CT-Guided Liver Biopsy: Evaluation of Spectral Data From Dual-Layer Detector CT for Improved Lesion Detection.

PURPOSE: Evaluation of dual-layer spectral computed tomography (CT) for contrast enhancement during image-guided biopsy of liver lesions using virtual monoenergetic images (VMI) and virtual non-contrast (VNC) images. METHODS: Spectral CT data of 20 patients receiving CT-guided needle biopsy of focal liver lesions were used to generate VMI at energy levels from 40 to 200 keV and VNC images. Images were analyzed objectively regarding contrast-to-noise ratio between lesion center (CNRcent) or periphery (CNRperi) and normal liver parenchyma. Lesion visibility and image quality were evaluated on a 4-point Likert scale by two radiologists. RESULTS: Using VMI/VNC images, readers reported an increased visibility of the lesion compared to the conventional CT images in 18/20 cases. In 75% of cases, the highest visibility was derived by VMI-40. Showing all reconstructions simultaneously, VMI-40 offered the highest visibility in 75% of cases, followed by VNC in 12.5% of cases. Either CNRcent (17/20) or/and CNRperi (17/20) was higher (CNR increase > 50%) in 19/20 cases for VMI-40 or VNC images compared to conventional CT images. VMI-40 showed the highest CNRcent in 14 cases and the highest CNRperi in 12 cases. High image quality was present for all reconstructions with a minimum median of 3.5 for VMI-40 and VMI-50. CONCLUSIONS: When implemented in the CT scanner software, automated contrast enhancement of liver lesions during image-guided biopsy may facilitate the procedure.

X-ray Dark-Field CT for Early Detection of Radiation-induced Lung Injury in a Murine Model.

Online supplemental material is available for this article.

Diagnosis of joint invasion in patients with malignant bone tumors: value and reproducibility of direct and indirect signs on MR imaging.

OBJECTIVES: To evaluate the performance and reproducibility of MR imaging features in the diagnosis of joint invasion (JI) by malignant bone tumors. METHODS: MR images of patients with and without JI (n = 24 each), who underwent surgical resection at our institution, were read by three radiologists. Direct (intrasynovial tumor tissue (ITT), intraarticular destruction of cartilage/bone, invasion of capsular/ligamentous insertions) and indirect (tumor size, signal alterations of epiphyseal/transarticular bone (bone marrow replacement/edema-like), synovial contrast enhancement, joint effusion) signs of JI were assessed. Odds ratios, sensitivity, specificity, PPV, NPV, and reproducibilities (Cohen's and Fleiss' κ) were calculated for each feature. Moreover, the diagnostic performance of combinations of direct features was assessed. RESULTS: Forty-eight patients (28.7 ± 21.4 years, 26 men) were evaluated. All readers reliably assessed the presence of JI (sensitivity = 92-100 %; specificity = 88-100%, respectively). Best predictors for JI were direct visualization of ITT (OR = 186-229, p < 0.001) and destruction of intraarticular bone (69-324, p < 0.001). Direct visualization of ITT was also highly reliable in assessing JI (sensitivity, specificity, PPV, NPV = 92-100 %), with excellent reproducibility (κ = 0.83). Epiphyseal bone marrow replacement and synovial contrast enhancement were the most sensitive indirect signs, but lacked specificity (29-54%). By combining direct signs with high specificity, sensitivity was increased (96 %) and specificity (100 %) was maintained. CONCLUSION: JI by malignant bone tumors can reliably be assessed on preoperative MR images with high sensitivity, specificity, and reproducibility. Particularly direct visualization of ITT, destruction of intraarticular bone, and a combination of highly specific direct signs were valuable, while indirect signs were less predictive and specific. KEY POINTS: • Direct visualization of intrasynovial tumor was the single most sensitive and specific (92-100%) MR imaging sign of joint invasion. • Indirect signs of joint invasion, such as joint effusion or synovial enhancement, were less sensitive and specific compared to direct signs. • A combination of the most specific direct signs of joint invasion showed best results with perfect specificity and PPV (both 100%) and excellent sensitivity and NPV (both 96 %).

In-vivo X-ray dark-field computed tomography for the detection of radiation-induced lung damage in mice.

BACKGROUND AND PURPOSE: Radiotherapy of thoracic tumours can lead to side effects in the lung, which may benefit from early diagnosis. We investigated the potential of X-ray dark-field computed tomography by a proof-of-principle murine study in a clinically relevant radiotherapeutic setting aiming at the detection of radiation-induced lung damage. MATERIAL AND METHODS: Six mice were irradiated with 20 Gy to the entire right lung. Together with five unirradiated control mice, they were imaged using computed tomography with absorption and dark-field contrast before and 16 weeks post irradiation. Mean pixel values for the right and left lung were calculated for both contrasts, and the right-to-left-ratio R of these means was compared. Radiologists also assessed the tomograms acquired 16 weeks post irradiation. Sensitivity, specificity, inter- and intra-reader accuracy were evaluated. RESULTS: In absorption contrast the group-average of R showed no increase in the control group and increased by 7% (p = 0.005) in the irradiated group. In dark-field contrast, it increased by 2% in the control group and by 14% (p = 0.005) in the irradiated group. Specificity was 100% for both contrasts but sensitivity was almost four times higher using dark-field tomography. Two cases were missed by absorption tomography but were detected by dark-field tomography. CONCLUSIONS: The applicability of X-ray dark-field computed tomography for the detection of radiation-induced lung damage was demonstrated in a pre-clinical mouse model. The presented results illustrate the differences between dark-field and absorption contrast and show that dark-field tomography could be advantageous in future clinical settings.

X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study.

BACKGROUND: Although advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD). METHODS: In this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)-which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison. FINDINGS: Between October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=-0·27, p=0·089 and visual emphysema ρ=-0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with lung diffusion capacity in the evaluated cohort. Emphysema assessment based on dark-field chest x-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance than conventional clinical tests characterising emphysema. Pair-wise comparison of corresponding test parameters between adjacent visual emphysema severity groups (CT-based, reference standard) showed higher effect sizes. The mean effect size over the group comparisons (absent-trace, trace-mild, mild-moderate, and moderate-confluent or advanced destructive visual emphysema grades) for the COPD assessment test score is 0·21, for forced expiratory volume in 1 s (FEV1)/functional vital capacity is 0·25, for FEV1% of predicted is 0·23, for residual volume % of predicted is 0·24, for CT emphysema index is 0·35, for dark-field signal homogeneity within lungs is 0·38, for dark-field signal texture within lungs is 0·38, and for dark-field-based emphysema severity is 0·42. INTERPRETATION: X-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema in patients with COPD because this technique provides relevant information representing the structural condition of lung parenchyma. This technique might offer a low radiation dose alternative to CT in COPD and potentially other lung disorders. FUNDING: European Research Council, Deutsche Forschungsgemeinschaft, Royal Philips, and Karlsruhe Nano Micro Facility.

X-ray Dark-Field Chest Imaging: Qualitative and Quantitative Results in Healthy Humans.

Background X-ray dark-field radiography takes advantage of the wave properties of x-rays, with a relatively high signal in the lungs due to the many air-tissue interfaces in the alveoli. Purpose To describe the qualitative and quantitative characteristics of x-ray dark-field images in healthy human subjects. Materials and Methods Between October 2018 and January 2020, patients of legal age who underwent chest CT as part of their diagnostic work-up were screened for study participation. Inclusion criteria were a normal chest CT scan, the ability to consent, and the ability to stand upright without help. Exclusion criteria were pregnancy, serious medical conditions, and changes in the lung tissue, such as those due to cancer, pleural effusion, atelectasis, emphysema, infiltrates, ground-glass opacities, or pneumothorax. Images of study participants were obtained by using a clinical x-ray dark-field prototype, recently constructed and commissioned at the authors' institution, to simultaneously acquire both attenuation-based and dark-field thorax radiographs. Each subject's total dark-field signal was correlated with his or her lung volume, and the dark-field coefficient was correlated with age, sex, weight, and height. Results Overall, 40 subjects were included in this study (average age, 62 years ± 13 [standard deviation]; 26 men, 14 women). Normal human lungs have high signal, while the surrounding osseous structures and soft tissue have very low and no signal, respectively. The average dark-field signal was 2.5 m-1 ± 0.4 of examined lung tissue. There was a correlation between the total dark-field signal and the lung volume (r = 0.61, P < .001). No difference was found between men and women (P = .78). Also, age (r = -0.18, P = .26), weight (r = 0.24, P = .13), and height (r = 0.01, P = .96) did not influence dark-field signal. Conclusion This study introduces qualitative and quantitative values for x-ray dark-field imaging in healthy human subjects. The quantitative x-ray dark-field coefficient is independent from demographic subject parameters, emphasizing its potential in diagnostic assessment of the lung. ©RSNA, 2021 See also the editorial by Hatabu and Madore in this issue.

Early detection of radiation-induced lung damage with X-ray dark-field radiography in mice.

OBJECTIVE: Assessing the advantage of x-ray dark-field contrast over x-ray transmission contrast in radiography for the detection of developing radiation-induced lung damage in mice. METHODS: Two groups of female C57BL/6 mice (irradiated and control) were imaged obtaining both contrasts monthly for 28 weeks post irradiation. Six mice received 20 Gy of irradiation to the entire right lung sparing the left lung. The control group of six mice was not irradiated. A total of 88 radiographs of both contrasts were evaluated for both groups based on average values for two regions of interest, covering (irradiated) right lung and healthy left lung. The ratio of these average values, R, was distinguished between healthy and damaged lungs for both contrasts. The time-point when deviations of R from healthy lung exceeded 3σ was determined and compared among contrasts. The Wilcoxon-Mann-Whitney test was used to test against the null hypothesis that there is no difference between both groups. A selection of 32 radiographs was assessed by radiologists. Sensitivity and specificity were determined in order to compare the diagnostic potential of both contrasts. Inter-reader and intra-reader accuracy were rated with Cohen's kappa. RESULTS: Radiation-induced morphological changes of lung tissue caused deviations from the control group that were measured on average 10 weeks earlier with x-ray dark-field contrast than with x-ray transmission contrast. Sensitivity, specificity, and accuracy doubled using dark-field radiography. CONCLUSION: X-ray dark-field radiography detects morphological changes of lung tissue associated with radiation-induced damage earlier than transmission radiography in a pre-clinical mouse model. KEY POINTS: • Significant deviations from healthy lung due to irradiation were measured after 16 weeks with x-ray dark-field radiography (p = 0.004). • Significant deviations occur on average 10 weeks earlier for x-ray dark-field radiography in comparison to x-ray transmission radiography. • Sensitivity and specificity doubled when using x-ray dark-field radiography instead of x-ray transmission radiography.

Grating-based phase-contrast CT (PCCT): histopathological correlation of human liver cirrhosis and hepatocellular carcinoma specimen.

AIMS: To correlate signal intensities in grating-based phase-contrast CT (PCCT) images obtained at a synchrotron light source and a conventional X-ray source with tissue components in human liver cirrhosis and hepatocellular carcinoma (HCC) specimen. METHODS: Study approval was obtained by the institutional review board. Human specimen of liver cirrhosis and HCC were imaged at experimental grating-based PCCT setups using either a synchrotron radiation source or a conventional X-ray tube. Tissue samples were sectioned and processed for H&E and Elastica van Gieson staining. PCCT and histological images were manually correlated. Depending on morphology and staining characteristics tissue components like fibrosis, HCC, inflammation, connective tissue and necrosis were differentiated and visually correlated with signal intensity in PCCT images using a 5-point Likert scale with normal liver parenchyma as a reference. RESULTS: Grating-based PCCT images of human cirrhotic liver and HCC specimen showed high soft-tissue contrast allowing correlation with histopathological sections. Signal intensities were similar in both setups independent of the nature of the radiation source. Connective tissue and areas of haemorrhage displayed the highest signal intensities, fibrotic liver tissue the lowest. CONCLUSIONS: Grating-based PCCT provides comparable results for the characterisation of human specimen of liver cirrhosis and HCC using either a synchrotron light source or a conventional X-ray tube. Due to its high soft-tissue contrast and its applicability to conventional X-ray tubes grating-based PCCT holds potential for preclinical research and virtual histology applications.

Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography.

PURPOSE: To evaluate the benefit of the additional available information present in spectral CT datasets, as compared to conventional CT datasets, when utilizing convolutional neural networks for fully automatic localisation and classification of liver lesions in CT images. MATERIALS AND METHODS: Conventional and spectral CT images (iodine maps, virtual monochromatic images (VMI)) were obtained from a spectral dual-layer CT system. Patient diagnosis were known from the clinical reports and classified into healthy, cyst and hypodense metastasis. In order to compare the value of spectral versus conventional datasets when being passed as input to machine learning algorithms, we implemented a weakly-supervised convolutional neural network (CNN) that learns liver lesion localisation without pixel-level ground truth annotations. Regions-of-interest are selected automatically based on the localisation results and are used to train a second CNN for liver lesion classification (healthy, cyst, hypodense metastasis). The accuracy of lesion localisation was evaluated using the Euclidian distances between the ground truth centres of mass and the predicted centres of mass. Lesion classification was evaluated by precision, recall, accuracy and F1-Score. RESULTS: Lesion localisation showed the best results for spectral information with distances of 8.22 ± 10.72 mm, 8.78 ± 15.21 mm and 8.29 ± 12.97 mm for iodine maps, 40 keV and 70 keV VMIs, respectively. With conventional data distances of 10.58 ± 17.65 mm were measured. For lesion classification, the 40 keV VMIs achieved the highest overall accuracy of 0.899 compared to 0.854 for conventional data. CONCLUSION: An enhanced localisation and classification is reported for spectral CT data, which demonstrates that combining machine-learning technology with spectral CT information may in the future improve the clinical workflow as well as the diagnostic accuracy.

Dual-layer spectral computed tomography: measuring relative electron density.

BACKGROUND: X-ray and particle radiation therapy planning requires accurate estimation of local electron density within the patient body to calculate dose delivery to tumour regions. We evaluate the feasibility and accuracy of electron density measurement using dual-layer computed tomography (DLCT), a recently introduced dual-energy CT technique. METHODS: Two calibration phantoms were scanned with DLCT and virtual monoenergetic images (VMIs) at 50 keV and 200 keV were generated. We investigated two approaches to obtain relative electron densities from these VMIs: to fit an analytic interaction cross-sectional model and to empirically calibrate a conversion function with one of the phantoms. Knowledge of the emitted x-ray spectrum was not required for the presented work. RESULTS: The results from both methods were highly correlated to the nominal values (R > 0.999). Except for the water and lung inserts, the error was within 1.79% (average 1.53%) for the cross-sectional model and 1.61% (average 0.87%) for the calibrated conversion. Different radiation doses did not have a significant influence on the measurement (p = 0.348, 0.167), suggesting that the methods are reproducible. Further, we applied these methods to routine clinical data. CONCLUSIONS: Our study shows a high validity of electron density estimation based on DLCT, which has potential to improve the procedure and accuracy of measuring electron density in clinical practice.

CNN as model observer in a liver lesion detection task for x-ray computed tomography: A phantom study.

PURPOSE: The purpose of this study was the evaluation of anthropomorphic model observers trained with neural networks for the prediction of a human observer's performance. METHODS: To simulate liver lesions, a phantom with contrast targets (acrylic spheres, varying diameters, +30 HU) was repeatedly scanned on a computed tomography scanner. Image data labeled with confidence ratings assessed in a reader study for a detection task of liver lesions were used to build several anthropomorphic model observers. Models were trained with images reconstructed with iterative reconstruction and evaluated with images reconstructed with filtered backprojection. A neural network, based on softmax regression (SR-MO), and convolutional neural networks (CNN-MO) were used to predict the performance of a human observer and compared to a channelized Hotelling observer [with Gabor channels and internal channel noise (CHOi)]. Model observers were evaluated by a receiver operating characteristic curve analysis and compared to the results in the reader study. Two strategies were used to train the SR-MO and CNN-MO: A) building a separate model for each lesion size; B) building one model that was applied to lesions of all sizes. RESULTS: All tested model observers and the human observer were highly correlated at each lesion size and dose level. With strategy A, Pearson's product-moment correlation coefficients r were 0.926 (95% confidence interval (CI): 0.679-0.985) for SR-MO and 0.979 (95% CI: 0.902-0.996) for CNN-MO. With strategy B, r was 0.860 (95% CI: 0.454-0.970) for SR-MO and 0.918 (95% CI: 0.651-0.983) for CNN-MO. For CHOi, r was 0.945 (95% CI: 0.755-0.989). With strategy A, mean absolute percentage differences (MAPD) between the model observers and the human observer were 3.7% for SR-MO and 1.2% for CNN-MO. With strategy B, MAPD were 3.7% for SR-MO and 3.0% for CNN-MO. For the CHOi the MAPD was 2.2%. CONCLUSION: Convolutional neural network model observers can accurately predict the performance of a human observer for all lesion sizes and dose levels in the evaluated signal detection task.

Structured reporting adds clinical value in primary CT staging of diffuse large B-cell lymphoma.

OBJECTIVES: To evaluate whether template-based structured reports (SRs) add clinical value to primary CT staging in patients with diffuse large B-cell lymphoma (DLBCL) compared to free-text reports (FTRs). METHODS: In this two-centre study SRs and FTRs were acquired for 16 CT examinations. Thirty-two reports were independently scored by four haematologists using a questionnaire addressing completeness of information, structure, guidance for patient management and overall quality. The questionnaire included yes-no, 10-point Likert scale and 5-point scale questions. Altogether 128 completed questionnaires were evaluated. Non-parametric Wilcoxon signed-rank test and McNemar's test were used for statistical analysis. RESULTS: SRs contained information on affected organs more often than FTRs (95 % vs. 66 %). More SRs commented on extranodal involvement (91 % vs. 62 %). Sufficient information for Ann-Arbor classification was included in more SRs (89 % vs. 64 %). Information extraction was quicker from SRs (median rating on 10-point Likert scale=9 vs. 6; 7-10 vs. 4-8 interquartile range). SRs had better comprehensibility (9 vs. 7; 8-10 vs. 5-8). Contribution of SRs to clinical decision-making was higher (9 vs. 6; 6-10 vs. 3-8). SRs were of higher quality (p < 0.001). All haematologists preferred SRs over FTRs. CONCLUSIONS: Structured reporting of CT examinations for primary staging in patients with DLBCL adds clinical value compared to FTRs by increasing completeness of reports, facilitating information extraction and improving patient management. KEY POINTS: • Structured reporting in CT helps clinicians to assess patients with lymphoma. • This two-centre study showed that structured reporting improves information content and extraction. • Patient management may be improved by structured reporting. • Clinicians preferred structured reports over free-text reports.

Experimental feasibility of spectral photon-counting computed tomography with two contrast agents for the detection of endoleaks following endovascular aortic repair.

OBJECTIVES: After endovascular aortic repair (EVAR), discrimination of endoleaks and intra-aneurysmatic calcifications within the aneurysm often requires multiphase computed tomography (CT). Spectral photon-counting CT (SPCCT) in combination with a two-contrast agent injection protocol may provide reliable detection of endoleaks with a single CT acquisition. METHODS: To evaluate the feasibility of SPCCT, the stent-lined compartment of an abdominal aortic aneurysm phantom was filled with a mixture of iodine and gadolinium mimicking enhanced blood. To represent endoleaks of different flow rates, the adjacent compartments contained either one of the contrast agents or calcium chloride to mimic intra-aneurysmatic calcifications. After data acquisition with a SPCCT prototype scanner with multi-energy bins, material decomposition was performed to generate iodine, gadolinium and calcium maps. RESULTS: In a conventional CT slice, Hounsfield units (HU) of the compartments were similar ranging from 147 to 168 HU. Material-specific maps differentiate the distributions within the compartments filled with iodine, gadolinium or calcium. CONCLUSION: SPCCT may replace multiphase CT to detect endoleaks without sacrificing diagnostic accuracy. It is a unique feature of our method to capture endoleak dynamics and allow reliable distinction from intra-aneurysmatic calcifications in a single scan, thereby enabling a significant reduction of radiation exposure. KEY POINTS: • SPCCT might enable advanced endoleak detection. • Material maps derived from SPCCT can differentiate iodine, gadolinium and calcium. • SPCCT may potentially reduce radiation burden for EVAR patients under post-interventional surveillance.

Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations.

Background The explosive growth of computer tomography (CT) has led to a growing public health concern about patient and population radiation dose. A recently introduced technique for dose reduction, which can be combined with tube-current modulation, over-beam reduction, and organ-specific dose reduction, is iterative reconstruction (IR). Purpose To evaluate the quality, at different radiation dose levels, of three reconstruction algorithms for diagnostics of patients with proven liver metastases under tumor follow-up. Material and Methods A total of 40 thorax-abdomen-pelvis CT examinations acquired from 20 patients in a tumor follow-up were included. All patients were imaged using the standard-dose and a specific low-dose CT protocol. Reconstructed slices were generated by using three different reconstruction algorithms: a classical filtered back projection (FBP); a first-generation iterative noise-reduction algorithm (iDose4); and a next generation model-based IR algorithm (IMR). Results The overall detection of liver lesions tended to be higher with the IMR algorithm than with FBP or iDose4. The IMR dataset at standard dose yielded the highest overall detectability, while the low-dose FBP dataset showed the lowest detectability. For the low-dose protocols, a significantly improved detectability of the liver lesion can be reported compared to FBP or iDose4 ( P = 0.01). The radiation dose decreased by an approximate factor of 5 between the standard-dose and the low-dose protocol. Conclusion The latest generation of IR algorithms significantly improved the diagnostic image quality and provided virtually noise-free images for ultra-low-dose CT imaging.

In-vivo X-ray Dark-Field Chest Radiography of a Pig.

X-ray chest radiography is an inexpensive and broadly available tool for initial assessment of the lung in clinical routine, but typically lacks diagnostic sensitivity for detection of pulmonary diseases in their early stages. Recent X-ray dark-field (XDF) imaging studies on mice have shown significant improvements in imaging-based lung diagnostics. Especially in the case of early diagnosis of chronic obstructive pulmonary disease (COPD), XDF imaging clearly outperforms conventional radiography. However, a translation of this technique towards the investigation of larger mammals and finally humans has not yet been achieved. In this letter, we present the first in-vivo XDF full-field chest radiographs (32 × 35 cm2) of a living pig, acquired with clinically compatible parameters (40 s scan time, approx. 80 µSv dose). For imaging, we developed a novel high-energy XDF system that overcomes the limitations of currently established setups. Our XDF radiographs yield sufficiently high image quality to enable radiographic evaluation of the lungs. We consider this a milestone in the bench-to-bedside translation of XDF imaging and expect XDF imaging to become an invaluable tool in clinical practice, both as a general chest X-ray modality and as a dedicated tool for high-risk patients affected by smoking, industrial work and indoor cooking.

Spectral Photon-counting CT: Initial Experience with Dual-Contrast Agent K-Edge Colonography.

Purpose To investigate the feasibility of using spectral photon-counting computed tomography (CT) to differentiate between gadolinium-based and nonionic iodine-based contrast material in a colon phantom by using the characteristic k edge of gadolinium. Materials and Methods A custom-made colon phantom was filled with nonionic iodine-based contrast material, and a gadolinium-filled capsule representing a contrast material-enhanced polyp was positioned on the colon wall. The colon phantom was scanned with a preclinical spectral photon-counting CT system to obtain spectral and conventional data. By fully using the multibin spectral information, material decomposition was performed to generate iodine and gadolinium maps. Quantitative measurements were performed within the lumen and polyp to quantitatively determine the absolute content of iodine and gadolinium. Results In a conventional CT section, absorption values of both contrast agents were similar at approximately 110 HU. Contrast material maps clearly differentiated the distributions, with gadolinium solely in the polyp and iodine in the lumen of the colon. Quantitative measurements of contrast material concentrations in the colon and polyp matched well with those of actual prepared mixtures. Conclusion Dual-contrast spectral photon-counting CT colonography with iodine-filled lumen and gadolinium-tagged polyps may enable ready differentiation between polyps and tagged fecal material. © RSNA, 2016.

Simultaneous dual-contrast multi-phase liver imaging using spectral photon-counting computed tomography: a proof-of-concept study.

BACKGROUND: To assess the feasibility of dual-contrast spectral photon-counting computed tomography (SPCCT) for liver imaging. METHODS: We present an SPCCT in-silico study for simultaneous mapping of the complementary distribution in the liver of two contrast agents (CAs) subsequently intravenously injected: a gadolinium-based contrast agent and an iodine-based contrast agent. Four types of simulated liver lesions with a characteristic arterial and portal venous pattern (haemangioma, hepatocellular carcinoma, cyst, and metastasis) are presented. A material decomposition was performed to reconstruct quantitative iodine and gadolinium maps. Finally, a multi-dimensional classification algorithm for automatic lesion detection is presented. RESULTS: Our simulations showed that with a single-scan SPCCT and an adapted contrast injection protocol, it was possible to reconstruct contrast-enhanced images of the liver with arterial distribution of the iodine-based CA and portal venous phase of the gadolinium-based CA. The characteristic patterns of contrast enhancement were visible in all liver lesions. The approach allowed for an automatic detection and classification of liver lesions using a multi-dimensional analysis. CONCLUSIONS: Dual-contrast SPCCT should be able to visualise the characteristic arterial and portal venous enhancement with a single scan, allowing for an automatic lesion detection and characterisation, with a reduced radiation exposure.

Detection of liver metastases in patients with adenocarcinomas of the gastrointestinal tract: comparison of (18)F-FDG PET/CT and MR imaging.

PURPOSE: Aim of our study was to compare the diagnostic performance of (18)F-FDG PET/CT and MR imaging (MRI) in the detection of liver metastases in patients with adenocarcinomas of the gastrointestinal tract. METHODS: A total of 49 patients with adenocarcinomas of the gastrointestinal tract who had undergone (18)F-FDG PET/CT and MRI of the liver were included in this study. The MRI protocol included diffusion-weighted imaging and dynamic contrast-enhanced MR imaging after intravenous injection of Gd-DTPA. PET and MR images were analyzed by two experienced radiologists. Imaging results were correlated with histopathological findings or imaging follow-up as available. Sensitivities of both modalities were compared using McNemar Test. Receiver operating characteristic (ROC) curves were calculated to determine the diagnostic performance in correctly identifying liver metastases. RESULTS: A total of 151 metastases were confirmed. For lesion detection, MRI was significantly superior to (18)F-FDG PET/CT. Sensitivity of MRI in detecting metastases was 86.8% for Reader 1 (R1) and 87.4% for Reader 2 (R2), of PET/CT 66.2% for R1 and 68.2% for R2. Regarding only metastases with diameters of 10 mm or less, sensitivities of MRI were 66.7% for R1 and 75.0% for R2, and were significantly higher than those of PET/CT (17.9% for R1 and 20.5% for R2). ROC analysis showed superiority for lesion classification of MRI as compared to (18)F-FDG PET/CT. CONCLUSION: MRI is significantly superior to (18)F-FDG PET/CT in the detection and classification of liver metastases in patients with adenocarcinomas of the gastrointestinal tract, especially in the detection of small metastases.

IKKα promotes intestinal tumorigenesis by limiting recruitment of M1-like polarized myeloid cells.

The recruitment of immune cells into solid tumors is an essential prerequisite of tumor development. Depending on the prevailing polarization profile of these infiltrating leucocytes, tumorigenesis is either promoted or blocked. Here, we identify IκB kinase α (IKKα) as a central regulator of a tumoricidal microenvironment during intestinal carcinogenesis. Mice deficient in IKKα kinase activity are largely protected from intestinal tumor development that is dependent on the enhanced recruitment of interferon γ (IFNγ)-expressing M1-like myeloid cells. In IKKα mutant mice, M1-like polarization is not controlled in a cell-autonomous manner but, rather, depends on the interplay of both IKKα mutant tumor epithelia and immune cells. Because therapies aiming at the tumor microenvironment rather than directly at the mutated cancer cell may circumvent resistance development, we suggest IKKα as a promising target for colorectal cancer (CRC) therapy.