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Spectral computed tomography (CT) imaging encompasses a unique generation of CT systems based on a simple principle that makes use of the energy-dependent information present in CT images. Over the past two decades this principle has been expanded with the introduction of dual-energy CT systems. The first generation of spectral CT systems, represented either by dual-source or dual-layer technology, opened up a new imaging approach in the radiology community with their ability to overcome the limitations of tissue characterization encountered with conventional CT. Its expansion worldwide can also be considered as an important leverage for the recent groundbreaking technology based on a new chain of detection available on photon counting CT systems, which holds great promise for extending CT towards multi-energy CT imaging. The purpose of this article was to detail the basic principles and techniques of spectral CT with a particular emphasis on the newest technical developments of dual-energy and multi-energy CT systems.

Significant work has been done to develop nanoparticle contrast agents for computed tomography (CT), with a focus on identifying safer and more effective formulations. Contrast agents for spectral photon-counting computed tomography (SPCCT), a fast-growing imaging modality derived from conventional CT, have also recently gained considerable attention. In this study, we explored the synthesis of ultrasmall ytterbium nanoparticles (YbNP) and demonstrated that, potentially, they can be used as conventional CT and SPCCT contrast agents. These nanoparticles were tested

International audience; BackgroundThe present article is an English-language version of the French National Diagnostic and Care Protocol, a pragmatic tool to optimize and harmonize the diagnosis, care pathway, management and follow-up of lymphangioleiomyomatosis in France.MethodsPractical recommendations were developed in accordance with the method for developing a National Diagnosis and Care Protocol for rare diseases of the Haute Autorité de Santé and following international guidelines and literature on lymphangioleiomyomatosis. It was developed by a multidisciplinary group, with the help of patient representatives and of RespiFIL, the rare disease network on respiratory diseases.ResultsLymphangioleiomyomatosis is a rare lung disease characterised by a proliferation of smooth muscle cells that leads to the formation of multiple lung cysts. It occurs sporadically or as part of a genetic disease called tuberous sclerosis complex (TSC). The document addresses multiple aspects of the disease, to guide the clinicians regarding when to suspect a diagnosis of lymphangioleiomyomatosis, what to do in case of recurrent pneumothorax or angiomyolipomas, what investigations are needed to make the diagnosis of lymphangioleiomyomatosis, what the diagnostic criteria are for lymphangioleiomyomatosis, what the principles of management are, and how follow-up can be organised. Recommendations are made regarding the use of pharmaceutical specialties and treatment other than medications.ConclusionThese recommendations are intended to guide the diagnosis and practical management of pulmonary lymphangioleiomyomatosis.

To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications.Acquisitions on phantoms were performed at 5 dose levels (CTDIFrom Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d') of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d' values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level.Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality.• Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.

Resume Introduction Dans le cadre de la reforme du deuxieme cycle des etudes medicales en France, l’accent est particulierement mis sur l’evaluation des competences des etudiants en utilisant un outil standardise dit « ECOS » pour examen clinique objectif et structure. Donnees recentes Cet outil, largement utilise dans de nombreux pays comme le Canada, la Suisse et l’Inde, se compose d’une succession d’ateliers de simulation d’une competence de courte duree permettant une evaluation standardisee, contrairement aux methodes d’evaluation dites traditionnelles. Pour ce qui concerne le deuxieme cycle des etudes medicales en France, l’etudiant, pendant sa formation approfondie en sciences medicales devra acquerir des connaissances de radio-anatomie, devra connaitre les principales indications des examens d’imagerie et enfin avoir des connaissances sur la semeiologie en imagerie des principales urgences et pathologies qu’un medecin de premier recours pourra rencontrer. Conclusion L’ECOS va permettre ainsi d’evaluer l’ensemble des competences requises a la pratique de l’imagerie medicale pour un etudiant de deuxieme cycle d’etudes.

Objectives: To evaluate the image quality (IQ) of a spectral photon-counting CT (SPCCT) using filtered back projection (FBP) and hybrid iterative reconstruction (IR) algorithms (iDose4), in comparison with a dual-layer CT (DLCT) system, and to choose the best image quality according to the IR level for SPCCT. Methods: Two phantoms were scanned using a standard lung protocol (120 kVp, 40 mAs) with SPCCT and DLCT systems. Raw data were reconstructed using FBP and 9 iDose4 levels (i1/i2/i3/i4/i5/i6/i7/i9/i11) for SPCCT and 7 for DLCT (i1/i2/i3/i4/i5/i6/i7). Noise power spectrum and task-based transfer function (TTF) were computed. Detectability index (d′) was computed for detection of 4 mm ground-glass nodule (GGN) and solid nodule. Two chest radiologists performed an IQ evaluation (noise/nodule sharpness/nodule conspicuity/overall IQ) in consensus, and chose the best image for SPCCT. Results: Noise magnitude was −47% ± 2% lower on average with SPCCT than with DLCT for iDose4 range from i1 to i6. Average NPS spatial frequencies increased for SPCCT in comparison with DLCT. TTF also increased, except for the air insert with FBP, and i1/i2/i3. Higher detectability was found for SPCCT for both GGN and solid nodules. IQ for both types of nodule was rated consistently higher with SPCCT than with DLCT for the same iDose4 level. For SPCCT and both nodules, the scores for noise and conspicuity improved with increasing iDose4 level. iDose4 level 6 provided the best subjective IQ for both types of nodule. Conclusions: Higher IQ for GGN and solid nodules was demonstrated with SPCCT compared with DLCT with better detectability using iDose4. Key Points: Using spectral photon-counting CT compared with dual-layer CT, noise magnitude was reduced with improvements in spatial resolution and detectability of ground-glass nodules and solid lung nodules.As the iDose4level increased, noise magnitude was reduced and detectability of ground-glass and solid lung nodules was better for both CT systems.For spectral photon-counting CT imaging, two chest radiologists determined iDose4level 6 as the best image quality for detecting ground-glass nodules and solid lung nodules.

Background Pleuroparenchymal fibroelastosis (PPFE) has a variable disease course with dismal prognosis in the majority of patients with no validated drug therapy. This study is to evaluate the effect of nintedanib in patients with idiopathic and secondary PPFE. Patients admitted to a tertiary care center (2010–2019) were included into this retrospective analysis if they had a multidisciplinary diagnosis of PPFE, had been followed-up for 3 months or more, and had lung function tests and chest CTs available for review. Changes in pulmonary function tests were assessed using non-parametric tests and linear mixed effect model. Lung volumes were measured with lobar segmentation using chest CT. Results Out of 21 patients with PPFE, nine had received nintedanib, six had received another treatment and another six patients were monitored without drug therapy. Annual FVC (% of predicted) relative decline was − 13.6 ± 13.4%/year before nintedanib and − 1.6 ± 6.02%/year during nintedanib treatment (p = 0.014), whereas no significant change in FVC% relative decline was found in patients receiving another treatment (− 13.25 ± 34 before vs − 16.61 ± 36.2%/year during treatment; p = 0.343). Using linear mixed effect model, the slope in FVC was − 0.97%/month (95% CI: − 1.42; − 0.52) before treatment and − 0.50%/month (95% CI: − 0.88; 0.13) on nintedanib, with a difference between groups of + 0.47%/month (95% CI: 0.16; 0.78), p = 0.004. The decline in the upper lung volumes measured by CT was − 233 mL/year ± 387 mL/year before nintedanib and − 149 mL/year ± 173 mL/year on nintedanib (p = 0.327). Nintedanib tolerability was unremarkable. Conclusion In patients with PPFE, nintedanib treatment might be associated with slower decline in lung function, paving the way for prospective, controlled studies.

Spectral Photon-Counting Computed Tomography (SPCCT) is a new technique with the capability to provide mono-energetic (monoE) images with high signal to noise ratio. We demonstrate the feasibility of SPCCT to characterize at the same time cartilage and subchondral bone cysts (SBCs) without contrast agent in osteoarthritis (OA). To achieve this goal, 5 human knee specimens (3 normal and 2 with OA) were imaged with a clinical prototype SPCCT. The monoE images at 60 keV with isotropic voxels of 250x250x250 µm3 were compared with monoE synchrotron radiation CT (SR CT) images at 55 keV with isotropic voxels of 45x45x45 µm3 used as benchmark for cartilage segmentation. The volume and density of SBCs were evaluated in the two OA knees in SPCCT images. In 25 compartments (tibial lateral, tibial medial, femoral lateral, femoral medial and patella), the mean bias between SPCCT and SR CT analyses were 106 ± 196mm3 for cartilage volume and − 0.006mm ± 0.06 for mean cartilage thickness. The 2 OA knees displayed different SBCs profiles in terms of volume, density, and distribution according to size and location. SPCCT with fast acquisitions is able to characterize cartilage morphology and SBCs. SPCCT can be used potentially as a new tool in clinical studies in OA.

National audience; BACKGROUND: Since the previous French guidelines were published in 2017, substantial additional knowledge about idiopathic pulmonary fibrosis has accumulated. METHODS: Under the auspices of the French-speaking Learned Society of Pulmonology and at the initiative of the coordinating reference center, practical guidelines for treatment of rare pulmonary diseases have been established. They were elaborated by groups of writers, reviewers and coordinators with the help of the OrphaLung network, as well as pulmonologists with varying practice modalities, radiologists, pathologists, a general practitioner, a head nurse, and a patients’ association. The method was developed according to rules entitled "Good clinical practice" in the overall framework of the "Guidelines for clinical practice" of the official French health authority (HAS), taking into account the results of an online vote using a Likert scale. RESULTS: After analysis of the literature, 54 recommendations were formulated, improved, and validated by the working groups. The recommendations covered a wide-ranging aspects of the disease and its treatment: epidemiology, diagnostic modalities, quality criteria and interpretation of chest CT, indication and modalities of lung biopsy, etiologic workup, approach to familial disease entailing indications and modalities of genetic testing, evaluation of possible functional impairments and prognosis, indications for and use of antifibrotic therapy, lung transplantation, symptom management, comorbidities and complications, treatment of chronic respiratory failure, diagnosis and management of acute exacerbations of fibrosis. CONCLUSION: These evidence-based guidelines are aimed at guiding the diagnosis and the management in clinical practice of idiopathic pulmonary fibrosis.

Objectives To quantitatively evaluate the impact of virtual monochromatic images (VMI) on reduced-iodine-dose dual-energy coronary computed tomography angiography (CCTA) in terms of coronary lumen segmentation in vitro, and secondly to assess the image quality in vivo, compared with conventional CT obtained with regular iodine dose. Materials and methods A phantom simulating regular and reduced iodine injection was used to determine the accuracy and precision of lumen area segmentation for various VMI energy levels. We retrospectively included 203 patients from December 2017 to August 2018 (mean age, 51.7 ± 16.8 years) who underwent CCTA using either standard (group A, n = 103) or reduced (group B, n = 100) iodine doses. Conventional images (group A) were qualitatively and quantitatively compared with 55-keV VMI (group B). We recorded the location of venous catheters. Results In vitro, VMI outperformed conventional CT, with a segmentation accuracy of 0.998 vs. 1.684 mm2, respectively (p < 0.001), and a precision of 0.982 vs. 1.229 mm2, respectively (p < 0.001), in simulated overweight adult subjects. In vivo, the rate of diagnostic CCTA in groups A and B was 88.4% (n = 91/103) vs. 89% (n = 89/100), respectively, and noninferiority of protocol B was inferred. Contrast-to-noise ratios (CNR) of lumen versus fat and muscle were higher in group B (p < 0.001) and comparable for lumen versus calcium (p = 0.423). Venous catheters were more often placed on the forearm or hand in group B (p < 0.001). Conclusion In vitro, low-keV VMI improve vessel area segmentation. In vivo, low-keV VMI allows for a 40% iodine dose and injection rate reduction while maintaining diagnostic image quality and improves the CNR between lumen versus fat and muscle. Key Points • Dual-energy coronary CT angiography is becoming increasingly available and might help improve patient management. • Compared with regular-iodine-dose coronary CT angiography, reduced-iodine-dose dual-energy CT with low-keV monochromatic image reconstructions performed better in phantom-based vessel cross-sectional segmentation and proved to be noninferior in vivo. • Patients receiving reduced-iodine-dose dual-energy coronary CT angiography often had the venous catheter placed on the forearm or wrist without compromising image quality.

Pleuroparenchymal fibroelastosis (PPFE) is a clinico-radiologic-pathologic interstitial lung disease (ILD) characterized by fibrosis that has upper lobe and subpleural predominance, involving both the visceral pleura and the subjacent subpleural lung parenchyma, and comprises dense fibroelastic changes with prominent elastosis of the alveolar walls together with fibrous thickening of the visceral pleura. The goal of this review is to summarize the state-of-the-art understanding in PPFE.PPFE was described in an increasing number of conditions. The course of disease is heterogeneous. Idiopathic PPFE, cases associated with telomerase-related gene mutations, cases related to a history of chemotherapy, and cases combining PPFE with a pattern of usual interstitial pneumonia, may have a particularly poor prognosis. Well-conducted retrospective studies identified marked PPFE features in approximately 10% of patients with idiopathic pulmonary fibrosis, 11% of patients with systemic sclerosis-associated ILD, 6.5% of patients with rheumatoid arthritis-associated ILD, and 23% of patients with hypersensitivity pneumonitis. Drug therapy has not been evaluated prospectively. A small retrospective study suggests that nintedanib may slow disease progression. However, whether the efficacy of antifibrotics is comparable in PPFE and in other forms of progressive pulmonary fibrosis warrants further evaluation.Accumulating data indicate that PPFE features are associated with poor prognosis in fibrosing ILDs. Further research on the management of PPFE is warranted.

To compare the spectral performance of dual-energy CT (DECT) platforms using task-based image quality assessment based on phantom data. Two CT phantoms were scanned on four DECT platforms: fast kV-switching CT (KVSCT), split filter CT (SFCT), dual-source CT (DSCT), and dual-layer CT (DLCT). Acquisitions on each phantom were performed using classical parameters of abdomen-pelvic examination and a CTDIvol at 10 mGy. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 140 keV of virtual monoenergetic images. A detectability index (d′) was computed to model the detection task of two contrast-enhanced lesions as function of keV. The noise magnitude decreased from 40 to 70 keV for all DECT platforms, and the highest noise magnitude values were found for KVSCT and SFCT and the lowest for DSCT and DLCT. The average NPS spatial frequency shifted towards lower frequencies as the energy level increased for all DECT platforms, smoothing the image texture. TTF values decreased with the increase of keV deteriorating the spatial resolution. For both simulated lesions, higher detectability (d′ value) was obtained at 40 keV for DLCT, DSCT, and SFCT but at 70 keV for KVSCT. The detectability of both simulated lesions was highest for DLCT and DSCT. Highest detectability was found for DLCT for the lowest energy levels. The task-based image quality assessment used for the first time for DECT acquisitions showed the benefit of using low keV for the detection of contrast-enhanced lesions. • Detectability of both simulated contrast-enhanced lesions was higher for dual-layer CT for the lowest energy levels. • The image noise increased and the image texture changed for the lowest energy levels. • The detectability of both simulated contrast-enhanced lesions was highest at 40 keV for all dual-energy CT platforms except for fast kV-switching platform.

Computed tomography imaging plays a major role in the preoperative assessment of tumor burden by providing an accurate mapping of the distribution of peritoneal metastases (PM). Spectral Photon Counting Computed Tomography (SPCCT) is an innovative imaging modality that could overcome the current limitations of conventional CT, offering not only better spatial resolution but also better contrast resolution by allowing the discrimination of multiple contrast agents. Based on this capability, we tested the feasibility of SPCCT in the detection of PM at different time of tumor growth in 16 rats inoculated with CC531 cells using dual-contrast injection protocols in two compartments (i.e. intravenous iodine and intraperitoneal gadolinium or the reverse protocol), compared to surgery. For all peritoneal regions and for both protocols, sensitivity was 69%, specificity was 100% and accuracy was 80%, and the correlation with surgical exploration was strong (p = 0.97; p = 0.0001). No significant difference was found in terms of diagnostic performance, quality of peritoneal opacification or diagnostic quality between the 2 injection protocols. We also showed poor vascularization of peritoneal metastases by measuring low concentrations of contrast agent in the largest lesions using SPCCT, which was confirmed by immunohistochemical analyses. In conclusion, SPCCT using dual-contrast agent injection protocols in 2 compartments is a promising imaging modality to assess the extent of PM in a rat model.

To compare the quantitative and qualitative lung perfusion data acquired with dual energy CT (DECT) to that acquired with a large field-of-view cadmium-zinc-telluride camera single-photon emission CT coupled to a CT system (SPECT-CT).A total of 53 patients who underwent both dual-layer DECT angiography and perfusion SPECT-CT for pulmonary hypertension or pre-operative lobar resection surgery were retrospectively included. There were 30 men and 23 women with a mean age of 65.4±17.5 (SD)years (range: 18-88years). Relative lobar perfusion was calculated by dividing the amount (of radiotracer or iodinated contrast agent) per lobe by the total amount in both lungs. Linear regression, Bland-Altman analysis, and Pearson's correlation coefficient were also calculated. Kappa test was used to test agreements in morphology and severity of perfusion defects assessed on SPECT-CT and on DECT iodine maps with a one-month interval. Wilcoxon rank sum test was used to compare the sharpness of perfusion defects and radiation dose among modalities.Strong correlations for relative lobar perfusion using linear regression analysis and Pearson's correlation coefficient (r=0.93) were found. Bland-Altman analysis revealed a -0.10 bias, with limits of agreement between [-6.01; 5.81]. With respect to SPECT- CT as standard of reference, the sensitivity, specificity, PPV, NPV, accuracy for lobar perfusion defects were 89.4% (95% CI: 82.6-93.4%), 96.5% (95% CI: 92.1-98.5%), 95.6% (95% CI: 90.9-97.8%), 91.4% (95% CI: 85.6-94.9%) and 93.0% (95% CI: 87.6-96.1%) respectively. High level of agreement was found for morphology and severity of perfusion defects between modalities (Kappa=0.84 and 0.86 respectively) and on DECT images among readers (Kappa=0.94 and 0.89 respectively). A significantly sharper delineation of perfusion defects was found on DECT images (P0.0001) using a significantly lower equivalent dose of 4.1±2.3 (SD) mSv (range: 1.9-11.85mSv) compared to an equivalent dose of 5.3±1.1 (SD) mSv (range: 2.8-7.3mSv) for SPECT-CT, corresponding to a 21.2% dose reduction (P=0.0004).DECT imaging shows strong quantitative correlations and qualitative agreements with SPECT-CT for the evaluation of lung perfusion.

Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels.To assess the impact of an artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications.Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU-41 phantoms were performed at five dose levels (CTDIFrom Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: -66.3% ± 0.5% for mediastinal images and -63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: -35.3% ± 2.2% for mediastinal images and -13.3% ± 2.2% for parenchymal images), and the detectability (d') of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low-contrast polyethylene insert and the opposite for the high-contrast air insert. Compared to the i4 used in clinical practice, d' values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required.With AI-DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

Background There is a paucity of data on the epidemiology, survival estimates and healthcare resource utilisation and associated costs of patients with progressive fibrosing interstitial lung disease (PF-ILD) in France. An algorithm for extracting claims data was developed to indirectly identify and describe patients with PF-ILD in the French national administrative healthcare database. Methods The French healthcare database, the Système National des Données de Santé (SNDS), includes data related to ambulatory care, hospitalisations and death for 98.8% of the population. In this study, algorithms based on age, diagnosis and healthcare consumption were created to identify adult patients with PF-ILD other than idiopathic pulmonary fibrosis between 2010 and 2017. Incidence, prevalence, survival estimates, clinical features and healthcare resource usage and costs were described among patients with PF-ILD. Results We identified a total of 14,413 patients with PF-ILD. Almost half of them (48.1%) were female and the mean (± standard deviation) age was 68.4 (± 15.0) years. Between 2010 and 2017, the estimated incidence of PF-ILD ranged from 4.0 to 4.7/100,000 person-years and the estimated prevalence from 6.6 to 19.4/100,000 persons. The main diagnostic categories represented were exposure-related ILD other than hypersensitivity pneumonitis (n = 3486; 24.2%), idiopathic interstitial pneumonia (n = 3113; 21.6%) and rheumatoid arthritis-associated ILD (n = 2521; 17.5%). Median overall survival using Kaplan–Meier estimation was 3.7 years from the start of progression. During the study, 95.2% of patients had ≥ 1 hospitalisation for respiratory care and 34.3% were hospitalised in an intensive care unit. The median (interquartile range) total specific cost per patient during the follow-up period was €25,613 (10,622–54,287) and the median annual cost per patient was €18,362 (6856–52,026), of which €11,784 (3003–42,097) was related to hospitalisations. Limitations included the retrospective design and identification of cases through an algorithm in the absence of chest high-resolution computed tomography scans and pulmonary function tests. Conclusions This large, real-world, longitudinal study provides important insights into the characteristics, epidemiology and healthcare resource utilisation and costs associated with PF-ILD in France using a comprehensive and exhaustive database, and provides vital evidence that PF-ILD represents a high burden on both patients and healthcare services. Trial registration ClinicalTrials.gov, NCT03858842. ISRCTN, ISRCTN12345678. Registered 3 January 2019—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03858842

Background Dual-energy computed tomography has shown a great interest for musculoskeletal pathologies. Photon-counting spectral computed tomography (PCSCT) can acquire data in multiple energy bins with the potential to increase contrast, especially for soft tissues. Our objectives were to assess the value of PCSST to characterise cartilage and to extract quantitative measures of subchondral bone integrity. Methods Seven excised human knees (3 males and 4 females; 4 normal and 3 with osteoarthritis; age 80.6 ± 14 years, mean ± standard deviation) were scanned using a clinical PCSCT prototype scanner. Tomographic image reconstruction was performed after Compton/photoelectric decomposition. Virtual monoenergetic images were generated from 40 keV to 110 keV every 10 keV (cubic voxel size 250 × 250 × 250 μm3). After selecting an optimal virtual monoenergetic image, we analysed the grey level histograms of different tissues and extracted quantitative measurements on bone cysts. Results The optimal monoenergetic images were obtained for 60 keV and 70 keV. Visual inspection revealed that these images provide sufficient spatial resolution and soft-tissue contrast to characterise surfaces, disruption, calcification of cartilage, bone osteophytes, and bone cysts. Analysis of attenuation versus energy revealed different energy fingerprint according to tissues. The volumes and numbers of bone cyst were quantified. Conclusions Virtual monoenergetic images may provide direct visualisation of both cartilage and bone details. Thus, unenhanced PCSCT appears to be a new modality for characterising the knee joint with the potential to increase the diagnostic capability of computed tomography for joint diseases and osteoarthritis.

To assess the capability of a newly developed material decomposition method from contrast-enhanced dual-energy CT images, aiming to better visualize the aortic wall and aortic intramural hematoma (IMH), compared with true non-contrast (TNC) CT. Twenty-two patients (11 women; mean age, 61 ± 20 years) with acute chest pain underwent 25 dual-layer non-contrast and contrast-enhanced CT. CT-angiography images were retrospectively processed using two-material decomposition analysis, where we defined the first material as the content of a region of interest placed in the ascending aorta for each patient, and the second material as water. Two independent radiologists assessed the images from the second material termed “dark-blood” images and the TNC images regarding contrast-to-noise ratio (CNR) between the wall and the lumen, diagnostic quality regarding the presence of aortic wall thickening, and the inner/outer vessel wall conspicuity. Diagnostic quality scores in normal aortic segments were 0.9 ± 0.3 and 2.7 ± 0.6 (p

To assess whether virtual non-contrast (VNC) images derived from contrast dual-layer dual-energy computed tomography (DL-DECT) images could replace true non-contrast (TNC) images for aortic intramural hematoma (IMH) diagnosis in acute aortic syndrome (AAS) imaging protocols by performing quantitative as well as qualitative phantom and clinical studies. Patients with confirmed IMH were included retrospectively in two centers. For in vitro imaging, a custom-made phantom of IMH was placed in a semi-anthropomorphic thorax phantom (QRM GmbH) and imaged on a DL-DECT at 120 kVp under various conditions of patient size, radiation exposure, and reconstruction modes. For in vivo imaging, 21 patients (70 ± 13 years) who underwent AAS imaging protocols at 120 kVp were included. In both studies, contrast-to-noise ratio (CNR) between hematoma and lumen was compared using a paired t test. Diagnostic confidence (1 = non-diagnostic, 4 = exemplary) for VNC and TNC images was rated by two radiologists and compared. Effective radiation doses for each acquisition were calculated. In both the phantom and clinical studies, we observed that the CNRs were similar between the VNC and TNC images. Moreover, both methods allowed differentiating the hyper-attenuation within the hematoma from the blood. Finally, we obtained equivalent high diagnostic confidence with both VNC and TNC images (VNC = 3.2 ± 0.7, TNC = 3.1 ± 0.7; p = 0.3). Finally, by suppressing TNC acquisition and using VNC, the mean effective dose reduction would be 40%. DL-DECT offers similar performances with VNC and TNC images for IMH diagnosis without compromise in diagnostic image quality. • Dual-layer dual-energy CT enables virtual non-contrast imaging from a contrast-enhanced acquisition. • Virtual non-contrast imaging with dual-layer dual-energy CT reduces the number of acquisitions and radiation exposure in acute aortic syndrome imaging protocol. • Dual-layer dual-energy CT has the potential to become a suitable imaging tool for acute aortic syndrome.

Purpose This work presents our contribution to a data challenge organized by the French Radiology Society during the Journees Francophones de Radiologie in October 2018. This challenge consisted in classifying MR images of the knee with respect to the presence of tears in the knee menisci, on meniscal tear location, and meniscal tear orientation. Materials and methods We trained a mask region-based convolutional neural network (R-CNN) to explicitly localize normal and torn menisci, made it more robust with ensemble aggregation, and cascaded it into a shallow ConvNet to classify the orientation of the tear. Results Our approach predicted accurately tears in the database provided for the challenge. This strategy yielded a weighted AUC score of 0.906 for all three tasks, ranking first in this challenge. Conclusion The extension of the database or the use of 3D data could contribute to further improve the performances especially for non-typical cases of extensively damaged menisci or multiple tears.

PurposeThe objective of this technical note was to investigate the accuracy of proton stopping power relative to water (RSP) estimation using a novel dual-layer, dual-energy computed tomography (DL-DECT) scanner for potential use in proton therapy planning. DL-DECT allows dual-energy reconstruction from scans acquired at a single x-ray tube voltage V by using two-layered detectors.MethodsSets of calibration and evaluation inserts were scanned at a DL-DECT scanner in a custom phantom with variable diameter D (0 to 150mm) at V of 120 and 140kV. Inserts were additionally scanned at a synchrotron computed tomography facility to obtain comparative linear attenuation coefficients for energies from 50 to 100keV, and reference RSP was obtained using a carbon ion beam and variable water column. DL-DECT monoenergetic (mono-E) reconstructions were employed to obtain RSP by adapting the Yang-Saito-Landry (YSL) method. The method was compared to reference RSP via the root mean square error (RMSE) over insert mean values obtained from volumetric regions of interest. The accuracy of intermediate quantities such as the relative electron density (RED), effective atomic number (EAN), and the mono-E was additionally evaluated.ResultsThe lung inserts showed higher errors for all quantities and we report RMSE excluding them. RMSE for from DL-DECT mono-E was below 1.9%. For the evaluation inserts at D=150mm and V=140kV, RED RMSE was 1.0%, while for EAN it was 2.9%. RSP RMSE was below 0.8% for all D and V, which did not strongly affect the results.ConclusionsIn this investigation of RSP accuracy from DL-DECT, we have shown that RMSE below 1% can be achieved. It was possible to adapt the YSL method for DL-DECT and intermediate quantities RED and EAN had comparable accuracy to previous publications.

BACKGROUND: To assess the spectral performance of rapid kV switching dual-energy CT (KVSCT-Canon) equipped with a Deep-Learning spectral reconstruction algorithm on virtual-monoenergetic images at low-energy levels and to compare its performances with four other dual-energy CT (DECT) platforms equipped with iterative reconstruction algorithms. METHODS: Two CT phantoms were scanned on five DECT platforms: KVSCT-Canon, fast kV-switching CT (KVSCT-GE), split filter CT, dual-source CT (DSCT), and dual-layer CT (DLCT). The classical parameters of abdomen-pelvic examinations were used for all phantom acquisitions, and a CTDI(vol) close to 10 mGy. For KVSCT-Canon, virtual-monoenergetic images were reconstructed with a clinical slice thickness of 0.5 and 1.5 mm to be close to other platforms. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of virtual-monoenergetic images. A detectability index (d’) was computed to model the detection task of two contrast-enhanced lesions as function of keV. RESULTS: For KVSCT-Canon, the noise magnitude and average NPS spatial frequency (f(av)) decreased from 40 to 70 keV and increased thereafter. Similar noise magnitude outcomes were found for KVSCT-GE but the opposite for f(av). For the other DECT platforms, the noise magnitude decreased as the keV increased. For split filter CT, DSCT and DLCT, the f(av) values increased from 40 to 80 keV. For all DECT platforms, TTF at 50% (f(50)) decreased as the keV increased, decreasing spatial resolution. For KVSCT-Canon, d’ values peaked at 60 and 70 keV for both simulated lesions and from 50 to 70 keV for KVSCT-GE. d’ decreased between 40 and 70 keV for DSCT, DLCT and split filter CT. For KVSCT-Canon, the increase in slice thickness decreases noise magnitude, f(av) and f(50) and increases d’ values. The highest d’ values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV. CONCLUSIONS: For KVSCT-Canon, the detectability of contrast-enhanced lesions was highest at 60 keV. The highest d’ values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV.

Purpose The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications. Material and methods Acquisitions on image quality and anthropomorphic phantoms were performed at six dose levels (CTDIvol: 10/7.5/5/2.5/1/0.5mGy) on two CT scanners equipped with two different DLR algorithms (TrueFidelityTM and AiCE). Raw data were reconstructed using the filtered back-projection (FBP) and the lowest/intermediate/highest DLR levels (L-DLR/M-DLR/H-DLR) of each algorithm. Noise power spectrum, task-based transfer function (TTF) and detectability index (d’) were computed: d’ modelled detection of a soft tissue mediastinal nodule, ground-glass opacity, or high-contrast pulmonary lesion. Subjective image quality of anthropomorphic phantom images was analyzed by two radiologists. Results For the L-DLR/M-DLR levels, the noise magnitude was lower with TrueFidelityTM than with AiCE from 2.5 to 10 mGy. For H-DLR, noise magnitude was lower with AiCE . For L-DLR and M-DLR, the average NPS spatial frequency (fav) values were greater for AiCE except for 0.5 mGy. For H-DLR levels, fav was greater for TrueFidelityTM than for AiCE. TTF50% values were greater with AiCE for the air insert, and lower than TrueFidelityTM for the polyethylene insert. From 2.5 to10 mGy, d’ was greater for AiCE than for TrueFidelityTM for H-DLR for all lesions, but similar for L-DLR and M-DLR. Image quality was rated clinically appropriate for all levels of both algorithms, for dose from 2.5 to 10 mGy, except for L-DLR of AiCE. Conclusion DLR algorithms reduce the image-noise and improve lesion detectability. Their operations and properties impacted both noise-texture and spatial resolution.

Objective The aim of the current study was, first, to assess the coronary artery calcium (CAC) scoring potential of spectral photon-counting CT (SPCCT) in comparison with computed tomography (CT) for routine clinical protocols. Second, improved CAC detection and quantification at reduced slice thickness were assessed. Methods Raw data was acquired and reconstructed with several combinations of reduced slice thickness and increasing strengths of iterative reconstruction (IR) for both CT systems with routine clinical CAC protocols for CT. Two CAC-containing cylindrical inserts, consisting of CAC of different densities and sizes, were placed in an anthropomorphic phantom. A specific CAC was detectable when 3 or more connected voxels exceeded the CAC scoring threshold of 130 Hounsfield units (HU). For all reconstructions, total CAC detectability was compared between both CT systems. Significant differences in CAC quantification (Agatston and volume scores) were assessed with Mann-Whitney U tests. Furthermore, volume scores were compared with the known CAC physical. Results CAC scores for routine clinical protocols were comparable between SPCCT and CT. SPCCT showed 34% and 4% higher detectability of CAC for the small and large phantom, respectively. At reduced slice thickness, CAC detection increased by 142% and 169% for CT and SPCCT, respectively. In comparison with CT, volume scores from SPCCT were more comparable with the physical volume of the CAC. Conclusion CAC scores using routine clinical protocols are comparable between conventional CT and SPCCT. The increased spatial resolution of SPCCT allows for increased detectability and more accurate CAC volume estimation. Key Points • Coronary artery calcium scores using routine clinical protocols are comparable between conventional CT and spectral photon-counting CT. • In comparison with conventional CT, increased coronary artery calcium detectability was shown for spectral photon-counting CT due to increased spatial resolution. • Volumes scores were more accurately determined with spectral photon-counting CT.

International audience; Denoising algorithms via sparse representation are among the state-of-the art for image restoration. On previous work, we proposed SPADE-a sparse-and prior-based method for 3D-image denoising. In this work, we extend this idea to learning approaches and propose a novel residual-U-Net prior-based (ResPrU-Net) method that exploits a prior image. The proposed ResPrU-Net architecture has two inputs, the noisy image and the prior image, and a residual connection that connects the prior image to the output of the network. We compare ResPrU-Net to U-Net and SPADE on human knee data acquired on a spectral computerized tomography scanner. The prior image is built from the noisy image by combining information from neighbor slices and it is the same for both SPADE and ResPrU-Net. For deep learning approaches, we use four knee samples and data augmentation for training, one knee for validation and two for test. Results show that for high noise, U-Net leads to worst results, with images that are excessively blurred. Prior-based methods, SPADE and ResPrU-Net, outperformed U-Net, leading to restored images that present similar image quality than the target. ResPrU-Net provides slightly better results than SPADE. For low noise, methods present similar results.