compared to SDCT without any changes in diagnostic accuracy.

A novel simulation-driven reconstruction approach for X-ray computed tomography

PURPOSE

Radiation dose reduction is critical to the success of x-ray computed tomography (CT). Many advanced reconstruction techniques have been developed over the years to combat noise resulting from the low-dose CT scans. These algorithms rely on accurate local estimation of the image noise to determine reconstruction parameters or to select inferencing models. Because of difficulties in the noise estimation for heterogeneous objects, the performance of many algorithms is inconsistent and suboptimal. In this paper, we propose a novel approach to overcome such shortcoming.

METHOD

By injecting appropriate amount of noise in the CT raw data, a computer simulation approach is capable of accurately estimating the local statistics of the raw data and the local noise in the reconstructed images. This information is then used to guide the noise reduction process during the reconstruction. As an initial implementation, a scaling map is generated based on the noise predicted from the simulation and the noise estimated from existing reconstruction algorithms. Images generated with existing algorithms are subsequently modified based on the scaling map. In this study, both iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms are evaluated.

RESULTS

Phantom experiments were conducted to evaluate the performance of the simulation-based noise estimation in terms of the standard deviation and noise power spectrum (NPS). Quantitative results have demonstrated that the noise measured from the original image matches well with the noise estimated from the simulation. Clinical datasets were utilized to further confirm the accuracy of the proposed approach under more challenging conditions. To validate the performance of the proposed reconstruction approach, clinical scans were used. Performance comparison was carried out qualitatively and quantitatively. Two existing advanced reconstruction techniques, IR and DLIR, were evaluated against the proposed approach. Results have shown that the proposed approach outperforms existing IR and DLIR algorithms in terms of noise suppression and, equally importantly, noise uniformity across the entire imaging volume. Visual assessment of the images also reveals that the proposed approach does not endure noise texture issues facing some of the existing reconstruction algorithms today.

CONCLUSION

Phantom and clinical results have demonstrated superior performance of the proposed approach with regard to noise reduction as well as noise homogeneity. Visual inspection of the noise texture further confirms the clinical utility of the proposed approach. Future enhancements on the current implementation are explored regarding image quality and computational efficiency. Because of the limited scope of this paper, detailed investigation on these enhancement features will be covered in a separate report. This article is protected by copyright. All rights reserved.

Diagnostic performance of digital tomosynthesis to evaluate silicone airway stents and related complications

Background

Digital tomosynthesis (DTS) is an imaging technique with benefits in reconstructing sequential cross-sectional images. We evaluated the diagnostic performance of DTS for silicone airway stents and stent-related complications in patients who underwent bronchoscopic intervention.

Methods

This retrospective study included patients who underwent bronchoscopic intervention after chest radiography (CXR) and DTS examinations from September 2013 to August 2020. The interval between CXR, DTS, and bronchoscopic intervention was a maximum of 10 days. CXR and DTS images were evaluated using a bronchoscopic view as a reference. We calculated the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for assessing the diagnostic performance.

Results

The total CXR, DTS, and bronchoscopic intervention-matching datasets comprised 213 cases from 119 patients and, silicone stents were present in 167 of them. The ability of DTS to detect silicone stents was better than that of CXR (sensitivity, 92.8% vs. 71.3%, P<0.001). Of the 167 cases with silicone stents, 53 experienced stent migration and 121 experienced stent obstructions due to granulation tissue or fibrosis. The sensitivity for detecting stent migration was also higher with DTS than with CXR (45.3% vs. 24.5%, P=0.025). The sensitivity for detecting the stent obstruction was better with DTS than with CXR (64.5% vs. 19.0%, P<0.001).

Conclusions

DTS was more sensitive and accurate in revealing silicone airway stents and silicone stent-related complications than CXR. However, there were limitations in confirming stent migration and obstruction with DTS due to granulation tissue growth and fibrosis.

A modified algebraic reconstruction algorithm for sparse projection

Background

Computed tomography (CT) is an advanced medical imaging technology. The images obtained by CT are helpful for improving diagnostic accuracy. Currently, CT is widely used in clinical settings for diagnosis and health examinations. However, full angle CT scanning has the disadvantage of causing radiation damage to the human body. Sparse angle projection CT scanning is the most effective way to minimize this damage, but the quality of the reconstructed image is reduced. Therefore, it is important to improve the reconstructed image quality produced by sparse angle projection.

Methods

In this paper, we focused on the algebraic reconstruction algorithm. To reduce the accumulation of random noise, we formulated a modified algebraic reconstruction algorithm. Firstly, the algebraic reconstruction algorithm was used to compute two consecutive results, and then the weighted sum of these two results was used to correct the reconstructed image, and an iterative result was obtained. Using this method, we aimed to reduce the noise accumulation caused by iteration.

Results

In this study, 20 angle projections were used for the reconstruction. The experimental object was the Shepp-Logan phantom test image. The experiments were implemented under two conditions: without noise and with noise. The peak signal to noise ratio (PSNR) and the mean squared error (MSE) of the reconstructed image from projections without noise were 76.0896 and 0.0016, respectively. The PSNR and MSE of the reconstructed image from projections with noise were 75.8263 and 0.0017, respectively. The reconstructed performance was superior to the previous algebraic reconstruction algorithm.

Conclusions

The performance of the proposed method was superior to other algorithms, which confirms that noise accumulation caused by iteration can be effectively reduced by the weighted summation of two consecutive reconstruction results. Moreover, the reconstruction performance under noisy projection is superior to other algorithms, which demonstrates that the proposed method improves anti-noise performance.

Quantifying image quality in chest computed tomography angiography: Evaluation of different contrast-to-noise ratio measurement methods

BACKGROUND

Contrast-to-noise ratio is used to objectively evaluate image quality in chest computed tomography angiography (CTA). Different authors define and measure contrast-to-noise ratio using different methods.

PURPOSE

To summarize and evaluate the different contrast-to-noise ratio calculation formulas in the current literature.

MATERIAL AND METHODS

A systematic review of the recent literature for studies using contrast-to-noise ratio was performed. Contrast-to-noise ratio measurement methods reported by the different authors were recorded and reproduced in three patients who underwent chest CTA in our department for exploring variations among the different measurement methods.

RESULTS

The search resulted in 109 articles, of which 26 were included. The studies involved 69 different measurements and overall, three different formula patterns. In all three, aorta and pulmonary arteries comprised the objects of interest in the numerator. In the denominator, standard deviation of the attenuation of the object of interest itself or of another background were used to reflect image noise. Some authors averaged the ratio values at different levels to obtain global ratio values. Using the object of interest itself for image noise calculation in the denominator compared to the usage of another background caused the most prominent variances of contrast-to-noise ratio between the two different protocols used for the reproduction of the measurements.

CONCLUSION

We recommend using the standard deviation of the attenuation of a background indicator as image noise rather than the object of interest itself for more reliable and comparative values. Global contrast-to-noise ratios based on averaging the values of different measurement levels should be avoided.

Dose Estimation for the European Epidemiological Study on Pediatric Computed Tomography (EPI-CT)

Within the European Epidemiological Study to Quantify Risks for Paediatric Computerized Tomography (EPI-CT study), a cohort was assembled comprising nearly one million children, adolescents and young adults who received over 1.4 million computed tomography (CT) examinations before 22 years of age in nine European countries from the late 1970s to 2014. Here we describe the methods used for, and the results of, organ dose estimations from CT scanning for the EPI-CT cohort members. Data on CT machine settings were obtained from national surveys, questionnaire data, and the Digital Imaging and Communications in Medicine (DICOM) headers of 437,249 individual CT scans. Exposure characteristics were reconstructed for patients within specific age groups who received scans of the same body region, based on categories of machines with common technology used over the time period in each of the 276 participating hospitals. A carefully designed method for assessing uncertainty combined with the National Cancer Institute Dosimetry System for CT (NCICT, a CT organ dose calculator), was employed to estimate absorbed dose to individual organs for each CT scan received. The two-dimensional Monte Carlo sampling method, which maintains a separation of shared and unshared error, allowed us to characterize uncertainty both on individual doses as well as for the entire cohort dose distribution. Provided here are summaries of estimated doses from CT imaging per scan and per examination, as well as the overall distribution of estimated doses in the cohort. Doses are provided for five selected tissues (active bone marrow, brain, eye lens, thyroid and female breasts), by body region (i.e., head, chest, abdomen/pelvis), patient age, and time period (1977-1990, 1991-2000, 2001-2014). Relatively high doses were received by the brain from head CTs in the early 1990s, with individual mean doses (mean of 200 simulated values) of up to 66 mGy per scan. Optimization strategies implemented since the late 1990s have resulted in an overall decrease in doses over time, especially at young ages. In chest CTs, active bone marrow doses dropped from over 15 mGy prior to 1991 to approximately 5 mGy per scan after 2001. Our findings illustrate patterns of age-specific doses and their temporal changes, and provide suitable dose estimates for radiation-induced risk estimation in epidemiological studies.

Best Practices: Imaging Strategies for Reduced-Dose Chest CT in the Management of Cystic Fibrosis-Related Lung Disease

OBJECTIVE. Cystic fibrosis (CF) is a multisystemic life-limiting disorder. The leading cause of morbidity in CF is chronic pulmonary disease. Chest CT is the reference standard for detection of bronchiectasis. Cumulative ionizing radiation limits the use of CT, particularly as treatments improve and life expectancy increases. The purpose of this article is to summarize the evidence on low-dose chest CT and its effect on image quality to determine best practices for imaging in CF. CONCLUSION. Low-dose chest CT is technically feasible, reduces dose, and renders satisfactory image quality. There are few comparison studies of low-dose chest CT and standard chest CT in CF; however, evidence suggests equivalent diagnostic capability. Low-dose chest CT with iterative reconstructive algorithms appears superior to chest radiography and equivalent to standard CT and has potential for early detection of bronchiectasis and infective exacerbations, because clinically significant abnormalities can develop in patients who do not have symptoms. Infection and inflammation remain the primary causes of morbidity requiring early intervention. Research gaps include the benefits of replacing chest radiography with low-dose chest CT in terms of improved diagnostic yield, clinical decision making, and patient outcomes. Longitudinal clinical studies comparing CT with MRI for the monitoring of CF lung disease may better establish the complementary strengths of these imaging modalities.

Toward a comparison and an optimization of CT protocols using new metrics of dose and image quality part I: prediction of human observers using a model observer for detection and discrimination tasks in low-dose CT images in various scanning conditions

In the context of reducing the patient dose coming from CT scanner examinations without penalizing the diagnosis, the assessment of both patient dose and image quality (IQ) with relevant metrics is crucial. The present study represents the first stage in a larger work, aiming to compare and optimize CT protocols using dose and IQ new metrics. We proposed here to evaluate the capacity of the Non-PreWhitening matched filter with an eye (NPWE) model observer to be a robust and accurate estimation of IQ. We focused our work on two types of clinical tasks: a low contrast detection task and a discrimination task. We designed a torso-shaped phantom, including Plastic Water^®slabs with cylindrical inserts of different diameters, sections and compositions. We led a human observer study with 13 human observers on images acquired in multiple irradiation and reconstruction scanning conditions (voltage, pitch, slice thickness, noise level of the reconstruction algorithm, energy level in dual-energy mode and dose), to evaluate the behavior of the model observer compared to the human responses faced to changing conditions. The model observer presented the same trends as the human observers with generally better results. We rescaled the NPWE model on the human responses by scanning conditions (kVp, pitch, slice thickness) to obtain the best agreement between both observer types, estimated using the Bland-Altman method. The impact of some scanning parameters was estimated using the correct answer rate given by the rescaled NPWE model, for both tasks and each insert size. In particular, the comparison between the dual-energy mode at 74 keV and the single-energy mode at 120 kVp showed that, if the 120 kVp voltage provided better results for the smallest insert at the lower doses for both tasks, their responses were equivalent in many cases.

Recent Imaging Advancements for Lung Metastases in Children with Sarcoma

In children and adolescents affected by musculoskeletal sarcomas (both soft tissue and bone sarcomas), the presence of lung metastases is a frequent complication, that should be known since the patient's prognosis, as management, and treatment depend on it. During the staging phase, the detection of lung metastases should be sensitive and specific, and it should be carried out by minimizing the radiation exposure. To deal with this problem, imaging has reached important goals in recent years, thanks to the development of cone-beam CT or low-dose computed tomography, with some new iterative reconstruction methods, such as Veo and ASIR. Imaging is also fundamental for the possibility to perform lung biopsies under CT guidance, with less morbidity, less time-consumption, and shorter recovery time, compared to surgical biopsies.Moreover, important results have also been demonstrated in the treatment of lung metastases, due to the improvement of new mini-invasive image-guided percutaneous thermal ablation procedures, which proved to be safe and effective also in young patients.

Radiologic Assessment of Osteosarcoma Lung Metastases: State of the Art and Recent Advances

The lung is the most frequent site of osteosarcoma (OS) metastases, which are a critical point in defining a patient’s prognosis. Chest computed tomography (CT) represents the gold standard for the detection of lung metastases even if its sensitivity widely ranges in the literature since lung localizations are often atypical. ESMO guidelines represent one of the major references for the follow-up program of OS patients. The development of new reconstruction techniques, such as the iterative method and the deep learning-based image reconstruction (DLIR), has led to a significant reduction of the radiation dose with the low-dose CT. The improvement of these techniques has great importance considering the young-onset of the disease and the strict chest surveillance during follow-up programs. The use of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT is still controversial, while volume doubling time (VDT) and computer-aided diagnosis (CAD) systems are recent diagnostic tools that could support radiologists for lung nodules evaluation. Their use, well-established for other malignancies, needs to be further evaluated, focusing on OS patients.

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

OBJECTIVES

To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR).

METHODS

The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDI_vol) and dose-length product (DLP) were compared between SD and LD scans.

RESULTS

The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28-0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDI_vol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001).

CONCLUSION

LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR.

ADVANCES IN KNOWLEDGE

Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction

PURPOSE

To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.

METHOD

Pre and post-contrast abdominal CT images in 50 patients were reconstructed with 2 different algorithms: hybrid iterative reconstruction (hybrid IR: ASiR-V 50%) and DLIR (TrueFidelity). Standard deviation of attenuation in normal liver parenchyma was measured as the image noise on pre and post-contrast CT. The contrast-to-noise ratio (CNR) for the aorta, and the signal-to-noise ratio (SNR) of the liver were calculated on post-contrast CT. The overall image quality was graded on a 5-point scale ranging from 1 (poor) to 5 (excellent).

RESULTS

The image noise was significantly decreased by DLIR compared to hybrid-IR [hybrid IR, median 8.3 Hounsfield unit (HU) (interquartile range (IQR) 7.6-9.2 HU); DLIR, median 5.2 HU (IQR 4.6-5.8), P < 0.0001 for post-contrast CT]. The CNR and SNR were significantly improved by DLIR [CNR, median 4.5 (IQR 3.8-5.6) vs 7.3 (IQR 6.2-8.8), P < 0.0001; SNR, median 9.4 (IQR 8.3-10.1) vs 15.0 (IQR 13.2-16.4), P < 0.0001]. The overall image quality score was also higher for DLIR compared to hybrid-IR (hybrid IR 3.1 ± 0.6 vs DLIR 4.6 ± 0.5, P < 0.0001 for post-contrast CT).

CONCLUSIONS

Image noise, overall image quality, CNR and SNR for abdominal CT images are improved with DLIR compared to hybrid IR.

Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients

Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, “Cycle-Deblur GAN”, combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.

Review of Technical Advancements and Clinical Applications of Photon-counting Computed Tomography in Imaging of the Thorax

Photon-counting computed tomography (CT) is a developing technology that has the potential to address some limitations of CT imaging and bring about improvements and potentially new applications to this field. Photon-counting detectors have a fundamentally different detection mechanism from conventional CT energy-integrating detectors that can improve dose efficiency, spatial resolution, and energy-discrimination capabilities. In the past decade, promising human studies have been reported in the literature that have demonstrated benefits of this relatively new technology for various clinical applications. In this review, we provide a succinct description of the photon-counting detector technology and its detection mechanism in comparison with energy-integrating detectors in a manner understandable for clinicians and radiologists, introduce benefits and some of the existing challenges present in this technology, and provide an overview of the current status and potential clinical applications of this technology in imaging of the thorax by providing example images acquired with an investigational whole-body photon-counting CT scanner.

Diagnostic and Therapeutic Nanomedicine

Nanotechnology has been widely applied to medical interventions for prevention, diagnostics, and therapeutics of diseases, and the application of nanotechnology for medical purposes, which is called as a term "nanomedicine" has received tremendous attention. In particular, the design and development of nanoparticle for biosensors have received a great deal of attention, since those are most impactful area of clinical translation showing potential breakthrough in early diagnosis of diseases such as cancers and infections. For example, the nanoparticles that have intrinsic unique features such as magnetic responsive characteristics or photoluminescence can be utilized for noninvasive visualization of inner body. Drug delivery that makes use of drug-containing nanoparticles as a carrier is another field of study, in which the particulate form nanomedicine is given by parenteral administration for further systemic targeting to pathological tissues. In addition, encapsulation into nanoparticles gives the opportunity to secure the sensitive therapeutic payloads that are readily degraded or deactivated until reached to the target in biological environments, or to provide sufficient solubilization (e.g., to deliver compounds which have physicochemical properties that strongly limit their aqueous solubility and therefore systemic bioavailability). The nanomedicine is further intended to enhance the targeting index such as increased specificity and reduced false binding, thus improve the diagnostic and therapeutic performances. In this chapter, principles of nanomaterials for medicine will be thoroughly covered with applications for imaging-based diagnostics and therapeutics.

Value of using adaptive statistical iterative reconstruction-V (ASIR-V) technology in pediatric head CT dose reduction

Background

With widespread use of pediatric head CT, it is critically important to protect patients from radiation hazards, using reduced dose CT techniques. In this regard, adaptive statistical iterative reconstruction-V (ASIR-V) algorithm can decrease image noise, generating CT images of reasonable diagnostic quality with less radiation. The objective of this study was radiation dose assessment, quantitative and qualitative evaluation of reduced dose pediatric head CT using ASIR-V 60% and 80% reconstruction.

Results

Retrospective analysis was performed on two groups of pediatric head CT examinations, a reduced dose CT examination group with ASIR-V reconstruction (ASIR group) (n = 27) and a standard dose CT examination group without ASIR reconstruction (non-ASIR group) (n = 14). The average effective dose (ED) of ASIR group was significantly lower than that of the non-ASIR group (1.04 ± 0.1 mS vs 3.48 ± 0.45 mS; p = 0.001). Quantitative analysis revealed comparable results of signal to noise ratio (SNR) and contrast to noise ratio (CNR) of ASIR and non-ASIR groups (p > 0.05). Qualitative evaluation of resulting images by two readers revealed comparable results of both ASIR and non-ASIR groups (p > 0.05) with excellent inter-reader agreement (κ = 0.97). Both quantitative and qualitative assessment demonstrated better ASIR-V 80% than ASIR-V 60% reconstructed images.

Conclusion

ASIR-V algorithm is a promising technology for effective dose reduction of pediatric head CT with preservation of diagnostic image quality.

Rapid kVp-switching DECT portal venous phase abdominal CT scans in patients with large body habitus: image quality considerations

PURPOSE

To assess the diagnostic image quality and material decomposition characteristics of portal venous phase abdominal CT scans performed on rapid kVp-switching DECT (rsDECT) in patients with large body habitus.

METHODS

We retrospectively included consecutive patients with large body habitus (≥ 90 kg) undergoing portal venous phase abdominal CT scans on rsDECT scanners between Sep 2014 and March 2018. Qualitative and quantitative assessment of the DECT data sets [65 keV monoenergetic, material density iodine (MD-I) and material density water (MD-W) images] was performed for determination of image quality (IQ) and image noise. Correlation of qualitative assessment scores with weight, BMI and patients' diameter were calculated using Pearson correlation test. Optimal thresholds were calculated using AUC and Youden index to define most appropriate size cut off, below which the IQ of material density images is largely acceptable.

RESULTS

The 65 keV monoenergetic images were of diagnostic quality (diagnostic acceptability, DA ≥ 3) in 97.8% of patients (n = 91/93). However, there was significant IQ degradation of MD-I images in 20.4% (n = 19/93, DA < 3) of patients. Similarly, there was significant degradation (DA < 3) of MD-W images in 26.9% (25/92). Clinically significant artifacts (PA ≥ 3/4) were seen in 31% (n = 29/93) and 32.3% (30/93) of MD-I and MD-W images respectively. Optimal threshold for diagnostic acceptability of MD-I images were 110 kg for weight and 33.5 kg/m2 for BMI.

CONCLUSION

Rapid kVp-switching DECT provides diagnostically acceptable monoenergetic images for patients with large body habitus (≥ 90 kg). There is degradation of IQ in the material density specific images particularly in patients weighing > 110 kg and with BMI > 33.5 kg/m2, due to higher number of artifacts.

Spatial Distribution of Noise Reduction in Four Iterative Reconstruction Algorithms in CT—A Technical Evaluation

Iterative reconstruction (IR) is a computed tomgraphy (CT) reconstruction algorithm aiming at improving image quality by reducing noise in the image. During this process, IR also changes the noise properties in the images. To assess how IR algorithms from four vendors affect the noise properties in CT images, an anthropomorphic phantom was scanned and images reconstructed with filtered back projection (FBP), and a medium and high level of IR. Each image acquisition was performed 30 times at the same slice position, to create noise maps showing the inter-image pixel standard deviation through the 30 images. We observed that IR changed the noise properties in the CT images by reducing noise more in homogeneous areas than at anatomical edges between structures of different densities. This difference increased with increasing IR level, and with increasing difference in density between two adjacent structures. Each vendor’s IR algorithm showed slightly different noise reduction properties in how much noise was reduced at different positions in the phantom. Users need to be aware of these differences when working with optimization of protocols using IR across scanners from different vendors.

In vivo noninvasive three-dimensional (3D) assessment of microwave thermal ablation zone using non-contrast-enhanced x-ray CT

PURPOSE

To develop an image processing methodology for noninvasive three-dimensional (3D) quantification of microwave thermal ablation zones in vivo using x-ray computed tomography (CT) imaging without injection of a contrast enhancing material.

METHODS

Six microwave (MW) thermal ablation procedures were performed in three pigs. The ablations were performed with a constant heating duration of 8 min and power level of 30 W. During the procedure images from sixty 1 mm thick slices were acquired every 30 s. At the end of all ablation procedures for each pig, a contrast-enhanced scan was acquired for reference. Special algorithms for addressing challenges stemming from the 3D in vivo setup and processing the acquired images were prepared. The algorithms first rearranged the data to account for the oblique needle orientation and for breathing motion. Then, the gray level variance changes were analyzed, and optical flow analysis was applied to the treated volume in order to obtain the ablation contours and reconstruct the ablation zone in 3D. The analysis also included a special correction algorithm for eliminating artifacts caused by proximal major blood vessels and blood flow. Finally, 3D reference reconstructions from the contrast-enhanced scan were obtained for quantitative comparison.

RESULTS

For four ablations located >3 mm from a large blood vessel, the mean dice similarity coefficient (DSC) and the mean absolute radial discrepancy between the contours obtained from the reference contrast-enhanced images and the contours produced by the algorithm were 0.82 ± 0.03 and 1.92 ± 1.47 mm, respectively. In two cases of ablation adjacent to large blood vessels, the average DSC and discrepancy were: 0.67 ± 0.6 and 2.96 ± 2.15 mm, respectively. The addition of the special correction algorithm utilizing blood vessels mapping improved the mean DSC and the mean absolute discrepancy to 0.85 ± 0.02 and 1.19 ± 1.00 mm, respectively.

CONCLUSIONS

The developed algorithms provide highly accurate detailed contours in vivo (average error < 2.5 mm) and cope well with the challenges listed above. Clinical implementation of the developed methodology could potentially provide real time noninvasive 3D accurate monitoring of MW thermal ablation in-vivo, provided that the radiation dose can be reduced.

A practical and adaptive approach to lung cancer screening: a review of international evidence and position on CT lung cancer screening in the Singaporean population by the College of Radiologists Singapore

Lung cancer is the leading cause of cancer-related death around the world, being the top cause of cancer-related deaths among men and the second most common cause of cancer-related deaths among women in Singapore. Currently, no screening programme for lung cancer exists in Singapore. Since there is mounting evidence indicating a different epidemiology of lung cancer in Asian countries, including Singapore, compared to the rest of the world, a unique and adaptive approach must be taken for a screening programme to be successful at reducing mortality while maintaining cost-effectiveness and a favourable risk-benefit ratio. This review article promotes the use of low-dose computed tomography of the chest and explores the radiological challenges and future directions.

Evaluation of three-dimensional iterative image reconstruction in virtual monochromatic imaging at 40 kilo-electron volts: phantom and clinical studies to assess the image noise and image quality in comparison with other reconstruction techniques

OBJECTIVE

The purpose of this study was to evaluate the image quality in virtual monochromatic imaging (VMI) at 40 kilo-electron volts (keV) with three-dimensional iterative image reconstruction (3D-IIR).

METHODS

A phantom study and clinical study (31 patients) were performed with dual-energy CT (DECT). VMI at 40 keV was obtained and the images were reconstructed using filtered back projection (FBP), 50% adaptive statistical iterative reconstruction (ASiR), and 3D-IIR. We conducted subjective and objective evaluations of the image quality with each reconstruction technique.

RESULTS

The image contrast-to-noise ratio and image noise in both the clinical and phantom studies were significantly better with 3D-IIR than with 50% ASiR, and with 50% ASiR than with FBP (all, p < 0.05). The standard deviation and noise power spectra of the reconstructed images decreased in the order of 3D-IIR to 50% ASiR to FBP, while the modulation transfer function was maintained across the three reconstruction techniques. In most subjective evaluations in the clinical study, the image quality was significantly better with 3D-IIR than with 50% ASiR, and with 50% ASiR than with FBP (all, p < 0.001). Regarding the diagnostic acceptability, all images using 3D-IIR were evaluated as being fully or probably acceptable.

CONCLUSIONS

The quality of VMI at 40 keV is improved by 3D-IIR, which allows the image noise to be reduced and structural details to be maintained.

ADVANCES IN KNOWLEDGE

The improvement of the image quality of VMI at 40 keV by 3D-IIR may increase the subjective acceptance in the clinical setting.

Cone-beam breast computed tomography using ultra-fast image reconstruction with constrained, total-variation minimization for suppression of artifacts

Compressed sensing based iterative reconstruction algorithms for computed tomography such as adaptive steepest descent-projection on convex sets (ASD-POCS) are attractive due to their applicability in incomplete datasets such as sparse-view data and can reduce radiation dose to the patients while preserving image quality. Although IR algorithms reduce image noise compared to analytical Feldkamp-Davis-Kress (FDK) algorithm, they may generate artifacts, particularly along the periphery of the object. One popular solution is to use finer image-grid followed by down-sampling. This approach is computationally intensive but may be compensated by reducing the field of view. Our proposed solution is to replace the algebraic reconstruction technique within the original ASD-POCS by ordered subsets-simultaneous algebraic reconstruction technique (OS-SART) and with initialization using FDK image. We refer to this method as Fast, Iterative, TV-Regularized, Statistical reconstruction Technique (FIRST). In this study, we investigate FIRST for cone-beam dedicated breast CT with large image matrix. The signal-difference to noise ratio (SDNR), the difference of the mean value and the variance of adipose and fibroglandular tissues for both FDK and FIRST reconstructions were determined. With FDK serving as the reference, the root-mean-square error (RMSE), bias, and the full-width at half-maximum (FWHM) of microcalcifications in two orthogonal directions were also computed. Our results suggest that FIRST is competitive to the finer image-grid method with shorter reconstruction time. Images reconstructed using the FIRST do not exhibit artifacts and outperformed FDK in terms of image noise. This suggests the potential of this approach for radiation dose reduction in cone-beam breast CT.

Coronary CT Angiography with Knowledge-Based Iterative Model Reconstruction for Assessing Coronary Arteries and Non-Calcified Predominant Plaques

Objective

To assess the effects of iterative model reconstruction (IMR) on image quality for demonstrating non-calcific high-risk plaque characteristics of coronary arteries.

Materials and Methods

This study included 66 patients (53 men and 13 women; aged 39–76 years; mean age, 55 ± 13 years) having single-vessel disease with predominantly non-calcified plaques evaluated using prospective electrocardiogram-gated 256-slice CT angiography. Paired image sets were created using two types of reconstruction: hybrid iterative reconstruction (HIR) and IMR. Plaque characteristics were compared using the two algorithms. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images and the CNR between the plaque and adjacent adipose tissue were also compared between the two reformatted methods.

Results

Seventy-seven predominantly non-calcified plaques were detected. Forty plaques showed napkin-ring sign with the IMR reformatted method, while nineteen plaques demonstrated napkin-ring sign with HIR. There was no statistically significant difference in the presentation of positive remodeling, low attenuation plaque, and spotty calcification between the HIR and IMR reconstructed methods (all p > 0.5); however, there was a statistically significant difference in the ability to discern the napkin-ring sign between the two algorithms (χ² = 12.12, p < 0.001). The image noise of IMR was lower than that of HIR (10 ± 2 HU versus 12 ± 2 HU; p < 0.01), and the SNR and CNR of the images and the CNR between plaques and surrounding adipose tissues on IMR were better than those on HIR (p < 0.01).

Conclusion

IMR can significantly improve image quality compared with HIR for the demonstration of coronary artery and atherosclerotic plaques using a 256-slice CT.

[New Potential Method for Optimizing the ATCM Technique in Pediatric CT Examination]

PURPOSE

To compare the radiation dose and image quality using the conventional method for performing the front and side scout view and a new method for performing the side scout view, and then correct the table height at the scan isocenter and perform the front scout view.

METHODS

We retrospectively analyzed fifty-six children who had underwent computed tomography (CT) examination between June 2014 and August 2018. We divided them into two groups. The conventional method was performed in 3 steps: 1. obtain the front scout view, 2. obtain the side scout view, and 3. main scan. Without table position correction, the new method was performed in 4 steps: 1. obtain the side scout view with table position correction, 2. patient correction at the scan isocenter, 3. obtain the front scout view, and 4. main scan. We used a 64-row CT scanner (LightSpeed VCT; GE Healthcare). Scan parameters were tube voltage 80 kV, automatic tube current modulation, noise index 16, slice thickness 5 mm, rotation time 0.4 s/rot, helical pitch 1.375, and reconstruction kernel standard. We recorded the volume dose index (CTDI_vol) and dose length product (DLP) on the CT console and compared the radiation dose in both groups. To evaluate the image quality in both groups, the mean standard deviation of CT number (SD value) was measured within an approximately 5-10 mm²  circular region of interest. We measured the scan length of the pediatric patient and accuracy of pediatric positioning at the CT examination. A grid was displayed on the CT axial image, taken to evaluate the error from the scan isocenter during alignment, and the error between the height of half the body thickness and the scan isocenter was recorded.

RESULTS

Scan lengths were median (minimum-maximum) values of 16.2 cm (10.8-21.5 cm) and 16.8 cm (11.5-23.0 cm). There were no significant differences in the scan length between both groups (p=0.47). In the group with table position correction, median (minimum-maximum) values for CTDI_vol, DLP and SD value were 0.40 mGy (0.3-0.7 mGy), 7.6 mGy･cm (4.4-11.5 mGy･cm), and 24.0 HU (18.3-37.5 HU), respectively. In the group without the table position correction, median (minimum-maximum) values for CTDI_vol, DLP and SD value were 0.40 mGy (0.3-0.6 mGy), 7.1 mGy･cm (4.2-13.8 mGy･cm), and 20.3 HU (11.3-28.8 HU), respectively. There were no significant differences in the CTDI_vol and DLP values between both groups (p=0.42 and p=0.44, respectively); however, there were significant differences in the SD value in both groups (p<0.01). The error for the accuracy of pediatric positioning was 0 mm (0 to 0 mm) and 10 mm (-16 to+59 mm) using the conventional and new methods (p<0.01), respectively.

CONCLUSIONS

It was suggested that the optimum image could be obtained during CT scan with automatic tube current modulation by using this potential new method (1. obtain the side scout view, 2. patient correction at the scan isocenter, 3. obtain the side scout view, and 4. main scan).

Assessment of potential reduction in multidetector computed tomography doses using FBP and SAFIRE for detection and measurement of the position of the inferior alveolar canal

OBJECTIVE

The objective was to identify the lowest doses required to detect and measure the position of the inferior alveolar canal (IAC) on multidetector computed tomography (MDCT) images using filtered backprojection (FBP) and sinogram-affirmed iterative reconstructions (SAFIRE) 3 and SAFIRE 5.

STUDY DESIGN

Four cadaveric mandibles were imaged using a reference protocol with standard dose and FBP and 3 ultra-low-dose protocols (LD1-LD3), using an MDCT scanner. All test examinations were reconstructed with FBP, SAFIRE 3, and SAFIRE 5. Subjective visibility of the IAC in the images and digital measurements of the height of the ridge above the IAC were recorded from test images and compared with those from the reference image using one-sample t tests, Bland-Altman plots, and linear regression.

RESULTS

Subjective visibility comparable to the standard protocol was obtained with an 84.6% dose reduction using the LD2 protocol. No statistically significant difference was found between the height measurements from the reference protocol and any of the LD1 and LD2 protocols. The t tests indicated a significant difference between the measurements from the reference and all LD3 test protocols. SAFIRE did not have an advantage over FBP images.

CONCLUSIONS

Significant dose reduction from the reference dose can allow adequate detection and measurements of the IAC.