Ultralow dose CT for pulmonary nodule detection with chest x-ray equivalent dose – a prospective intra-individual comparative study

Implementing tin-prefiltration in routine clinical CT scans of the lower extremity: impact on radiation dose

OBJECTIVES

Several studies have demonstrated the potential of tin-prefiltration to reduce radiation dose while maintaining diagnostic image quality for musculoskeletal imaging. Still, no study has reported data on the impact of tin-prefiltration on radiation dose reduction for clinical routine scanning.

MATERIALS AND METHODS

Retrospective inclusion of 300 clinically indicated CT scans of the pelvis, knee, and ankle before January 2020 (without tin filter) and after December 2020 (with tin filter). For each joint, 50 examinations with tin-prefiltration and 50 examinations without tin-prefiltration were selected. Dose parameters were extracted, calculated, and compared. Subjective and quantitative parameters for image quality were assessed.

RESULTS

The CTDIvol, DLP, and effective dose were reduced significantly in all tin-prefiltered examinations compared to the non-tin-prefiltered examinations (p < 0.001): CTDIvol was 65% lower in the pelvis, 73% lower in the knee, and 54% lower in the ankle. This reduced the effective dose of 61%, 71%, and 60%, respectively. In absolute numbers, the reduction of the median effective dose delivered in a single CT scan of the pelvis was - 2.29 mSv, - 0.15 mSv for the knee, and - 0.03 mSv for the ankle. No difference in diagnostic image quality, depiction of bone anatomy and soft tissues, and image artifacts was observed (p > 0.05). Subjective and objective image noise was higher in tin-prefiltered pelvis CT (p < 0.001).

CONCLUSION

The implementation of tin-prefiltration in clinical routine scan protocols significantly reduced the effective radiation dose for unenhanced CT scans of the lower extremities between 60 and 70%.

Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V

OBJECTIVES

To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.

MATERIALS AND METHODS

A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses.

RESULTS

A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001).

CONCLUSION

DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT.

KEY POINTS

Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.

Tin prefiltration in computed tomography does not significantly alter radiation-induced gene expression and DNA double-strand break formation

BACKGROUND

The tin (Sn) prefilter technique is a recently introduced dose-saving technique in computed tomography (CT). This study investigates whether there is an altered molecular biological response in blood cells using the tin prefiltering technique.

METHODS

Blood from 6 donors was X-irradiated ex-vivo with 20 mGy full dose (FD) protocols (Sn 150 kV, 150 kV, and 120 kV) and a tin prefiltered 16.5 mGy low dose (LD) protocol on a CT scanner. Biological changes were determined by quantification of γH2AX DNA double-strand break (DSB) foci, and differential gene expression (DGE) relative to unexposed samples were examined for seven known radiation-induced genes (FDXR, DDB2, BAX, CDKN1A, AEN, EDA2R, APOBEC3H) and 667 microRNAs (miRNA).

RESULTS

EDA2R and DDB2 gene expression (GE) increased 1.7-6-fold (p = 0.0004-0.02) and average DNA DSB foci value (0.31±0.02, p<0.0001) increased significantly relative to unexposed samples, but similarly for the applied radiation protocols. FDXR upregulation (2.2-fold) was significant for FD protocols (p = 0.01-0.02) relative to unexposed samples. miRNA GE changes were not significant (p = 0.15-1.00) and DGE were similar for the examined protocols (p = 0.10-1.00). An increased frequency of lower DGE values was seen in the Sn 150 kV LD protocol compared to the 120 kV FD and Sn 150 kV FD protocols (p = 0.001-0.008).

CONCLUSIONS

The current ex-vivo study indicates no changes regarding transcriptional and post-transcriptional DGE and DNA DSB induction when using the tin prefilter technique and even a significant tendency to lower radiation-induced DGE-changes due to the dose reduction of the tin prefilter with equal image quality compared to classical CT scan protocols was found.

Ultra-low-dose chest computed tomography with model-based iterative reconstruction in the analysis of solid pulmonary nodules: A prospective study

BACKGROUND

Incidental pulmonary nodules are an increasingly common finding on computed tomography (CT) scans of the thorax due to the exponential rise in CT examinations in everyday practice. The majority of incidental pulmonary nodules are benign and correctly identifying the small number of malignant nodules is challenging. Ultra-low-dose CT (ULDCT) has been shown to be effective in diagnosis of respiratory pathology in comparison with traditional standard dose techniques. Our hypothesis was that ULDCT chest combined with model-based iterative reconstruction (MBIR) is comparable to standard dose CT (SDCT) chest in the analysis of pulmonary nodules with significant reduction in radiation dose.

AIM

To prospectively compare ULDCT chest combined with MBIR with SDCT chest in the analysis of solid pulmonary nodules.

METHODS

A prospective cohort study was conducted on adult patients (n = 30) attending a respiratory medicine outpatient clinic in a tertiary referral university hospital for surveillance of previously detected indeterminate pulmonary nodules on SDCT chest. This study involved the acquisition of a reference SDCT chest followed immediately by an ULDCT chest. Nodule identification, nodule characterisation, nodule measurement, objective and subjective image quality and radiation dose were compared between ULDCT with MBIR and SDCT chest.

RESULTS

One hundred solid nodules were detected on ULDCT chest and 98 on SDCT chest. There was no significant difference in the characteristics of correctly identified nodules when comparing SDCT chest to ULDCT chest protocols. Signal-to-noise ratio was significantly increased in the ULDCT chest in all areas except in the paraspinal muscle at the maximum cardiac diameter level (P < 0.001). The mean subjective image quality score for overall diagnostic acceptability was 8.9/10. The mean dose length product, computed tomography volume dose index and effective dose for the ULDCT chest protocol were 5.592 mGy.cm, 0.16 mGy and 0.08 mSv respectively. These were significantly less than the SDCT chest protocol (P < 0.001) and represent a radiation dose reduction of 97.6%.

CONCLUSION

ULDCT chest combined with MBIR is non-inferior to SDCT chest in the analysis of previously identified solid pulmonary nodules and facilitates a large reduction in radiation dose.

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm

PURPOSE

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).

MATERIAL AND METHODS

This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location.

RESULTS

The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6 mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively).

CONCLUSIONS

At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging

PURPOSE

To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT).

METHODS

142 participants required lung examination who underwent simultaneously ULD-CT (UL-A, 0.57 ± 0.04 mSv or UL-B, 0.33 ± 0.03 mSv), and standard CT (SDCT, 4.32 ± 0.33 mSv) plain scans were included in this prospective study. SDCT was the reference standard using ASIR-V at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective scores were measured. The presence and accuracy of nodules were analyzed using a combination of a deep learning-based nodule evaluation system and a radiologist.

RESULTS

A total of 710 nodules were detected by SDCT, including 358 nodules in UL-A and 352 nodules in UL-B. DLIR-H exhibited superior noise, SNR, and CNR performance, and achieved comparable or even higher subjective scores compared to 50%ASIR-V in ULD-CT. Nodules sensitivity detection of 50%ASIR-V, DLIR-M, and DLIR-H in ULD-CT were identical (96.90%). In multivariate analysis, body mass index (BMI), nodule diameter, and type were independent predictors for the sensitivity of nodule detection (p<.001). DLIR-H provided a lower absolute percent error (APE) in volume (3.10% ± 95.11% vs 8.29% ± 99.14%) compared to 50%ASIR-V of ULD-CT (P<.001).

CONCLUSIONS

ULD-CT scanning has a high sensitivity for detecting pulmonary nodules. Compared with ASIR-V, DLIR can significantly reduce image noise, and improve image quality, and accuracy of the nodule measurement in ULD-CT.

Optimizing lung biopsy procedures:Comparative analysis of diagnostic efficacy and safety in experimental low-dose, conventional low-dose, and standard-dose CT-guided approaches

PURPOSE

Lung cancer is a major cause of cancer-related deaths, emphasizing the importance of early diagnosis. CT-guided percutaneous lung biopsy(CT-PLB) is a valuable method for diagnosing lung lesions, but multiple scans can elevate radiation exposure. This study aims to compare diagnostic efficacy and safety across different CT-PLB protocols.

METHODS

273 consecutive patients who underwent CT-PLB between June 2018 and February 2021 were enrolled, and were divided into standard-dose, conventional low-dose, and experimental low-dose groups. The study mainly evaluated technical success, diagnostic efficacy, radiation dose, complications, and image quality.

RESULTS

93 patients were assigned to standard-dose group, 85 to conventional low-dose group, and 95 to experimental low-dose group. Technical success rates in these groups were 97.9%, 100%, and 97.9%, respectively. Procedure-related complications rates were similar across the groups(pneumothorax:p=0.71, hemorrhage:p=0.59). Sensitivity, specificity, and overall diagnostic accuracy were comparable among three groups(p=0.59,1.0,0.65), with respective values of 90.5%, 100%, and 93.2% in standard-dose group, 88.1%, 100%, and 90.5% in conventional low-dose group, and 91.9%, 100%, and 93.4% in experimental low-dose group. The effective dose (ED) in the experimental low-dose group was significantly lower compared to both the standard-dose and conventional low-dose CT-PLB groups[ED: 1.49(1.0∼1.97) mSv vs 5.42(3.92∼6.91) mSv vs 3.15(2.52∼4.22) mSv, p<0.001].

CONCLUSIONS

This study has developed a standardized six-step procedure for CT-PLB using experimental low-dose settings. It can achieve comparable diagnostic efficacy to conventional low-dose and standard-dose CT-PLB protocols while substantially reducing radiation exposure. These findings indicate that the experimental low-dose protocol could serve as a safe and effective alternative for CT-PLB.

Deep learning segmentation and registration-driven lung parenchymal volume and movement CT analysis in prone positioning

PURPOSE

To conduct a volumetric and movement analysis of lung parenchyma in prone positioning using deep neural networks (DNNs).

METHOD

We included patients with suspected interstitial lung abnormalities or disease who underwent full-inspiratory supine and prone chest CT at a single institution between June 2021 and March 2022. A thoracic radiologist visually assessed the fibrosis extent in the total lung (using units of 10%) on supine CT. After preprocessing the images into 192×192×192 resolution, a DNN automatically segmented the whole lung and pulmonary lobes in prone and supine CT images. Affine registration matched the patient's center and location, and the DNN deformably registered prone and supine CT images to calculate the x-, y-, z-axis, and 3D pixel movements.

RESULTS

In total, 108 CT pairs had successful registration. Prone positioning significantly increased the left lower (90.2±69.5 mL, P = 0.000) and right lower lobar volumes (52.5±74.2 mL, P = 0.000). During deformable registration, the average maximum whole-lung pixel movements between the two positions were 1.5, 1.9, 1.6, and 2.8 cm in each axis and 3D plane. Compared to patients with <30% fibrosis, those with ≥30% fibrosis had smaller volume changes (P<0.001) and smaller pixel movements in all axes between the positions (P = 0.000-0.007). Forced vital capacity (FVC) correlated with the left lower lobar volume increase (Spearman correlation coefficient, 0.238) and the maximum whole-lung pixel movements in all axes (coefficients, 0.311 to 0.357).

CONCLUSIONS

Prone positioning led to the preferential expansion of the lower lobes, correlated with FVC, and lung fibrosis limited lung expansion during prone positioning.

Detectability and Volumetric Accuracy of Pulmonary Nodules in Low-Dose Photon-Counting Detector Computed Tomography: An Anthropomorphic Phantom Study

The aim of this phantom study was to assess the detectability and volumetric accuracy of pulmonary nodules on photon-counting detector CT (PCD-CT) at different low-dose levels compared to conventional energy-integrating detector CT (EID-CT). In-house fabricated artificial nodules of different shapes (spherical, lobulated, spiculated), sizes (2.5-10 mm and 5-1222 mm3), and densities (-330 HU and 100 HU) were randomly inserted into an anthropomorphic thorax phantom. The phantom was scanned with a low-dose chest protocol with PCD-CT and EID-CT, in which the dose with PCD-CT was lowered from 100% to 10% with respect to the EID-CT reference dose. Two blinded observers independently assessed the CT examinations of the nodules. A third observer measured the nodule volumes using commercial software. The influence of the scanner type, dose, observer, physical nodule volume, shape, and density on the detectability and volumetric accuracy was assessed by a multivariable regression analysis. In 120 CT examinations, 642 nodules were present. Observer 1 and 2 detected 367 (57%) and 289 nodules (45%), respectively. With PCD-CT and EID-CT, the nodule detectability was similar. The physical nodule volumes were underestimated by 20% (range 8-52%) with PCD-CT and 24% (range 9-52%) with EID-CT. With PCD-CT, no significant decrease in the detectability and volumetric accuracy was found at dose reductions down to 10% of the reference dose (p > 0.05). The detectability and volumetric accuracy were significantly influenced by the observer, nodule volume, and a spiculated nodule shape (p < 0.05), but not by dose, CT scanner type, and nodule density (p > 0.05). Low-dose PCD-CT demonstrates potential to detect and assess the volumes of pulmonary nodules, even with a radiation dose reduction of up to 90%.

Dose development in sinonasal imaging over the last decade - a retrospective patient study

BACKGROUND

Computed tomography (CT) has become the primary imaging modality for visualization of the paranasal sinuses. In this retrospective, single center patient study the radiation dose development in the past 12 years in CT imaging of the paranasal sinuses was assessed.

METHODS

The computed tomography dose index (CTDIVol) and dose length product (DLP) of a total of 1246 patients (average age: 41 ± 18 years, 361 females, 885 males) were evaluated, who received imaging of the paranasal sinuses either for chronic sinusitis diagnostic, preoperatively or posttraumatically. Scans were performed on three different CT scanners (Somatom Definition AS, Somatom Definition AS+, Somatom Force, all from Siemens Healthineers) and on one CBCT (Morita) ranging from 2010 to 2022. Reconstruction techniques were filtered back projection and three generations of iterative reconstruction (IRIS, SAFIRE, ADMIRE, all from Siemens Healthineers). Group comparisons were performed using either parametrical (ANOVA) or non-parametrical tests (Kruskal-Wallis Test), where applicable.

RESULTS

Over the past 12 years, there was a 73%, 54%, and 66% CTDIVol reduction and a significant (p < 0.001) 72%, 33%, and 67% DLP reduction in assessing the paranasal sinuses for chronic sinusitis, preoperatively and posttraumatically, respectively.

CONCLUSION

Technological developments in CT imaging, both hardware and software based, have led to a significant reduction in dose exposure in recent years. Particularly in imaging of the paranasal sinuses, the reduction of radiation exposure is of great interest due to the often young patient age and radiation-sensitive organs in the area of radiation exposure.

Developing a pan-European technical standard for a comprehensive high-quality lung cancer computed tomography screening programme: an ERS technical standard

BACKGROUND

Screening for lung cancer with low radiation dose computed tomography (LDCT) has a strong evidence base. The European Council adopted a recommendation in November 2022 that lung cancer screening (LCS) be implemented using a stepwise approach. The imperative now is to ensure that implementation follows an evidence-based process that delivers clinical and cost-effectiveness. This European Respiratory Society (ERS) Task Force was formed to provide a technical standard for a high-quality LCS programme.

METHOD

A collaborative group was convened to include members of multiple European societies. Topics were identified during a scoping review and a systematic review of the literature was conducted. Full text was provided to members of the group for each topic. The final document was approved by all members and the ERS Scientific Advisory Committee.

RESULTS

Topics were identified representing key components of a screening programme. The actions on findings from the LDCT were not included as they are addressed by separate international guidelines (nodule management and clinical management of lung cancer) and by a linked ERS Task Force (incidental findings). Other than smoking cessation, other interventions that are not part of the core screening process were not included (e.g. pulmonary function measurement). 56 statements were produced and areas for further research identified.

CONCLUSIONS

This European collaborative group has produced a technical standard that is a timely contribution to implementation of LCS. It will serve as a standard that can be used, as recommended by the European Council, to ensure a high-quality and effective programme.

Detection and size quantification of pulmonary nodules in ultralow-dose versus regular-dose CT: a comparative study in COPD patients

OBJECTIVE

To evaluate detectability and semi-automatic diameter and volume measurements of pulmonary nodules in ultralow-dose CT (ULDCT) vs regular-dose CT (RDCT).

METHODS

Fifty patients with chronic obstructive pulmonary disease (COPD) underwent RDCT on 64-multidetector CT (120 kV, filtered back projection), and ULDCT on third-generation dual source CT (100 kV with tin filter, advanced modeled iterative reconstruction). One radiologist evaluated the presence of nodules on both scans in random order, with discrepancies judged by two independent radiologists and consensus reading. Sensitivity of nodule detection on RDCT and ULDCT was compared to reader consensus. Systematic error in semi-automatically derived diameter and volume, and 95% limits of agreement (LoA) were evaluated. Nodule classification was compared by κ statistics.

RESULTS

ULDCT resulted in 83.1% (95% CI: 81.0-85.2) dose reduction compared to RDCT (p < 0.001). 45 nodules were present, with diameter range 4.0-25.3 mm and volume range 16.0-4483.0 mm³. Detection sensitivity was non-significant (p = 0.503) between RDCT 88.8% (95% CI: 76.0-96.3) and ULDCT 95.5% (95% CI: 84.9-99.5). No systematic bias in diameter measurements (median difference: -0.2 mm) or volumetry (median difference: -6 mm³) was found for ULDCT compared to RDCT. The 95% LoA for diameter and volume measurements were ±3.0 mm and ±33.5%, respectively. κ value for nodule classification was 0.852 for diameter measurements and 0.930 for volumetry.

CONCLUSION

ULDCT based on Sn100 kV enables comparable detectability of solid pulmonary nodules in COPD patients, at 83% reduced radiation dose compared to RDCT, without relevant difference in nodule measurement and size classification.

ADVANCES IN KNOWLEDGE

Pulmonary nodule detectability and measurements in ULDCT are comparable to RDCT.

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software

AIM

To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

MATERIALS AND METHODS

A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference.

RESULTS

Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively.

CONCLUSION

Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.

Correlation of lung function with ultra-low-dose CT-detected lung parenchymal abnormalities: a cohort study of 1344 asbestos exposed individuals

INTRODUCTION

Deliberate exposure to medical ionising radiation should be as low as reasonably practicable but the reduction of radiation from CT should be balanced against diagnostic image quality. The ability of ultra-low-dose CT (uLDCT: similar radiation to chest X-ray) to demonstrate low contrast abnormalities (emphysema and interstitial lung abnormality (ILA)) is unclear.The aim of this cross-sectional study was to analyse the lung parenchymal findings from uLDCT scans against physiological measures of respiratory function.

METHODS

WA Asbestos Review Programme participants were eligible if they had an uLDCT scan and lung function assessment between Janary and December 2018. All scans were performed using a single CT machine and reported using a standardised, semiquantitative synoptic report which includes emphysema and linear fibrosis (ILA) scores.

RESULTS

Of 1344 participants, median (IQR) age was 72.0 (65.0-78.0) years, the majority were males (84.9%) with mixed occupational asbestos exposure (68.1%). There were 721 (53.6%) with no abnormality, 158 (11.8%) with emphysema, 465 (34.6%) with ILA. Mean radiation dose was 0.12 mSv. There was statistically significant between group differences for all physiological parameters of lung function compared with controls. For instance, the emphysema score significantly correlated with obstructive forced expiratory volume in 1 s (FEV₁)/forced vital capacity ratio (r=0.512), per cent predicted FEV₁ (r=0.24) and lower diffusion of carbon monoxide (DLCO) (r=0.337). Multivariate modelling demonstrated that increasing age, emphysema and fibrosis scores predicted reduced DLCO (adjusted R²=0.30).

DISCUSSION

uLDCT-detected parenchymal lung abnormalities correlate strongly with significant changes on lung function testing suggesting the observed CT abnormalities are of physiological and clinical significance.

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.

The role of ultra-low-dose computed tomography in the detection of pulmonary pathologies: a prospective observational study

PURPOSE

The aim of the study was to compare the image noise, radiation dose, and image quality of ultra-low-dose computed tomography (CT) and standard CT in the imaging of pulmonary pathologies.

MATERIAL AND METHODS

This observational study was performed between July 2020 and August 2021. All enrolled patients underwent both ultra-low-dose and standard CTs. The image noise, image quality for normal pulmonary structures, presence or absence of various pulmonary lesions, and radiation dose were recorded for each of the scans. The findings of standard-dose CT were regarded as the gold standard and compared with that of ultra-low-dose CT.

RESULTS

A total of 124 patients were included in the study. The image noise was higher in the ultra-low-dose CT compared to standard-dose CT. The overall image quality was determined to be diagnostic in 100% of standard CT images and in 96.77% of ultra-low-dose CT images with proportional worsening of the image quality as the body mass index (BMI) range was increased. Ultra-low-dose CT offered higher (> 90%) sensitivity for lesions like consolidation (97%), pleural effusion (95%), fibrosis (92%), and solid pulmonary nodules (91%). The effective radiation dose (mSv) was many times lower in ultra-low-dose CT when compared to standard-dose CT (mean ± SD: 0.50 ± 0.005 vs. 3.99 ± 1.57).

CONCLUSIONS

The radiation dose of ultra-low-dose chest CT was almost equal to that of a chest X-ray. It could be used for the screening and/or follow-up of patients with solid pulmonary nodules (> 3 mm) and consolidation.

Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans

PURPOSE

To assess the performance of semi-automated volumetry of solid pulmonary nodules on single-energy tin-filtered ultralow dose (ULD) chest CT scans at a radiation dose equivalent to chest X-ray relative to standard dose (SD) chest CT scans and assess the impact of kernel and iterative reconstruction selection.

METHODS

Ninety-four consecutive patients from a prospective single-center study were included and underwent clinically indicated SD chest CT (1.9 ± 0.8 mSv) and additional ULD chest CT (0.13 ± 0.01 mSv) in the same session. All scans were reconstructed with a soft tissue (Br40) and lung (Bl64) kernel as well as with Filtered Back Projection (FBP) and Iterative Reconstruction (ADMIRE-3 and ADMIRE-5). One hundred and forty-eight solid pulmonary nodules were identified and analysed by semi-automated volumetry on all reconstructions. Nodule volumes were compared amongst all reconstructions thereby focusing on the agreement between SD and ULD scans.

RESULTS

Nodule volumes ranged from 58.5 (28.8-126) mm3 for ADMIRE-5 Br40 ULD reconstructions to 72.5 (39-134) mm3 for FBP Bl64 SD reconstructions with significant differences between reconstructions (p < 0.001). Interscan agreement of volumes between two given reconstructions ranged from ICC = 0.605 to ICC = 0.999. Between SD and ULD scans, agreement of nodule volumes was highest for FBP Br40 (ICC = 0.995), FBP Bl64 (ICC = 0.939) and ADMIRE-5 Bl64 (ICC = 0.994) reconstructions. ADMIRE-3 reconstructions exhibited reduced interscan agreement of nodule volumes (ICCs from 0.788 - 0.882).

CONCLUSIONS

The interscan agreement of node volumes between SD and ULD is high depending on the choice of kernel and reconstruction algorithm. However, caution should be exercised when comparing two image series that were not identically reconstructed.

Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of ‘non visual’ markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID‐19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x‐ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra‐low‐dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon‐counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X‐ray velocimetry integrates x‐ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.

See relatedEditorial

Evaluation of Ultra-Low-Dose Chest Computed Tomography Images in Detecting Lung Lesions Related to COVID-19: A Prospective Study

Background

The present study aimed to evaluate the effectiveness of ultra-low-dose (ULD) chest computed tomography (CT) in comparison with the routine dose (RD) CT images in detecting lung lesions related to COVID-19.

Methods

A prospective study was conducted during April-September 2020 at Shahid Faghihi Hospital affiliated with Shiraz University of Medical Sciences, Shiraz, Iran. In total, 273 volunteers with suspected COVID-19 participated in the study and successively underwent RD-CT and ULD-CT chest scans. Two expert radiologists qualitatively evaluated the images. Dose assessment was performed by determining volume CT dose index, dose length product, and size-specific dose estimate. Data analysis was performed using a ranking test and kappa coefficient (κ). P<0.05 was considered statistically significant.

Results

Lung lesions could be detected with both RD-CT and ULD-CT images in patients with suspected or confirmed COVID-19 (κ=1.0, P=0.016). The estimated effective dose for the RD-CT protocol was 22-fold higher than in the ULD-CT protocol. In the case of the ULD-CT protocol, sensitivity, specificity, accuracy, and positive predictive value for the detection of consolidation were 60%, 83%, 80%, and 20%, respectively. Comparably, in the case of RD-CT, these percentages for the detection of ground-glass opacity (GGO) were 62%, 66%, 66%, and 18%, respectively. Assuming the result of real-time polymerase chain reaction as true-positive, analysis of the receiver-operating characteristic curve for GGO detected using the ULD-CT protocol showed a maximum area under the curve of 0.78.

Conclusion

ULD-CT, with 94% dose reduction, can be an alternative to RD-CT to detect lung lesions for COVID-19 diagnosis and follow-up.An earlier preliminary report of a similar work with a lower sample size was submitted to the arXive as a preprint. The preprint is cited as: https://arxiv.org/abs/2005.03347.

Liver Attenuation Assessment in Reduced Radiation Chest Computed Tomography

OBJECTIVE

This study aimed to evaluate the reliability of liver and spleen Hounsfield units (HU) measurements in reduced radiation computed tomography (RRCT) of the chest within the sub-millisievert range.

METHODS

We performed a prospective, institutional review board-approved study of accrued patients who underwent unenhanced normal-dose chest CT (NDCT) and with an average radiation dose of less than 5% of NDCT. In-house artificial intelligence-based denoising methods produced 2 denoised RRCT (dRRCT) series. Hepatic and splenic attenuations were measured on all 4 series: NDCT, RRCT, dRRCT1, and dRRCT2. Statistical analyses assessed the differences between the HU measurements of the liver and spleen in RRCTs and NDCT. As a test case, we assessed the performance of RRCTs for fatty liver detection, considering NDCT to be the reference standard.

RESULTS

Wilcoxon test compared liver and spleen attenuation in the 72 patients included in our cohort. The liver attenuation in NDCT (median, 59.38 HU; interquartile range, 55.00-66.06 HU) was significantly different from the attenuation in RRCT, dRRCT1, and dRRCT2 (median, 63.63, 42.00, and 33.67 HU; interquartile range, 56.19-67.19, 37.33-45.83, and 30.33-38.50 HU, respectively), all with a P value <0.01. Six patients (8.3%) were considered to have fatty liver on NDCT. The specificity, sensitivity, and accuracy of fatty liver detection by RRCT were greater than 98.5%, 50%, and 94.3%, respectively.

CONCLUSIONS

Attenuation measurements were significantly different between NDCT and RRCTs, but may still have diagnostic value in appreciating hepatosteastosis. Abdominal organ attenuation on RRCT protocols may differ from attenuation on NDCT and should be validated when new low-dose protocols are used.

Spanish Society of Thoracic Surgery (SECT) consensus document. Long-term follow-up for operated patients with lung cancer

The most effective treatment for lung cancer is complete lung resection, although recurrences reach up to 10% and the appearance of second neoplasms, up to 6%. Therefore, the follow-up of these patients will be essential for the early detection and treatment of these events; however there is no definition of the form, time and cadence of these follow-ups. In this consensus document, we try to define them based on the available scientific evidence. A critical review of the literature is carried out (meta-analysis, systematic reviews, reviews, consensus recommendations of scientific societies, randomized controlled studies, non-randomized controlled studies, observational studies and case series studies) and communications to the main congresses on oncology and thoracic surgery in Spanish, English and French. The evidences found are classified following the GRADE system. It is defined according to the existing evidence that the patient resected for lung cancer should be followed up, as well as that this follow-up should be close during the first years and with CT (not being necessary to follow up with PET-CT, biomarkers or bronchoscopy). Cessation of smoking is also recommended in this follow-up.

Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology

Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices.

Analysis of high-resolution reconstruction of medical images based on deep convolutional neural networks in lung cancer diagnostics

BACKGROUND AND OBJECTIVE

To study the diagnostic effect of 64-slice spiral CT and MRI high-resolution images based on deep convolutional neural networks(CNN) in lung cancer.

METHODS

In this paper, we Select 74 patients with highly suspected lung cancer who were treated in our hospital from January 2017 to January 2021 as the research objects. The enhanced 64-slice spiral CT and MRI were used to detect and diagnose respectively, and the images and accuracy of CT diagnosis and MRI diagnosis were retrospectively analyzed.

RESULTS

The accuracy of CT diagnosis is 94.6% (70/74), and the accuracy of MRI diagnosis is 89.2% (66/74). CT examination has the advantages of non-invasive, convenient operation and fast examination. MRI is showing there are advantages in the relationship between the chest wall and the mediastinum, and the relationship between the lesion and the large blood vessels.

CONCLUSION

Enhanced CT and MRI examinations based on convolutional neural networks(CNN) to improve image clarity have high application value in the diagnosis of lung cancer patients, but the focus of performance is different.

Evaluation of ultralow-dose computed tomography on detection of pulmonary nodules in overweight or obese adult patients

Purpose

To evaluate the accuracy of pulmonary nodule (PN) detection in overweight or obese adult patients using ultralow‐dose computed tomography (ULDCT) with tin filtration at 100 kV and advanced model‐based iterative reconstruction (ADMIRE).

Methods

Eighty‐one patients with body mass indices of ≥25 kg/m² were enrolled. All patients underwent low‐dose chest CT (LDCT), followed by ULDCT. Two radiologists experienced in LDCT established the standard of reference (SOR) for PNs. The number, type, size, and location of PNs were identified in the SOR. Effective dose, objective image quality (IQ), and subjective IQ based on two radiologists’ scores were compared between ULDCT and LDCT. The detection performances of radiologists based on ULDCT were calculated according to the nodule analyses. Logistic regression was used to test for independent predictors of PN detection sensitivity.

Results

Both the effective dose and objective IQ were lower for ULDCT than for LDCT (both p < 0.001). Both radiologists rated the subjective IQ of the overall IQ on ULDCT to be diagnostically sufficient. In total, 234 nodules (mean diameter, 3.4 ± 1.9 mm) were classified into 32 subsolid, 149 solid, and 53 calcified nodules according to the SOR. The overall sensitivity of ULDCT for nodule detection was 93.6%. Based on multivariate analyses, the nodule types (p = 0.015) and sizes (p = 0.013) were independent predictors of nodule detection.

Conclusions

Compared with LDCT, ULDCT with tin filtration at 100 kV and ADMIRE could significantly reduce the radiation dose in overweight or obese patients while maintaining good sensitivity for nodule detection.

Detection of pulmonary nodules with scoutless fixed-dose ultra-low-dose CT: a prospective study

OBJECTIVES

To determine the accuracy of scoutless, fixed-dose ultra-low-dose (ULD) CT compared to standard-dose (SD) CT for pulmonary nodule detection and semi-automated nodule measurement, across different patient sizes.

METHODS

Sixty-three patients underwent ULD and SD CT. Two readers examined all studies visually and with computer-aided detection (CAD). Nodules detected on SD CT were included in the reference standard by consensus and stratified into 4 categories (nodule category, NODCAT) from the Dutch-Belgian Lung Cancer Screening trial (NELSON). Effects of NODCAT and patient size on nodule detection were determined. For each nodule, volume and diameter were compared between both scans.

RESULTS

The reference standard comprised 173 nodules. For both readers, detection rates on ULD versus SD CT were not significantly different for NODCAT 3 and 4 nodules > 50 mm³ (reader 1: 93% versus 89% (p = 0.257); reader 2: 96% versus 98% (p = 0.317)). For NODCAT 1 and 2 nodules < 50 mm³, detection rates on ULD versus SD CT dropped significantly (reader 1: 66% versus 80% (p = 0.023); reader 2: 77% versus 87% (p = 0.039)). Body mass index and chest circumference did not influence nodule detectability (p = 0.229 and p = 0.362, respectively). Calculated volumes and diameters were smaller on ULD CT (p < 0.0001), without altering NODCAT (84% agreement).

CONCLUSIONS

Scoutless ULD CT reliably detects solid lung nodules with a clinically relevant volume (> 50 mm³) in lung cancer screening, irrespective of patient size. Since detection rates were lower compared to SD CT for nodules < 50 mm³, its use for lung metastasis detection should be considered on a case-by-case basis.

KEY POINTS

• Detection rates of pulmonary nodules > 50 mm³are not significantly different between scoutless ULD and SD CT (i.e. volumes clinically relevant in lung cancer screening based on the NELSON trial), but were different for the detection of nodules < 50 mm³(i.e. volumes still potentially relevant in lung metastasis screening). • Calculated nodule volumes were on average 0.03 mL or 9% smaller on ULD CT, which is below the 20-25% interscan variability previously reported with software-based volumetry. • Even though a scoutless, fixed-dose ULD CT protocol was used (CTDI_vol0.15 mGy), pulmonary nodule detection was not influenced by patient size.

Personalized Chest Computed Tomography: Minimum Diagnostic Radiation Dose Levels for the Detection of Fibrosis, Nodules, and Pneumonia

OBJECTIVES

The purpose of this study was to evaluate the minimum diagnostic radiation dose level for the detection of high-resolution (HR) lung structures, pulmonary nodules (PNs), and infectious diseases (IDs).

MATERIALS AND METHODS

A preclinical chest computed tomography (CT) trial was performed with a human cadaver without known lung disease with incremental radiation dose using tin filter-based spectral shaping protocols. A subset of protocols for full diagnostic evaluation of HR, PN, and ID structures was translated to clinical routine. Also, a minimum diagnostic radiation dose protocol was defined (MIN). These protocols were prospectively applied over 5 months in the clinical routine under consideration of the individual clinical indication. We compared radiation dose parameters, objective and subjective image quality (IQ).

RESULTS

The HR protocol was performed in 38 patients (43%), PN in 21 patients (24%), ID in 20 patients (23%), and MIN in 9 patients (10%). Radiation dose differed significantly among HR, PN, and ID (5.4, 1.2, and 0.6 mGy, respectively; P < 0.001). Differences between ID and MIN (0.2 mGy) were not significant (P = 0.262). Dose-normalized contrast-to-noise ratio was comparable among all groups (P = 0.087). Overall IQ was perfect for the HR protocol (median, 5.0) and decreased for PN (4.5), ID-CT (4.3), and MIN-CT (2.5). The delineation of disease-specific findings was high in all dedicated protocols (HR, 5.0; PN, 5.0; ID, 4.5). The MIN protocol had borderline IQ for PN and ID lesions but was insufficient for HR structures. The dose reductions were 78% (PN), 89% (ID), and 97% (MIN) compared with the HR protocols.

CONCLUSIONS

Personalized chest CT tailored to the clinical indications leads to substantial dose reduction without reducing interpretability. More than 50% of patients can benefit from such individual adaptation in a clinical routine setting. Personalized radiation dose adjustments with validated diagnostic IQ are especially preferable for evaluating ID and PN lesions.

Metal Artifact Reduction With Tin Prefiltration in Computed Tomography: A Cadaver Study for Comparison With Other Novel Techniques

OBJECTIVES

With the aging population and thus rising numbers of orthopedic implants (OIs), metal artifacts (MAs) increasingly pose a problem for computed tomography (CT) examinations. In the study presented here, different MA reduction techniques (iterative metal artifact reduction software [iMAR], tin prefilter technique, and dual-energy CT [DECT]) were compared.

MATERIALS AND METHODS

Four human cadaver pelvises with OIs were scanned on a third-generation DECT scanner using tin prefilter (Sn), dual-energy (DE), and conventional protocols. Virtual monoenergetic CT images were generated from DE data sets. Postprocessing of CT images was performed using iMAR. Qualitative (bony structures, MA, image noise) image analysis using a 6-point Likert scale and quantitative image analysis (contrast-to-noise ratio, standard deviation of background noise) were performed by 2 observers. Statistical testing was performed using Friedman test with Nemenyi test as a post hoc test.

RESULTS

The iMAR Sn 150 kV protocol provided the best overall assessability of bony structures and the lowest subjective image noise. The iMAR DE protocol and virtual monochromatic image (VMI) ± iMAR achieved the most effective metal artifact reduction (MAR) (P < 0.05 compared with conventional protocols). Bony structures were rated worse in VMI ± iMAR (P < 0.05) than in tin prefilter protocols ± iMAR. The DE protocol ± iMAR had the lowest contrast-to-noise ratio (P < 0.05 compared with iMAR standard) and the highest image noise (P < 0.05 compared with iMAR VMI). The iMAR reduced MA very efficiently.

CONCLUSIONS

When considering MAR and image quality, the iMAR Sn 150 kV protocol performed best overall in CT images with OI. The iMAR generated new artifacts that impaired image quality. The DECT/VMI reduced MA best, but experienced from a lack of resolution of bony fine structures.

Low-dose CT with tin filter combined with iterative metal artefact reduction for guiding lung biopsy

Background

Computed tomography (CT) is currently the imaging modality of choice for guiding pulmonary percutaneous procedures. The use of a tin filter allows low-energy photons to be absorbed which contribute little to image quality but increases the radiation dose that a patient receives. Iterative metal artefact reduction (iMAR) was developed to diminish metal artefacts. This study investigated the impact of using tin filtration combined with an iMAR algorithm on dose reduction and image quality in CT-guided lung biopsy.

Methods

Ninety-nine consecutive patients undergoing CT-guided lung biopsy were randomly assigned to routine-dose CT protocols (groups A and B; without and with iMAR, respectively) or tin filter CT protocols (groups C and D; without or with iMAR, respectively). Subjective image quality was analysed using a 5-point Likert scale. Objective image quality was assessed, and the noise, contrast-to-noise ratio, and figure of merit were compared among the four groups. Metal artefacts were quantified using CT number reduction and metal diameter blurring. The radiation doses, diagnostic performance, and complication rates were also estimated.

Results

The subjective image quality of the two scan types was compared. Images with iMAR reconstruction were superior to those without iMAR reconstruction (group A: 3.49±0.65 vs. group B: 4.63±0.57; P<0.001, and group C: 3.88±0.66 vs. group D: 4.82±0.39; P<0.001). Images taken with a tin filter were found to have a significantly higher figure-of-merit than those taken without a tin filter (group A: 14,041±7,230 vs. group C: 21,866±10,656; P=0.001, and group B: 13,836±6,849 vs. group D: 21,639±9,964; P=0.001). In terms of metal artefact reduction, tin filtration combined with iMAR showed the lowest CT number reduction (116.62±103.48 HU) and metal diameter blurring (0.85±0.30) among the protocols. The effective radiation dose in the tin filter groups was 73.2% lower than that in the routine-dose groups. The complication rate and diagnostic performance (sensitivity, specificity, and overall accuracy) did not differ significantly between the tin filter and routine-dose groups (all P>0.05).

Conclusions

Tin filtration combined with an iMAR algorithm may reduce the radiation dose compared to the routine-dose CT protocol, while maintaining comparable diagnostic accuracy and image quality and producing fewer metal artefacts.

First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels

OBJECTIVE

The aim of this study was to evaluate the image quality (IQ) and performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system in photon-counting detector computed tomography (PCD-CT) for pulmonary nodule evaluation at different low-dose levels.

MATERIALS AND METHODS

An anthropomorphic chest-phantom containing 14 pulmonary nodules of different sizes (range, 3-12 mm) was imaged on a PCD-CT and on a conventional energy-integrating detector CT (EID-CT). Scans were performed with each of the 3 vendor-specific scanning modes (QuantumPlus [Q+], Quantum [Q], and High Resolution [HR]) at decreasing matched radiation dose levels (volume computed tomography dose index ranging from 1.79 to 0.31 mGy) by adapting IQ levels from 30 to 5. Image noise was measured manually in the chest wall at 8 different locations. Subjective IQ was evaluated by 2 readers in consensus. Nodule detection and volumetry were performed using a commercially available AI-CAD system.

RESULTS

Subjective IQ was superior in PCD-CT compared with EID-CT (P < 0.001), and objective image noise was similar in the Q+ and Q-mode (P > 0.05) and superior in the HR-mode (PCD 55.8 ± 11.7 HU vs EID 74.8 ± 5.4 HU; P = 0.01). High resolution showed the lowest image noise values among PCD modes (P = 0.01). Overall, the AI-CAD system delivered comparable results for lung nodule detection and volumetry between PCD- and dose-matched EID-CT (P = 0.08-1.00), with a mean sensitivity of 95% for PCD-CT and of 86% for dose-matched EID-CT in the lowest evaluated dose level (IQ5). Q+ and Q-mode showed higher false-positive rates than EID-CT at lower-dose levels (IQ10 and IQ5). The HR-mode showed a sensitivity of 100% with a false-positive rate of 1 even at the lowest evaluated dose level (IQ5; CDTIvol, 0.41 mGy).

CONCLUSIONS

Photon-counting detector CT was superior to dose-matched EID-CT in subjective IQ while showing comparable to lower objective image noise. Fully automatized AI-aided nodule detection and volumetry are feasible in PCD-CT, but attention has to be paid to false-positive findings.

Photon Starvation Artifact Reduction by Shift-Variant Processing

The x-ray computed tomography (CT) images with low dose are noisy and may contain photon starvation artifacts. The artifacts are location and direction dependent. Therefore, the common shift-invariant denoising filters do not work well. The state-of-the-art methods to process the low-dose CT images are image reconstruction based; they require the raw projection data. In many situations, the raw CT projections are not accessible. This paper suggests a method to denoise the low-dose CT image using the pseudo projections generated by the application of a forward projector on the low-dose CT image. The feasibility of the proposed method is demonstrated by real clinical data.

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT

Background Ultra-low-dose (ULD) CT could facilitate the clinical implementation of large-scale lung cancer screening while minimizing the radiation dose. However, traditional image reconstruction methods are associated with image noise in low-dose acquisitions. Purpose To compare the image quality and lung nodule detectability of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) in ULD CT. Materials and Methods Patients who underwent noncontrast ULD CT (performed at 0.07 or 0.14 mSv, similar to a single chest radiograph) and contrast-enhanced chest CT (CECT) from April to June 2020 were included in this prospective study. ULD CT images were reconstructed with filtered back projection (FBP), ASIR-V, and DLIR. Three-dimensional segmentation of lung tissue was performed to evaluate image noise. Radiologists detected and measured nodules with use of a deep learning-based nodule assessment system and recognized malignancy-related imaging features. Bland-Altman analysis and repeated-measures analysis of variance were used to evaluate the differences between ULD CT images and CECT images. Results A total of 203 participants (mean age ± standard deviation, 61 years ± 12; 129 men) with 1066 nodules were included, with 100 scans at 0.07 mSv and 103 scans at 0.14 mSv. The mean lung tissue noise ± standard deviation was 46 HU ± 4 for CECT and 59 HU ± 4, 56 HU ± 4, 53 HU ± 4, 54 HU ± 4, and 51 HU ± 4 in FBP, ASIR-V level 40%, ASIR-V level 80% (ASIR-V-80%), medium-strength DLIR, and high-strength DLIR (DLIR-H), respectively, of ULD CT scans (P < .001). The nodule detection rates of FBP reconstruction, ASIR-V-80%, and DLIR-H were 62.5% (666 of 1066 nodules), 73.3% (781 of 1066 nodules), and 75.8% (808 of 1066 nodules), respectively (P < .001). Bland-Altman analysis showed the percentage difference in long diameter from that of CECT was 9.3% (95% CI of the mean: 8.0, 10.6), 9.2% (95% CI of the mean: 8.0, 10.4), and 6.2% (95% CI of the mean: 5.0, 7.4) in FBP reconstruction, ASIR-V-80%, and DLIR-H, respectively (P < .001). Conclusion Compared with adaptive statistical iterative reconstruction-V, deep learning image reconstruction reduced image noise, increased nodule detection rate, and improved measurement accuracy on ultra-low-dose chest CT images. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee in this issue.

Spanish Society of Thoracic Surgery (SECT) consensus document. Long-term follow-up for operated patients with lung cancer

The most effective treatment for lung cancer is complete lung resection, although recurrences reach up to 10% and the appearance of second neoplasms, up to 6%. Therefore, the follow-up of these patients will be essential for the early detection and treatment of these events; however, there is no definition of the form, time and cadence of these follow-ups. In this consensus document, we try to define them based on the available scientific evidence. A critical review of the literature is carried out (meta-analysis, systematic reviews, reviews, consensus recommendations of scientific societies, randomized controlled studies, non-randomized controlled studies, observational studies and case series studies) and communications to the main congresses on oncology and thoracic surgery in Spanish, English and French. The evidences found are classified following the GRADE system. It is defined according to the existing evidence that the patient resected for lung cancer should be followed up, as well as that this follow-up should be close during the first years and with CT (not being necessary to follow up with PET-CT, biomarkers or bronchoscopy). Cessation of smoking is also recommended in this follow-up.

Pneumonia Detection in Chest X-Ray Dose-Equivalent CT: Impact of Dose Reduction on Detectability by Artificial Intelligence

RATIONALE AND OBJECTIVES

There has been a significant increase of immunocompromised patients in recent years due to new treatment modalities for previously fatal diseases. This comes at the cost of an elevated risk for infectious diseases, most notably pathogens affecting the respiratory tract. Because early diagnosis and treatment of pneumonia can help reducing morbidity and mortality, we assessed the performance of a deep neural network in the detection of pulmonary infection in chest X-ray dose-equivalent computed tomography (CT).

MATERIALS AND METHODS

The 100 patients included in this retrospective study were referred to our department for suspicion of pulmonary infection and/or follow-up of known pulmonary nodules. Every patient was scanned with a standard dose (1.43 ± 0.54 mSv) and a 20 times dose-reduced (0.07 ± 0.03 mSv) CT protocol. We trained a deep neural network to perform binary classification (pulmonary consolidation present or not) and assessed diagnostic performance on both standard dose and reduced dose CT images.

RESULTS

The areas under the curve of the deep learning algorithm for the standard dose CT was 0.923 (confidence interval [CI] 95%: 0.905-0.941) and significantly higher than the areas under the curve (0.881, CI 95%: 0.859-0.903) of the reduced dose CT (p = 0.001). Sensitivity and specificity of the standard dose CT was 82.9% and 93.8%, and of the reduced dose CT 71.0% and 93.3%.

CONCLUSION

Pneumonia detection with X-ray dose-equivalent CT using artificial intelligence is feasible and may contribute to a more robust and reproducible diagnostic performance. Dose reduction lowered the performance of the deep neural network, which calls for optimization and adaption of CT protocols when using AI algorithms at reduced doses.

Impact of Morphotype on Image Quality and Diagnostic Performance of Ultra-Low-Dose Chest CT

Objectives: The image quality of an Ultra-Low-Dose (ULD) chest CT depends on the patient’s morphotype. We hypothesize that there is a threshold beyond which the diagnostic performance of a ULD chest CT is too degraded. This work assesses the influence of morphotype (Body Mass Index BMI, Maximum Transverse Chest Diameter MTCD and gender) on image quality and the diagnostic performance of a ULD chest CT. Methods: A total of 170 patients from three prior prospective monocentric studies were retrospectively included. Renewal of consent was waived by our IRB. All the patients underwent two consecutive unenhanced chest CT acquisitions with a full dose (120 kV, automated tube current modulation) and a ULD (135 kV, fixed tube current at 10 mA). Image noise, subjective image quality and diagnostic performance for nine predefined lung parenchyma lesions were assessed by two independent readers, and correlations with the patient’s morphotype were sought. Results: The mean BMI was 26.6 ± 5.3; 20.6% of patients had a BMI > 30. There was a statistically significant negative correlation of the BMI with the image quality (ρ = −0.32; IC95% = (−0.468; −0.18)). The per-patient diagnostic performance of ULD was sensitivity, 77%; specificity, 99%; PPV, 94% and NPV, 65%. There was no statistically significant influence of the BMI, the MTCD nor the gender on the per-patient and per-lesion diagnostic performance of a ULD chest CT, apart from a significant negative correlation for the detection of emphysema. Conclusions: Despite a negative correlation between the BMI and the image quality of a ULD chest CT, we did not find a correlation between the BMI and the diagnostic performance of the examination, suggesting a possible use of the ULD protocol in obese patients.

Comparison of 0.3-mSv CT to Standard-Dose CT for Detection of Lung Nodules in Children and Young Adults With Cancer

Background: CT is the imaging modality of choice to identify lung metastasis. Objective: The purpose of this study was to evaluate the performance of reduced-dose CT for detection of lung nodules in children and young adults with cancer. Methods: This prospective study enrolled patients 4-21 years old with known or suspected malignancy who were undergoing clinically indicated chest CT. Study participants underwent an additional investigational reduced-dose chest CT in the same imaging encounter. Separated deidentified CT examinations were reviewed in blinded fashion by three independent radiologists. One reviewer performed a subsequent secondary review to match nodules between the standard- and reduced-dose examinations. Diagnostic performance was computed for the reduced-dose examinations, using clinical examinations as reference standard. Intraobserver and interobserver agreement were calculated using Cohen's Kappa. Results: A total of 78 patients (44 male, 34 female; mean age 15.2±3.8 years) were enrolled. Mean estimated effective dose was 1.8±1.1 mSv for clinical CT and 0.3±0.1 mSv for reduced-dose CT, an 83% reduction. Forty-five (58%) patients had 162 total lung nodules (mean size 3.4±3.3 mm) detected on the clinical CT examinations. A total of 92% of nodules were visible on reduced-dose CT. Sensitivity and specificity of reduced-dose CT for nodules ranged from 63%-77% and 80%-90% respectively across the three reviewers. Intraobserver agreement between clinical and reduced-dose CT was moderate to substantial for presence of nodules (κ=0.45-0.67), and good to excellent for number of nodules (κ=0.68-0.84) and nodule size (κ=0.69-0.86). Interobserver agreement for the presence of nodules was moderate for both reduced-dose (κ=0.53) and clinical (κ=0.54) CT. A median of 1 nodule was present on clinical CT in patients with a falsely negative reduced-dose CT examination. Conclusion: Reduced-dose CT depicts greater than 90% of lung nodules in children and young adults with cancer. Reviewers identified the presence of nodules with moderate sensitivity and high specificity. Clinical Impact: CT performed at 0.3 mSv mean effective dose has acceptable diagnostic performance for lung nodule detection in children and young adults and has the potential to reduce patient dose or expand CT utilization (e.g., to replace radiography in screening or monitoring protocols).

Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules

Background

The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT.

Methods

Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for all features between ULDCT and LDCT were also compared.

Results

Sixty-three solid nodules (SNs) and 48 pure ground-glass nodules (pGGNs) were analyzed. There was no difference for the proportion of features in SNs among ULDCTs and between ULDCT and LDCT (P>0.05). The proportion of features in pGGNs were highest for ULDCT_{70% vs. 90%} (78.5%) and ULDCT_90% vs. LDCT_50% (50.5%). In SNs, the mean CCC for ULDCT_90% vs. LDCT_50% was 0.67±0.26, not different with that for ULDCT_50% vs. LDCT_50% (0.68±0.24) and ULDCT_70% vs. LDCT_50% (0.64±0.21) (P>0.05). In pGGNs, the mean CCC for ULDCT_90% vs. LDCT_50% was 0.79±0.19, higher than that for ULDCT_50% vs. LDCT_50% (0.61±0.28) and ULDCT_70% vs. LDCT_50% (0.76±0.24) (P<0.05).

Conclusions

ASiR-V levels significantly affected ULDCT radiomic feature quantification in pulmonary nodules, with stronger effects in pGGNs than in SNs. The reproducibility of radiomic features was highest between ULDCT_90% and LDCT_50%.

A pilot study on lung cancer detection based on regional metabolic activity distribution in digital low-dose 18F-FDG PET

OBJECTIVES

To investigate the potential of automatic lung cancer detection on submillisievert dose ¹⁸F-fludeoxyglucose (¹⁸F-FDG) scans using different positron emission tomography (PET) parameters, as a primary step towards a potential new indication for ¹⁸F-FDG PET in lung cancer screening.

METHODS

We performed a retrospective cohort analysis with 83 patients referred for ¹⁸F-FDG PET/CT, including of 34 patients with histology-proven lung cancer and 49 patients without lung disease. Aside clinical standard PET images (PET_100%) two additional low-dose PET reconstructions were generated, using only 15 s and 5 s of the 150 s list mode raw data of the full-dose PET, corresponding to 10% and 3.3% of the original ¹⁸F-FDG activity. The lungs were subdivided into three segments on each side, and each segment was classified as normal or containing cancer. The following standardized uptake values (SUVs) were extracted from PET per lung segment: SUV_mean, SUV_hot5, SUV_median, SUV_std and SUV_total. A multivariate linear regression model was used and cross-validated. The accuracy for lung cancer detection was tested with receiver operating characteristics analysis and T-statistics was used to calculate the weight of each parameter.

RESULTS

The T-statistics showed that SUV_std was the most important discriminative factor for lung cancer detection. The multivariate model achieved an area under the curve of 0.97 for full-dose PET, 0.85 for PET_10% with PET_3.3% reconstructions resulting in a still high sensitivity the PET_10% reconstruction of 80%.

CONCLUSION

This pilot study indicates that segment-based, quantitative PET parameters of low-dose PET reconstructions could be used to automatically detect lung cancer with high sensitivity.

ADVANCES IN KNOWLEDGE

Automated assessment of PET parameters in low-dose PET may aid for an early detection of lung cancer.

Potential for Radiation Dose Reduction in Dual-Source Computed Tomography of the Lung in the Pediatric and Adolescent Population Compared to Digital Radiography

Low-dose dual-source computed tomography (DSCT) protocols for the evaluation of lung diseases in children and adolescents are of importance since this age group is particularly prone to radiation damage. The aim of this study was to evaluate image quality of low-dose DSCT of the lung and to assess the potential of radiation dose reduction compared to digital radiographs (DR). Three groups, each consisting of 19 patients, were examined with different DSCT protocols using tin prefiltration (Sn96/64/32 ref. mAs at 100 kV). Different strengths of iterative reconstruction were applied (ADMIRE 2/3/4). DSCT groups were compared to 19 matched patients examined with posterior–anterior DR. Diagnostic confidence, detectability of anatomical structures and small lung lesions were evaluated on a 4-point Likert scale (LS 1 = unacceptable, 4 = fully acceptable; a value ≥ 3 was considered acceptable). Effective dose (ED) was 31-/21-/9-fold higher in Sn96/Sn64/Sn32 compared to DR. Diagnostic confidence was sufficient in Sn96/Sn64 (LS 3.4/3.2), reduced in Sn32 (LS 2.7) and the worst in DR (LS 2.4). In DSCT, detectability of small anatomical structures was always superior to DR (p < 0.05). Mean lesion size ranged from 5.1–7 mm; detectability was acceptable in all DSCT groups (LS 3.0–3.4) and superior to DR (LS 1.9; p < 0.05). Substantial dose lowering in DSCT of the pediatric lung enables acceptable detectability of small lung lesions with a radiation dose being about 10-fold higher compared to DR.

Latest CT technologies in lung cancer screening: protocols and radiation dose reduction

The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in post-processing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols.

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques

OBJECTIVE

To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT.

METHODS

Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDI_vol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas.

RESULTS

DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows.

CONCLUSION

Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers.

KEY POINTS

• A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

Diagnostic accuracy of low-dose and ultra-low-dose CT in detection of chest pathology: a systematic review

PURPOSE

Studies have evaluated imaging modalities with a lower radiation dose than standard-dose CT (SD-CT) for chest examination. This systematic review aimed to summarize evidence on diagnostic accuracy of these modalities - low-dose and ultra-low-dose CT (LD- and ULD-CT) - for chest pathology.

METHOD

Ovid-MEDLINE, Ovid-EMBASE and the Cochrane Library were systematically searched April 29th-30th, 2019 and screened by two reviewers. Studies on diagnostic accuracy were included if they defined their index tests as 'LD-CT', 'Reduced-dose CT' or 'ULD-CT' and had SD-CT as reference standard. Risk of bias was evaluated on study level using the Quality Assessment of Diagnostic Accuracy Studies-2. A narrative synthesis was conducted to compare the diagnostic accuracy measurements.

RESULTS

Of the 4257 studies identified, 18 were eligible for inclusion. SD-CT (3.17 ± 1.47 mSv) was used as reference standard in all studies to evaluate diagnostic accuracy of LD- (1.22 ± 0.34 mSv) and ULD-CT (0.22 ± 0.05 mSv), respectively. LD-CT had high sensitivities for detection of bronchiectasis (82-96%), honeycomb (75-100%), and varying sensitivities for nodules (63-99%) and ground glass opacities (GGO) (77-91%). ULD-CT had high sensitivities for GGO (93-100%), pneumothorax (100%), consolidations (90-100%), and varying sensitivities for nodules (60-100%) and emphysema (65-90%).

CONCLUSION

The included studies found LD-CT to have high diagnostic accuracy in detection of honeycombing and bronchiectasis and ULD-CT to have high diagnostic accuracy for pneumothorax, consolidations and GGO. Summarizing evidence on diagnostic accuracy of LD- and ULD-CT for other chest pathology was not possible due to varying outcome measures, lack of precision estimates and heterogeneous study design and methodology.

Ultra-low-dose CT reconstructed with ASiR-V using SmartmA for pulmonary nodule detection and Lung-RADS classifications compared with low-dose CT

AIM

To evaluate the accuracy of ultra-low-dose computed tomography (ULDCT) with ASiR-V using a noise index (SmartmA) for pulmonary nodule detection and Lung CT Screening Reporting And Data System (Lung-RADS) classifications compared with low-dose CT (LDCT).

MATERIALS AND METHODS

Two-hundred and ten patients referred for lung cancer screening underwent conventional chest LDCT (0.80 ± 0.28 mSv) followed immediately by ULDCT (0.16 ± 0.03 mSv). ULDCT was scanned using 120 kV/SmartmA with a noise index of 28 HU and reconstructed with ASiR-V70%. The types and diameters of all nodules were recorded. The attenuation of pure ground-glass nodules (pGGNs) was measured on LDCT. All nodules were further classified using Lung-RADS. Sensitivities of nodule detection on ULDCT were analysed using LDCT as the reference standard. Logistic regression was used to establish a prediction model for the sensitivity of nodules.

RESULTS

LDCT revealed 362 nodules and the overall sensitivity on ULDCT was 90.1%. The sensitivity for solid nodules (SNs) of ≥1 mm diameter was 96.6% (228/236) and 100% (26/26) for SNs of ≥6 mm diameter. For pGGNs of ≥6 mm, the overall sensitivity was 93% (40/43) and 100% (29/29) for nodules with a attenuation value -700 HU or more. The agreement of Lung-RADS classification between two scans was good. On logistic regression, diameter was the only independent predictor for sensitivity of SNs (p<0.05). Diameter and attenuation value were predictors for pGGNs (p<0.05).

CONCLUSION

ULDCT with ASiR-V using SmartmA is suitable for lung-cancer screening in people with a BMI ≤35 kg/m² as it has a low radiation dose of 0.16 mSv, high sensitivity for nodule detection and good performance of Lung-RADS classifications.

European Respiratory Society International Congress 2020: highlights from best-abstract awardees

#ERSCongress 2020 best-abstract awardees summarise their virtual European Respiratory Society International Congress experience and views on the evolving field of research for their respective assembly https://bit.ly/3kJ9JrJ.

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation

OBJECTIVE. The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). MATERIALS AND METHODS. Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images were reconstructed using hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR), and they were postprocessed using DLD (i.e., HIR-DLD and MBIR-DLD). SDCT images were reconstructed using filtered back projection. Three independent radiologists subjectively evaluated HIR, HIR-DLD, MBIR, and MBIR-DLD images on a 5-point scale in terms of noise, streak artifact, nodule edge, clarity of small vessels, homogeneity of the normal lung parenchyma, and overall image quality. Two radiologists independently evaluated the nodules according to Lung-RADS using HIR, MBIR, HIR-DLD, and MBIR-DLD ULDCT images and SDCT images. The median scores for subjective analysis were analyzed using Wilcoxon signed rank test with Bonferroni correction. Intraobserver agreement for Lung-RADS category between ULDCT and SDCT was evaluated using the weighted kappa coefficient. RESULTS. In the subjective analysis, ULDCT with DLD showed significantly better scores than did ULDCT without DLD (p < 0.001), and MBIR-DLD showed the best scores among the ULDCT images (p < 0.001) for all items. In the Lung-RADS evaluation, HIR showed fair or moderate agreement (reader 1 and reader 2: κw = 0.46 and 0.32, respectively); MBIR, moderate or good agreement (κw = 0.68 and 0.57); HIR-DLD, moderate agreement (κw = 0.53 and 0.48); and MBIR-DLD, good agreement (κw = 0.70 and 0.72). CONCLUSION. DLD improved the image quality of both HIR and MBIR on ULDCT. MBIR-DLD was superior to HIR_DLD for image quality and for Lung-RADS evaluation.

Comparison of submillisievert CT with standard-dose CT for urolithiasis

BACKGROUND

Patients with renal stones receive multiple computed tomography (CT) examinations. We investigated whether submillisievert (sub-mSv) CT for stone detection could reduce radiation dose at exposure levels comparable to kidney, ureter, and bladder (KUB) radiography.

PURPOSE

To evaluate the radiation dose exposure, diagnostic performance, and image quality of sub-mSv non-contrast CT using advanced modelled iterative reconstruction algorithm with spectral filtration for the detection of urolithiasis.

MATERIAL AND METHODS

A total of 145 consecutive patients underwent non-contrast CT using a third-generation dual-source scanner to obtain two datasets, i.e. 16.7% (sub-mSv CT, tube detector A) and 100% (standard-dose CT, combination of tube detector A and B) tube loads with spectral filtration. The performance of sub-mSv CT for the detection of stones was analyzed by two readers and compared with that of standard-dose CT. Image quality was measured subjectively and objectively.

RESULTS

In total, 171 stones were detected in 79 patients. The mean effective radiation doses of sub-mSv CT was 0.3 mSv. The sensitivity and specificity values for diagnosis of stones measuring ≥3 mm was 95.1% and 100% for sub-mSv CT. The sensitivity and specificity for all stone detection was 74.9% and 97.8%, respectivey, for sub-mSv CT. The image quality was lower for sub-mSv CT than for standard-dose CT (P < 0.01).

CONCLUSION

Sub-mSv CT can be achieved with radiation doses close to KUB radiography. Sub-mSv CT with spectral filtration can be used to detect stones measuring ≥3 mm and be used as a follow-up imaging modality as an alternative to KUB radiography.

Optimization of acquisition parameters for reduced-dose thoracic CT: A phantom study

PURPOSE

The purpose of this study was to analyze the impact of different options for reduced-dose computed tomography (CT) on image noise and visibility of pulmonary structures in order to define the best choice of parameters when performing ultra-low dose acquisitions of the chest in clinical routine.

MATERIALS AND METHODS

Using an anthropomorphic chest phantom, CT images were acquired at four defined low dose levels (computed tomography dose index [CTDI_vol]=0.15, 0.20, 0.30 and 0.40mGy), by changing tube voltage, pitch factor, or rotation time and adapting tube current to reach the predefined CTDI_vol-values. Images were reconstructed using two different levels of iteration (adaptive statistical iterative reconstruction [ASIR®]-v70% and ASIR®-v100%). Signal-to-noise ratio (SNR) as well as contrast-to-noise ratio (CNR) was calculated. Visibility of pulmonary structures (bronchi/vessels) were assessed by two readers on a 5-point-Likert scale.

RESULTS

Best visual image assessments and CNR/SNR were obtained with high tube voltage, while lowest scores were reached with lower pitch factor followed by high tube current. Protocols favoring lower pitch factor resulted in decreased visibility of bronchi/vessels, especially in the periphery. Decreasing radiation dose from 0.40 to 0.30mGy was not associated with a significant decrease in visual scores (P<0.05), however decreasing radiation dose from 0.30mGy to 0.15mGy was associated with a lower visibility of most of the evaluated structures (P<0.001). While image noise could be significantly reduced when ASIR®-v100% instead of ASIR®-v70% was used, the visibility-scores of pulmonary structures did not change significantly.

CONCLUSION

Favoring high tube voltage is the best option for reduced-dose protocols. A decrease of SNR and CNR does not necessarily go along with reduced visibility of pulmonary structures.

Chest CT screening in patients with overweight or obesity using spectral shaping at 150 kVp: compared with 120 kVp protocol and spectral shaping at 100 kVp protocol

PURPOSE

To evaluate the image quality (IQ) and the figure of merit (FoM) of chest CT screening in patients with overweight or obesity using a tin filter for spectral shaping at 150 kVp.

MATERIALS AND METHODS

Patients with overweight or obesity (N = 150, body mass index ≥ 26 kg/m²) with indications for chest CT screening were prospectively enrolled and randomly divided into three groups: 120 kVp group (standard radiation dose/tube voltage, 120 kVp/CT volume does index, 4.68 mGy); Sn100 kVp group (1/10th radiation dose level/100 kVp with a tin filter/0.47 mGy); Sn150 kVp group (1/2th radiation dose level/150 kVp with a tin filter/2.34 mGy). IQ and FoMs were evaluated and compared among the three groups.

RESULTS

Image noise, signal-to-noise ratios and subjective IQ scores were significantly higher in the Sn150 kVp group than those in the Sn100 kVp group (all p < 0.05), but were not significantly different with those in the 120 kVp group. FoMs in the Sn150 kVp group were significantly higher than those in the 120 kVp group (all p < 0.05), but showed no statistical difference with those in the Sn100 kVp group.

CONCLUSIONS

Compared with scanning at 120 kVp, chest CT screening performed at 150 kVp with spectral shaping substantially reduces the radiation dose in overweight and obese patients while maintaining IQ.

Chest CT in patients after lung transplantation: A retrospective analysis to evaluate impact on image quality and radiation dose using spectral filtration tin-filtered imaging

OBJECTIVES

The purpose of this study was to investigate the impact of a 150kV spectral filtration chest imaging protocol (Sn150kVp) combined with advanced modeled iterative reconstruction (ADMIRE) on radiation dose and image quality in patients after lung-transplantation.

METHODS

This study included 102 patients who had unenhanced chest-CT examinations available on both, a second-generation dual-source CT (DSCT) using standard protocol (100kVp, filtered-back-projection) and, on a third-generation DSCT using Sn150kVp protocol with ADMIRE. Signal-to-noise-ratio (SNR) was measured in 6 standardized regions. A 5-point Likert scale was used to evaluate subjective image quality. Radiation metrics were compared.

RESULTS

The mean time interval between the two acquisitions was 1.1±0.7 years. Mean-volume-CT-dose-index, dose-length-product and effective dose were significantly lower for Sn150kVp protocol (2.1±0.5mGy;72.6±16.9mGy*cm;1.3±0.3mSv) compared to 100kVp protocol (6.2±1.8mGy;203.6±55.6mGy*cm;3.7±1.0mSv) (p<0.001), equaling a 65% dose reduction. All studies were considered of diagnostic quality. SNR measured in lung tissue, air inside trachea, vertebral body and air outside the body was significantly higher in 100kVp protocol compared to Sn150kVp protocol (12.5±2.7vs.9.6±1.5;17.4±3.6vs.11.8±1.8;0.7±0.3vs.0.4±0.2;25.2±6.9vs.14.9±3.3;p<0.001). SNR measured in muscle tissue was significantly higher in Sn150kVp protocol (3.2±0.9vs.2.6±1.0;p<0.001). For SNR measured in descending aorta there was a trend towards higher values for Sn150kVp protocol (2.8±0.6 vs. 2.7±0.9;p = 0.3). Overall SNR was significantly higher in 100kVp protocol (5.0±4.0vs.4.0±4.0;p<0.001). On subjective analysis both protocols achieved a median Likert rating of 1 (25th-75th-percentile:1-1;p = 0.122). Interobserver agreement was good (intraclass correlation coefficient = 0.73).

CONCLUSIONS

Combined use of 150kVp tin-filtered chest CT protocol with ADMIRE allows for significant dose reduction while maintaining highly diagnostic image quality in the follow up after lung transplantation when compared to a standard chest CT protocol using filtered back projection.