Dual-energy CT in the obese: a preliminary retrospective review to evaluate quality and feasibility of the single-source dual-detector implementation

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study

OBJECTIVES

To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.

METHODS

A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).

RESULTS

The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.

CONCLUSION

The radiomics features were non-reproducible with significant variability in values among different CT techniques.

CLINICAL RELEVANCE STATEMENT

Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.

KEY POINTS

CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology.

Dual-Energy Computed Tomography: Technological Considerations

Compared to conventional single-energy CT (SECT), dual-energy CT (DECT) provides additional information to better characterize imaged tissues. Approaches to DECT acquisition vary by vendor and include source-based and detector-based systems, each with its own advantages and disadvantages. Despite the different approaches to DECT acquisition, the most utilized DECT images include routine SECT equivalent, virtual monoenergetic, material density (eg, iodine map), and virtual non-contrast images. These images are generated either through reconstructions in the projection or image domains. Designing and implementing an optimal DECT workflow into routine clinical practice depends on radiologist and technologist input with special considerations including appropriate patient and protocol selection and workflow automation. In addition to better tissue characterization, DECT provides numerous advantages over SECT such as the characterization of incidental findings and dose reduction in radiation and iodinated contrast.

Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects

Gastrointestinal (GI) cancer is the leading cause of cancer-related deaths worldwide. Computed tomography (CT) is an important auxiliary tool for the diagnosis, evaluation, and prognosis prediction of gastrointestinal tumors. Spectral CT is another major CT revolution after spiral CT and multidetector CT. Compared to traditional CT which only provides single-parameter anatomical diagnostic mode imaging, spectral CT can achieve multi-parameter imaging and provide a wealth of image information to optimize disease diagnosis. In recent years, with the rapid development and application of spectral CT, more and more studies on the application of spectral CT in the characterization of GI tumors have been published. For this review, we obtained a substantial volume of literature, focusing on spectral CT imaging of gastrointestinal cancers, including esophageal, stomach, colorectal, liver, and pancreatic cancers. We found that spectral CT can not only accurately stage gastrointestinal tumors before operation but also distinguish benign and malignant GI tumors with improved image quality, and effectively evaluate the therapeutic response and prognosis of the lesions. In addition, this paper also discusses the limitations and prospects of using spectral CT in GI cancer diagnosis and treatment.

Application of multi-spectral CT imaging in Crohn's disease: a systematic review

BACKGROUND

No quantitative computed tomography (CT) biomarker is actually sufficiently accurate to assess Crohn's disease (CD) lesion activity, with adequate precision to guide clinical decisions.

PURPOSE

To assess the available literature on the use of iodine concentration (IC), from multi-spectral CT acquisition, as a quantitative parameter able to distinguish healthy from affected bowel and assess CD bowel activity and heterogeneity of activity along the involved segments.

MATERIAL AND METHODS

A literature search was conducted to identify original research studies published up to February 2022. The inclusion criteria were original research papers (>10 human participants), English language publications, focus on dual-energy CT (DECT) of CD with iodine quantification (IQ) as an outcome measure. The exclusion criteria were animal-only studies, languages other than English, review articles, case reports, correspondence, and study populations <10 patients.

RESULTS

Nine studies were included in this review; all of which showed a strong correlation between IC measurements and CD activity markers, such as CD activity index (CDAI), endoscopy findings and simple endoscopic score for Crohn's disease (SES-CD), and routine CT enterography (CTE) signs and histopathologic score. Statistically significant differences in IC were reported between affected bowel segments and healthy ones (higher P value was P < 0.001), normal segments and those with active inflammation (P < 0.0001) as well as between patients with active disease and those in remission (P < 0.001).

CONCLUSION

The mean normalized IC at DECTE could be a reliable tool in assisting radiologists in the diagnosis, classification and grading of CD activity.

Mapping the Spatial Extent of Hypoperfusion in Chronic Thromboembolic Pulmonary Hypertension Using Multienergy CT

Purpose

To assess if a novel automated method to spatially delineate and quantify the extent of hypoperfusion on multienergy CT angiograms can aid the evaluation of chronic thromboembolic pulmonary hypertension (CTEPH) disease severity.

Materials and Methods

Multienergy CT angiograms obtained between January 2018 and December 2020 in 51 patients with CTEPH (mean age, 47 years ± 17 [SD]; 27 women) were retrospectively compared with those in 110 controls with no imaging findings suggestive of pulmonary vascular abnormalities (mean age, 51 years ± 16; 81 women). Parenchymal iodine values were automatically isolated using deep learning lobar lung segmentations. Low iodine concentration was used to delineate areas of hypoperfusion and calculate hypoperfused lung volume (HLV). Receiver operating characteristic curves, correlations with preoperative and postoperative changes in invasive hemodynamics, and comparison with visual assessment of lobar hypoperfusion by two expert readers were evaluated.

Results

Global HLV correctly separated patients with CTEPH from controls (area under the receiver operating characteristic curve = 0.84; 10% HLV cutoff: 90% sensitivity, 72% accuracy, and 64% specificity) and correlated moderately with hemodynamic severity at time of imaging (pulmonary vascular resistance [PVR], ρ = 0.67; P < .001) and change after surgical treatment (∆PVR, ρ = -0.61; P < .001). In patients surgically classified as having segmental disease, global HLV correlated with preoperative PVR (ρ = 0.81) and postoperative ∆PVR (ρ = -0.70). Lobar HLV correlated moderately with expert reader lobar assessment (ρHLV = 0.71 for reader 1; ρHLV = 0.67 for reader 2).

Conclusion

Automated quantification of hypoperfused areas in patients with CTEPH can be performed from clinical multienergy CT examinations and may aid clinical evaluation, particularly in patients with segmental-level disease.Keywords: CT-Spectral Imaging (Multienergy), Pulmonary, Pulmonary Arteries, Embolism/Thrombosis, Chronic Thromboembolic Pulmonary Hypertension, Multienergy CT, Hypoperfusion© RSNA, 2023.

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability

OBJECTIVES

To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms.

METHODS

A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI_70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI_70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated.

RESULTS

92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI_70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI_70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI_70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI_70keV (r² 0.3870-0.6178, all p < 0.001).

CONCLUSIONS

The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Spectral CT Imaging of Prosthetic Valve Embolization after Transcatheter Aortic Valve Implantation

Transcatheter heart valve (THV) embolization is a rare complication of transcatheter aortic valve implantation (TAVI) generally caused by malpositioning, sizing inaccuracies and pacing failures. The consequences are related to the site of embolization, ranging from a silent clinical picture when the device is stably anchored in the descending aorta to potentially fatal outcomes (e.g., obstruction of flow to vital organs, aortic dissection, thrombosis, etc.). Here, we present the case of a 65-year-old severely obese woman affected by severe aortic valve stenosis who underwent TAVI complicated by embolization of the device. The patient underwent spectral CT angiography that allowed for improved image quality by means of virtual monoenergetic reconstructions, permitting optimal pre-procedural planning. She was successfully re-treated with implantation of a second prosthetic valve a few weeks later.

Economics of Dual-Energy CT: Workflow, Costs, and Benefits

Dual-energy CT is an emerging technology which is progressively becoming more available for routine clinical applications. As practices and institutions evaluate the business case for purchase of these high-end scanners, the clinical utility and downstream costs must be determined. This article will provide an overview of the technology and will review direct and indirect costs associated with the implementation of dual-energy CT programs.

Role of Dual-energy Computed Tomography in Diagnosis of Acute Pulmonary Emboli, a Review

Prompt diagnosis of pulmonary embolism is essential to avert morbidity and mortality. There are a number of diagnostic options for identification of a pulmonary embolism, including laboratory and imaging investigations. While computed tomography pulmonary angiography (CTPA) has largely supplanted nuclear medicine ventilation/perfusion studies, there remain significant limitations in the optimal performance of CTPA. Dual-energy computed tomography has the ability to overcome many of the limitations of standard of care CTPA, including rescue of poor contrast boluses and the ability to evaluate pulmonary perfusion defects.

Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction

OBJECTIVE

The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30.

METHODS

Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high). Subjective quality criteria were independently evaluated by 4 abdominal radiologists, and interreader agreement was assessed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio were compared between image reconstruction methods.

RESULTS

Forty-eight patients were included in this study, and the mean patient body mass index was 39.5 (SD, 7.36). TrueFidelity-High (DL-High) and TrueFidelity-Medium (DL-Med) image reconstructions showed statistically significant higher Likert scores compared with ASiR-V across all subjective image quality criteria (P < 0.001 for DL-High vs ASiR-V; P < 0.05 for DL-Med vs ASiR-V), and SNRs for aorta and liver were significantly higher for DL-High versus ASiR-V (P < 0.001). Contrast-to-noise ratio for aorta and SNR for aorta and liver were significantly higher for DL-Med versus ASiR-V (P < 0.05).

CONCLUSIONS

TrueFidelity DL image reconstruction provides improved image quality compared with ASiR-V in dual-energy CTs obtained in obese patients.

Assessing the Sensitivity of Dual-Energy Computed Tomography 3-Material Decomposition for the Detection of Gout

OBJECTIVES

The aim of this study was to assess the accuracy and precision of a novel application of 3-material decomposition (3MD) with virtual monochromatic images (VMIs) in the dual-energy computed tomography (DECT) assessment of monosodium urate (MSU) and hydroxyapatite (HA) phantoms compared with a commercial 2-material decomposition (2MD) and dual-thresholding (DT) material decomposition methods.

MATERIALS AND METHODS

Monosodium urate (0.0, 3.4, 13.3, 28.3, and 65.2 mg/dL tubes) and HA (100, 400, and 800 mg/cm3 tubes) phantoms were DECT scanned individually and together in the presence of the foot and ankle of 15 subjects. The raw data were decomposed with 3MD-VMI, 2MD, and DT to produce MSU-only and HA-only images. Mean values of 10 × 10 × 10-voxel volumes of interest (244 μm3) placed in each MSU and HA phantom well were obtained and compared with their known concentrations and across measurements with subjects' extremities to obtain accuracy and precision measures. A statistical difference was considered significant if P < 0.05.

RESULTS

Compared with known phantom standards, 3MD-VMI was accurate for the detection of MSU concentrations as low as 3.4 mg/dL (P = 0.75). In comparison, 2MD was limited to 13.3 mg/dL (P = 0.06) and DT was unable to detect MSU concentrations below 65.2 mg/L (P = 0.16). For the HA phantom, 3MD-VMI and 2MD were accurate for all concentrations including the lowest at 100 mg/cm3 (P = 0.63 and P = 0.55, respectively). Dual-thresholding was not useful for the decomposition of HA phantom. Precision was high for both 3MD-VMI and 2MD measurements for both MSU and HA phantoms. Qualitatively, 3MD-VMI MSU-only images demonstrated reduced beam-hardening artifact and voxel misclassification, compared with 2MD and DT.

CONCLUSIONS

Three-material decomposition-VMI DECT is accurate for quantification of MSU and HA concentrations in phantoms and accurately detects a lower concentration of MSU than either 2MD or DT. For concentration measurements of both MSU and HA phantoms, 3MD-VMI and 2MD have high precision, but DT had limitations. Clinical implementation of 3MD-VMI DECT promises to improve the performance of this imaging modality for diagnosis and treatment monitoring of gout.

Spectral CT of the abdomen: Where are we now?

Spectral CT adds a new dimension to radiological evaluation, beyond assessment of anatomical abnormalities. Spectral data allows for detection of specific materials, improves image quality while at the same time reducing radiation doses and contrast media doses, and decreases the need for follow up evaluation of indeterminate lesions. We review the different acquisition techniques of spectral images, mainly dual-source, rapid kV switching and dual-layer detector, and discuss the main spectral results available. We also discuss the use of spectral imaging in abdominal pathologies, emphasizing the strengths and pitfalls of the technique and its main applications in general and in specific organs.

Recognizing and Minimizing Artifacts at Dual-Energy CT

Dual-energy CT (DECT) is an exciting innovation in CT technology with profound capabilities to improve diagnosis and add value to patient care. Significant advances in this technology over the past decade have improved our ability to successfully adopt DECT into the clinical routine. To enable effective use of DECT, one must be aware of the pitfalls and artifacts related to this technology. Understanding the underlying technical basis of artifacts and the strategies to mitigate them requires optimization of scan protocols and parameters. The ability of radiologists and technologists to anticipate their occurrence and provide recommendations for proper selection of patients, intravenous and oral contrast media, and scan acquisition parameters is key to obtaining good-quality DECT images. In addition, choosing appropriate reconstruction algorithms such as image kernel, postprocessing parameters, and appropriate display settings is critical for preventing quantitative and qualitative interpretive errors. Therefore, knowledge of the appearances of these artifacts is essential to prevent errors and allows maximization of the potential of DECT. In this review article, the authors aim to provide a comprehensive and practical overview of possible artifacts that may be encountered at DECT across all currently available commercial clinical platforms. They also provide a pictorial overview of the diagnostic pitfalls and outline strategies for mitigating or preventing the occurrence of artifacts, when possible. The broadening scope of DECT applications necessitates up-to-date familiarity with these technologies to realize their full diagnostic potential.

Use of dual-energy CT for renal mass assessment

Although dual-energy CT (DECT) may prove useful in a variety of abdominal imaging tasks, renal mass evaluation represents the area where this technology can be most impactful in abdominal imaging compared to routinely performed contrast-enhanced-only single-energy CT exams. DECT post-processing techniques, such as creation of virtual unenhanced and iodine density images, can help in the characterization of incidentally discovered renal masses that would otherwise remain indeterminate based on post-contrast imaging only. The purpose of this article is to review the use of DECT for renal mass assessment, including its benefits and existing limitations. KEY POINTS: • If DECT is selected as the scanning mode for most common abdominal protocols, many incidentally found renal masses can be fully triaged within the same exam. • Virtual unenhanced and iodine density DECT images can provide additional information when renal masses are discovered in the post-contrast-only setting. • For renal mass evaluation, virtual unenhanced and iodine density DECT images should be interpreted side-by-side to troubleshoot pitfalls that can potentially lead to erroneous interpretation.

Virtual unenhanced imaging of the liver derived from 160-mm rapid-switching dual-energy CT (rsDECT): Comparison of the accuracy of attenuation values and solid liver lesion conspicuity with native unenhanced images

OBJECTIVES

To evaluate the reliability of attenuation values of the liver parenchyma and focal liver lesions on virtual unenhanced images from arterial (VUEart) and portal venous phases (VUEport) compared to native unenhanced (NU) attenuation values in patients referred for assessment of malignant liver lesions.

METHODS

Seventy-three patients with confirmed primary or metastatic liver tumors who underwent a multiphase contrast-enhanced rapid-switching kVp dual-energy CT (rsDECT) were included in this IRB-approved retrospective study. Both qualitative and quantitative analyses - including the lesion-to-liver contrast-to-noise ratio (LL-CNR) - were performed and compared between NU and both VUEart and VUEport images.

RESULTS

The mean liver attenuation values were significantly lower in VUEart images (56.7 ± 6.7 HU) than in NU images (59.6 ± 7.5 HU, p = 0.008), and were comparable between VUEart and VUEport images (57.9 ± 6 UH, p = 0.38) and between VUEport and NU images (p = 0.051). The mean liver lesions attenuation values were comparable between NU, VUEart and VUEport images (p = 0.60). Strong and significant correlations values were found both in liver lesions and tumor-free parenchyma (r = 0.82-0.91, p < 0.01). The mean LL-CNR was significantly higher in VUEart and VUEport images than in NU images (1.7 ± 1 and 1.6 ± 1.1 vs 0.9 ± 0.6; p < 0.001), but was comparable between VUEart and VUEport images (p > 0.9). Lesion conspicuity was significantly higher in VUEport images than in NU images (p < 0.001).

CONCLUSION

VUEport images derived from 3rd generation rsDECT could confidently replace NU images in patients undergoing assessment for malignant liver lesions. These images provide comparable attenuation values in both liver lesions and liver parenchyma while reducing the radiation dose and scanning time.

Radiation doses and image quality of abdominal CT scans at different patient sizes using spectral detector CT scanner: a phantom and clinical study

PURPOSE

To compare radiation dose and image quality for abdominal CTs performed on a spectral detector CT (SDCT) and a comparable single-energy conventional CT scanner for patients of different sizes.

METHODS

Four semi-anthropomorphic phantoms were scanned on an SDCT (IQon, Philips Healthcare) and a comparable single-energy CT (iCT 256, Philips Healthcare) under matched scan parameters. Image noise and radiation dose were compared. For the HIPAA-compliant, IRB-approved retrospective cohort patient study, radiation dose was compared after adjusting for patient water equivalent diameter. Difference in subjective and objective image quality was assessed on a subset of 50 patients scanned on both scanners by two readers.

RESULTS

CTDIvol and noise from SDCT were higher than conventional CT for all phantoms, with a relative difference of 7.8% (range 5.3-14%) for radiation dose and average difference of 9.0% (range 5.5-11%) for noise. 718 SDCT and 937 conventional CT patients were included in the patient study. CTDIvol for SDCT patients tends to be lower for smaller patients (- 2%, 95% confidence interval (- 5%, - 0.2%) for 200 mm water equivalent diameter) and higher for larger patients compared to conventional CT (8%, (6%, 11%) for 400 mm). No difference was seen for subjective image quality, SNR, CNR, or image noise between the two scanners, except for higher image noise in the portal vein and higher signal in the aorta on SDCT.

CONCLUSION

Radiation dose for abdominal CT performed on SDCT is similar to the dose on a conventional CT for average size patients, lower for smaller patients, and slightly higher for larger patients. Image quality is similar between the two scanners.

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.

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80-100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml^-1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml^-1 (DS-EID-CT) to 2.39 ± 0.05 mg ml^-1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.

Follow-up Recommendation Rates Associated With Spectral Detector Dual-Energy CT of the Abdomen and Pelvis: A Retrospective Comparison to Single-Energy CT

BACKGROUND

Dual-energy CT image sets have many applications in abdominopelvic imaging but no demonstrated clinical effect.

PURPOSE

To determine the effect of dual-energy CT iodine maps on abdominopelvic imaging follow-up recommendation rates.

MATERIALS AND METHODS

Retrospective study of abdominopelvic CTs acquired from April 2017 through June 2018. CT reports were analyzed for radiologic follow-up recommendation and follow-up recommendation reason. Follow-up MRI reports were analyzed for benign or nonbenign diagnosis. CT scans with iodine maps (CTIMs) and conventional CT scans (CCTs) subgroups were compared using χ² testing.

RESULTS

In all, 3,221 abdominopelvic CT scans of 2,401 patients (1,326 men, 1,075 women, mean age 54.1 years) were analyzed; 1,423 were CTIMs and 1,798 were CCTs. Follow-up recommendation rates were not significantly different for CTIMs and CCTs (19.5% and 21.4%, respectively, P = .19). Follow-up recommendations because of incomplete diagnosis were significantly lower in CTIMs (9.1%) than in CCTs (11.9%, P = .01). Follow-up recommendations for MRI and PET/CT were significantly lower in CTIMs (9.6%) than CCTs (13.0%, P = .003). Follow-up MRI outcomes (n = 111) were not different between CTIMs (61.2% benign) and CCTs (59.6%, P = .87).

CONCLUSION

Dual-energy CT iodine maps are associated with decreased follow-up examinations because of incomplete diagnosis and decreased recommendations for follow-up MRI, suggesting that abdominopelvic iodine maps may benefit patient care and decrease institutional cost.

The Effect of Image Resolution on Deep Learning in Radiography

PURPOSE

To examine variations of convolutional neural network (CNN) performance for multiple chest radiograph diagnoses and image resolutions.

MATERIALS AND METHODS

This retrospective study examined CNN performance using the publicly available National Institutes of Health chest radiograph dataset comprising 112 120 chest radiographic images from 30 805 patients. The network architectures examined included ResNet34 and DenseNet121. Image resolutions ranging from 32 × 32 to 600 × 600 pixels were investigated. Network training paradigms used 80% of samples for training and 20% for validation. CNN performance was evaluated based on area under the receiver operating characteristic curve (AUC) and label accuracy. Binary output networks were trained separately for each label or diagnosis under consideration.

RESULTS

Maximum AUCs were achieved at image resolutions between 256 × 256 and 448 × 448 pixels for binary decision networks targeting emphysema, cardiomegaly, hernias, edema, effusions, atelectasis, masses, and nodules. When comparing performance between networks that utilize lower resolution (64 × 64 pixels) versus higher (320 × 320 pixels) resolution inputs, emphysema, cardiomegaly, hernia, and pulmonary nodule detection had the highest fractional improvements in AUC at higher image resolutions. Specifically, pulmonary nodule detection had an AUC performance ratio of 80.7% ± 1.5 (standard deviation) (0.689 of 0.854) whereas thoracic mass detection had an AUC ratio of 86.7% ± 1.2 (0.767 of 0.886) for these image resolutions.

CONCLUSION

Increasing image resolution for CNN training often has a trade-off with the maximum possible batch size, yet optimal selection of image resolution has the potential for further increasing neural network performance for various radiology-based machine learning tasks. Furthermore, identifying diagnosis-specific tasks that require relatively higher image resolution can potentially provide insight into the relative difficulty of identifying different radiology findings. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Lakhani in this issue.