Evaluation of Virtual Monoenergetic Images on Pulmonary Vasculature Using the Dual-Layer Detector-Based Spectral Computed Tomography.

OBJECTIVE: To evaluate the ability of retrospectively generated virtual monoenergetic images (VMIs) from the detector-based spectral computed tomography (SDCT) to augment pulmonary artery enhancement in CT and if iodine map can predict the optimal monoenergetic level. METHODS: The study included 79 patients with contrast-enhanced chest CT scans on an SDCT scanner. Conventional 120-kVp images and VMI from 40 to 80 keV were generated. Attenuation, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at 7 different locations in the pulmonary arterial system. The iodine concentration (in milligrams per milliliter) was calculated using the iodine-density images. The overall image quality was subjectively graded on a 5-point scale, with 1 being the worst and 5 the best. Fifty-four patients with suboptimal pulmonary enhancement (<200 Hounsfield units [HU]) were then identified. From the VMIs, an ideal set was chosen that maintained mean vascular attenuation greater than 200 HU while maintaining at least diagnostically acceptable quality (ie, IQ score ≥3). At this ideal energy level, quantitative and qualitative parameters were compared with the standard 120-kVp polyenergetic study. Average iodine concentrations were correlated with the optimal keV levels used for salvaging suboptimal studies. RESULTS: The mean attenuation of all the measured pulmonary arterial regions in the suboptimal cases was 136.1 ± 18.1 HU in conventional 120-kVp images. Attenuations of the VMIs at 40, 50, and 60 keV were significantly higher than conventional images measuring 357.5 ± 19.5, 243.6 ± 16.7, and 176.6 ± 15.0 HU, respectively (P < 0.001). Similar results were seen with SNR and CNR. In total, 50 studies can be salvaged, with 50 keV being the optimal energy for 21, 60 keV optimal for 17, and 40 keV optimal for 12 studies. At the optimal energy level, there were improvements of attenuation, SNR, and CNR by 71%, 63%, and 137% compared with conventional images. There was a positive correlation between iodine value and optimal reconstruction energy with a linear equation y = 5.9539x + 27.434 and R = 0.8093. CONCLUSIONS: Suboptimal enhanced pulmonary arterial CT studies can be salvaged using low-energy VMI generated from the SDCT scanner. There were significant improvements of attenuation, SNR, and CNR at the optimal monoenergetic level.

Diagnosis and staging of hepatobiliary malignancies: Potential incremental value of (18)F-FDG-PET/MRI compared to MRI of the liver.

OBJECTIVE: The purpose of the study was to investigate the potential added value of 18F-FDG-PET/MRI (functional information derived from PET) over standard diagnostic liver MRI (excellent soft tissue characterization) in diagnosing and staging suspected primary hepatobiliary malignancies including extrahepatic cholangiocarcinoma (ECC), intrahepatic cholangiocellular carcinoma (ICC) and gallbladder cancer (GBCA). METHODS: Twenty consecutive patients with suspected hepatobiliary malignancy were included in this retrospective study. All patients underwent combined whole-body (WB) 18F-FDG-PET/MRI including contrast-enhanced MRI of the liver, contrast-enhanced WB-MRI and WB 18F-FDG-PET. Two experienced readers staged hepatobiliary disease using TNM criteria: first based on MRI alone and then based on combined 18F-FDG-PET/MRI. Subsequently, the impact of FDG-PET/MRI on clinical management compared to MRI alone was recorded. Histopathologic proof served as the reference standard. RESULTS: Hepatobiliary neoplasms were present in 16/20 patients (ECC n = 3, ICC n = 8, GBCA n = 5), two patients revealed benign disease, two were excluded. TNM staging with 18F-FDG-PET/MRI was identical to MRI alone in 11/18 (61.1 %) patients and correctly changed the stage in 4/18 (22.2 %), resulting in a change in management for 2/4 patients (11.1 %). 18F-FDG-PET/MRI was false-positive in 3/18 cases (16.7 %). Both MRI and 18F-FDG-PET/MRI were falsely positive in 1 case without malignancy. CONCLUSIONS: A small incremental benefit of 18F-FDG-PET/MRI over standard MRI of the liver was observed. However, in some cases 18F-FDG-PET/MRI may lead to false-positive findings. Overall there is seemingly limited role of 18F-FDG-PET/MRI in patients with suspected hepatobiliary malignancy.

Chronic airspace disease: Review of the causes and key computed tomography findings.

Chronic airspace diseases are commonly encountered by chest, body or general radiologists in everyday practice. Even though there is significant overlap in the imaging findings of different causes of chronic airspace disease, some key clinical, laboratory and imaging findings can be used to guide the radiologist to the correct diagnosis. The goal of this article is to review and compare these features.

(18F)-FDG-PET/MRI of unicentric retroperitoneal Castleman disease in a pediatric patient.

Castleman disease (CD) is a rare lymphoproliferative disorder of unknown etiology that may occur anywhere in the lymphatic system. Imaging plays an important role in detecting and staging this disease. Positron Emission Tomography/Magnetic Resonance Imaging (PET/MRI) combines the metabolic information derived from nuclear medical imaging with the high soft tissue resolution from MRI. We review the features of CD in standard diagnostic imaging, analyze the specific imaging findings of CD in FDG-PET/MRI and discuss a potential benefit of PET/MRI based on the case of a 15-year-old female patient with retroperitoneal CD.

Benefit and clinical significance of retrospectively obtained spectral data with a novel detector-based spectral computed tomography - Initial experiences and results.

OBJECTIVES: To evaluate the benefits and clinical significance of retrospectively generated spectral image-datasets with the novel detector-based Spectral CT (SDCT). METHODS: A total of 118 body CTs from the SDCT prototype were included. Based on the clinical indication, two radiologists were asked if they would have opted for a dual-energy mode/scan if the patient was scanned in one of the other commercially-available dual-energy scanners, which need prospective selection of dual energy mode. They also reviewed the scans, identified cases that would benefit from spectral images and evaluated these images for clinical utility and significance on a five-point scale, with 1 being the least and 5 being the highest. RESULTS: Dual-energy mode would have been prospectively selected in 20 cases (17%) for Reader 1 and 25 cases (21%) for Reader 2. Additional spectral images were requested for 94 cases (80%) and 96 cases (81%) respectively. A total of 196 and 206 spectral image-sets were utilized respectively with 97% and 96% of these image-sets useful retrospectively. The distribution of scores on the five-point scale for Readers 1 and 2 were, 1-7% & 6%; 2-26% & 30%; 3-36% & 36%; 4-27% & 21% and; 5-4% & 7%. Clinically significant score (≥4) was noted in 31% and 28% respectively. CONCLUSIONS: Additional spectral datasets retrospectively reconstructed from SDCT enhanced the diagnostic capabilities by reducing artifacts, improving contrast and allowing lesion characterization.

Noise characteristics of virtual monoenergetic images from a novel detector-based spectral CT scanner.

AIM: To evaluate the noise characteristics of virtual monoenergetic images (VMI) obtained from a recently introduced dual-layer detector-based spectral CT (SDCT), both in a phantom and patients. MATERIALS AND METHODS: A cylindrical Catphan® 600 phantom (The Phantom Library, Salem NY, USA) was scanned using the SDCT. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured in VMI from 40 to 200keV as well as conventional 120 kVp images. One hundred consecutive patients who had an abdominal CT on the SDCT were then recruited in the study. Noise, SNR and CNR were measured in the liver, pancreas, spleen, kidney, abdominal aorta, portal vein, muscle, bone, and fat, both in VMI (40-200 keV) and conventional 120kVp images. Qualitative image analysis was performed by an independent reader for vascular enhancement and image quality on a 5 point scale (1-worst, 5-best). RESULTS: On phantom studies, noise was low at all energies of VMI. Noise was highest at 40keV (5.3±0.2 HU), gradually decreased up to 70keV (3.6±0.2 HU), after which it remained constant up to 200keV (3.5±0.2 HU). In the patient cohort, noise was low (<25 HU) at all the energy levels of VMI for all the regions, with the exception of bone. For example, noise in the liver was highest at 40keV (13.2±4.6 HU), steadily decreased up to 70keV (12.0±4.4 HU) and then remained constantly low up to 200keV (11.6±4.3HU). For liver, pancreas, portal vein, aorta, muscle and fat, noise at all levels of VMI was lower than of conventional images (p<0.01). For all organs, SNR, and CNR were highest at 40keV (6.8-34.9; 18.3-44.9, respectively) after which they gradually decreased up to 120keV (3.4-6.5; 9.5-13.0) and then remained constant to 200keV (2.6-5.5; 8.5-12.5). Qualitative scores of VMI up to 70keV were significantly higher than the conventional images (p≤0.01), whereas for VMI≥80keV, they were lower than conventional images (p<0.001). CONCLUSION: VMI obtained from the novel SDCT scanner have low noise across the entire spectrum of energies. There are significant SNR and CNR improvements compared to conventional 120 kVp images.

Utility of virtual monoenergetic images derived from a dual-layer detector-based spectral CT in the assessment of aortic anatomy and pathology: A retrospective case control study.

OBJECTIVES: To evaluate the ability of the retrospectively generated virtual monoenergetic images (VMIs) from a dual-layer detector-based spectral computed tomography (SDCT) to augment aortic enhancement for the evaluation of aortic anatomy and pathology. METHODS: 98 patients with suboptimal aortic enhancement (≤200 HU) were retrospectively identified from SDCT scans. VMI from 40 to 80 keV were generated. Attenuation, noise, SNR, and CNR were measured at seven levels in the aorta. Image quality was graded on a 5-point scale, 5 being the best. From the VMI, an ideal set was chosen with mean vascular attenuation above 200 HU while maintaining diagnostic quality. Image parameters and quality of this ideal-set were compared to the standard 120-kVp images. RESULTS: The mean attenuation of all seven measured anatomical regions was 156.6 ±â€¯61.7 HU in the 120-kVp images. Attenuation of the VMI from 40 to 70 keV were higher than the 120-kVp image, measuring 439.2 ±â€¯215.3 HU, 298.5 ±â€¯140.6 HU, 213.4 ±â€¯94.3 HU, and 164.7 ±â€¯90.2 HU, for 40 keV, 50 keV, 60 keV, and 70 keV, respectively (p value <0.01 for 40, 50, 60 keV; 0.07 for 70 keV). SNR and CNR showed similar trends. The 50 keV VMI had the best image quality (4.48 ±â€¯0.84 vs. 2.24 ±â€¯0.92 on 120-kVp images, p < 0.001). Attenuation, CNR, and SNR increased by 90.6%, 85.0%, and 108.1% at 50 keV compared to 120-kVp. CONCLUSIONS: A contrast-enhanced CT study can be optimized for the assessment of the aorta by using low-energy VMI obtained using SDCT. At the optimal monoenergetic level, attenuation, SNR, CNR and image quality were significantly higher than that of conventional polyenergetic images.

Machine learning-based dual-energy CT parametric mapping.

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Detector-based spectral CT with a novel dual-layer technology: principles and applications.

Detector-based spectral computed tomography is a novel dual-energy CT technology that employs two layers of detectors to simultaneously collect low- and high-energy data in all patients using standard CT protocols. In addition to the conventional polyenergetic images created for each patient, projection-space decomposition is used to generate spectral basis images (photoelectric and Compton scatter) for creating multiple spectral images, including material decomposition (iodine-only, virtual non-contrast, effective atomic number) and virtual monoenergetic images, on-demand according to clinical need. These images are useful in multiple clinical applications, including- improving vascular contrast, improving lesion conspicuity, decreasing artefacts, material characterisation and reducing radiation dose. In this article, we discuss the principles of this novel technology and also illustrate the common clinical applications. Teaching points • The top and bottom layers of dual-layer CT absorb low- and high-energy photons, respectively.• Multiple spectral images are generated by projection-space decomposition.• Spectral images can be generated in all patients scanned in this scanner.

Assessment of 70-keV virtual monoenergetic spectral images in abdominal CT imaging: A comparison study to conventional polychromatic 120-kVp images.

PURPOSE: To evaluate the image quality of 70-keV virtual monoenergetic (monoE) abdominal CT images compared to 120-kVp polychromatic images generated from a spectral detector CT (SDCT) scanner. METHODS: This prospective study included generation of a 120-kVp polychromatic dataset and a 70-keV virtual monoE dataset after a single contrast-enhanced CT acquisition on a SDCT scanner (Philips Healthcare) during portal venous phase. The attenuation values (HU), noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured in the liver, spleen, pancreas, kidney, aorta, portal vein, and muscle. The subjective image quality including noise, soft tissue contrast, sharpness, and overall image quality were graded on a 5-point Likert scale by two radiologists independently (1-worst image quality, 5-best image quality). Statistical analysis was performed using paired sample t test and Fleiss's Kappa. RESULTS: Fifty-five patients (54.3 ± 16.8 y/o; 28 M, 27 F) were recruited. The noise of target organs was significantly lower in virtual monoE images in comparison to polychromatic images (p < 0.001). The SNR and CNR were significantly higher in virtual monoE images (p < 0.001 for both). Subjective image quality of 70-keV virtual monoE images was significantly better (p < 0.001) for all evaluated parameters. Median scores for all subjective parameters were 3.0 versus 4.0 for polychromatic vs virtual monoE images, respectively. The inter-reader agreement for overall image quality was good (Kappa were 0.767 and 0.762 for polychromatic and virtual monoE images, respectively). CONCLUSION: In abdominal imaging, 70-keV virtual monoE CT images demonstrated significantly better noise, SNR, CNR, and subjective score compared to conventional 120-kVp polychromatic images.

Quality of routine diagnostic abdominal images generated from a novel detector-based spectral CT scanner: a technical report on a phantom and clinical study.

PURPOSE: To evaluate the image quality of routine diagnostic images generated from a novel detector-based spectral detector CT (SDCT) and compare it with CT images obtained from a conventional scanner with an energy-integrating detector (Brilliance iCT), Routine diagnostic (conventional/polyenergetic) images are non-material-specific images that resemble single-energy images obtained at the same radiation, METHODS: ACR guideline-based phantom evaluations were performed on both SDCT and iCT for CT adult body protocol. Retrospective analysis was performed on 50 abdominal CT scans from each scanner. Identical ROIs were placed at multiple locations in the abdomen and attenuation, noise, SNR, and CNR were measured. Subjective image quality analysis on a 5-point Likert scale was performed by 2 readers for enhancement, noise, and image quality. RESULTS: On phantom studies, SDCT images met the ACR requirements for CT number and deviation, CNR and effective radiation dose. In patients, the qualitative scores were significantly higher for the SDCT than the iCT, including enhancement (4.79 ± 0.38 vs. 4.60 ± 0.51, p = 0.005), noise (4.63 ± 0.42 vs. 4.29 ± 0.50, p = 0.000), and quality (4.85 ± 0.32, vs. 4.57 ± 0.50, p = 0.000). The SNR was higher in SDCT than iCT for liver (7.4 ± 4.2 vs. 7.2 ± 5.3, p = 0.662), spleen (8.6 ± 4.1 vs. 7.4 ± 3.5, p = 0.152), kidney (11.1 ± 6.3 vs. 8.7 ± 5.0, p = 0.033), pancreas (6.90 ± 3.45 vs 6.11 ± 2.64, p = 0.303), aorta (14.2 ± 6.2 vs. 11.0 ± 4.9, p = 0.007), but was slightly lower in lumbar-vertebra (7.7 ± 4.2 vs. 7.8 ± 4.5, p = 0.937). The CNR of the SDCT was also higher than iCT for all abdominal organs. CONCLUSION: Image quality of routine diagnostic images from the SDCT is comparable to images of a conventional CT scanner with energy-integrating detectors, making it suitable for diagnostic purposes.

Spectral detector CT-derived virtual non-contrast images: comparison of attenuation values with unenhanced CT.

PURPOSE: To assess virtual non-contrast (VNC) images obtained on a detection-based spectral detector CT scanner and determine how attenuation on VNC images derived from various phases of enhanced CT compare to those obtained from true unenhanced images. METHODS: In this HIPAA compliant, IRB approved prospective multi-institutional study, 46 patients underwent pre- and post-contrast imaging on a prototype dual-layer spectral detector CT between October 2013 and November 2015, yielding 84 unenhanced and VNC pairs (25 arterial, 39 portal venous/nephrographic, 20 urographic). Mean attenuation was measured by one of three readers in the liver, spleen, kidneys, psoas muscle, abdominal aorta, and subcutaneous fat. Equivalence testing was used to determine if the mean difference between unenhanced and VNC attenuation was less than 5, 10, or 15 HU. VNC image quality was assessed on a 5 point scale. RESULTS: Mean difference between unenhanced and VNC attenuation was <15 HU in 92.6%, <10 HU in 75.2%, and <5 HU in 44.4% of all measurements. Unenhanced and VNC attenuation were equivalent in all tissues except fat using a threshold of <10 HU difference (p < 0.05). No significant variation was seen between phases. In fat, VNC overestimated the HU relative to unenhanced images. VNC image quality was rated as excellent or good in 84% of arterial phase and 85% of nephrographic phase cases, but only 40% of urographic phase. CONCLUSION: VNC images derived from novel dual layer spectral detector CT demonstrate attenuation values similar to unenhanced images in all tissues evaluated except for subcutaneous fat. Further study is needed to determine if attenuation thresholds currently used clinically for common pathology should be adjusted, particularly for lesions containing fat.