Effect of tin filter-based spectral shaping CT on image quality and radiation dose for routine use on ultralow-dose CT protocols: A phantom study

PurposeThe purpose of this study was to assess the impact of tin filter (TF) on X-ray beam quality, image quality and radiation dose and its suitability for routine use for chest and lumbar-spine/pelvis-hip ultralow-dose (ULD) CT examination protocols.Materials and methodsThe X-ray beam quality was determined by measuring the half-value layer (HVL) and calculating the mean weighted energy for 100, 120, 150 kVp (using standard filtration) and for 100 and 150 kVp using TF (Sn100 kVp and Sn150 kVp, respectively). Acquisitions were performed on a phantom at four dose levels for each previously defined kVp. The mean attenuation (N_CT), noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. The detectability index (d’) was computed to model the detection of two lesions in spine and pelvic/hip examination and two for chest exploration. Image quality and detectability using a TF were assessed for two routinely used ULD protocols.ResultsThe HVL and mean weighted energy increased using a TF for the same tube voltage. Using a TF for the same tube voltage changed N_CT for bone and acrylic inserts, decreased the NPS peak without changing the NPS spatial frequency and increased the TTF values. The d’ values were improved using a TF and with the dose increase. d’ values of all modeled lesions were improved using Sn100 kVp and Sn150 kVp for the lumbar-spine/pelvis-hip and chest ULD protocols except for sclerotic bone lesion using Sn150 kVp.ConclusionThe use of TF increases the X-ray beam quality and improves the image quality characteristics in phantom images, thus appearing as a promising tool for reducing dose and/or improving the image quality of ULD protocols.     

Impact of ultra-low dose CT acquisition on semi-automated RECIST tool in the evaluation of malignant focal liver lesions

PurposeTo compare the evaluation of malignant focal liver lesions (FLLs) using a semi-automated RECIST tool with a standard and an ultra-low dose (ULD) computed tomography (CT) protocol.Materials and methodsThirty-four patients with malignant FLLs underwent two abdominal-pelvic CT examinations one using a standard protocol and one using an ULD protocol. There were 23 men and 11 women with a mean age 64.3 ± 14.4 (SD) years (range: 22–91 years). Dosimetric indicators were recorded, and effective dose was calculated for both examinations. Mean malignant FLL attenuation, image noise and contrast-to-noise-ratio (CNR) were compared. The largest malignant FLL per patient was evaluated using the semi-automated RECIST tool to determine longest axis length, longest orthogonal axis length, volume and World Health Organisation area.ResultsDosimetric values were significantly reduced by −56% with ULD compared to standard protocol. No differences in mean malignant FLL attenuation values were found between the two protocols. Image noise was significantly increased for all locations (P < 0.05) with ULD compared to standard protocol, and CNR was significantly reduced (P < 0.05). On the 34 malignant FLLs analyzed, six semi-automated shapes non-concordant with radiologist's visual impression were highlighted with the software, including one FLL (1/34; 3%) with standard CT acquisition only, three FLLs (3/34; 9%) with ULD CT acquisition only and two FLLs (2/34; 6%) with both CT acquisitions. After manual editing, the concordance of the values of the studied criteria between both acquisitions was good and no significant difference was reported.ConclusionSemi-automated RECIST tool demonstrates good performances using ULD CT protocol. It could be used in routine clinical practice with a ULD protocol for follow-up studies in patients with known malignant FLL.     

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning

PurposeThe purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN).Materials and methodsThe method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations.ResultsThe final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring.ConclusionThe deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.     

Automatic cervical lymphadenopathy segmentation from CT data using deep learning

PurposeThe purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination.Materials and methodsAn ensemble of three convolutional neural networks (CNNs) based on a U-Net architecture were trained to segment the lymphadenopathies in a fully supervised framework. The resulting predictions were assessed using the Dice similarity coefficient (DSC) on examinations presenting one or more adenopathies. On examinations without adenopathies, the score was given by the formula M/(M + A) where M was the mean adenopathy volume per patient and A the volume segmented by the algorithm. The networks were trained on 117 annotated CT acquisitions.ResultsThe test set included 150 additional CT acquisitions unseen during the training. The performance on the test set yielded a mean score of 0.63.ConclusionDespite limited available data and partial annotations, our CNN based approach achieved promising results in the task of cervical lymphadenopathy segmentation. It has the potential to bring precise quantification to the clinical workflow and to assist the clinician in the detection task.     

Single-source dual energy CT to assess myocardial extracellular volume fraction in aortic stenosis before transcatheter aortic valve implantation (TAVI)

PurposeTo assess myocardial extracellular volume fraction (ECV) measurement provided by a single-source dual-energy computed tomography (SSDE-CT) acquisition added at the end of a routine CT examination before transcatether aortic valve implantation (TAVI) compared to cardiac magnetic resonance imaging (MRI).Materials and methodsTwenty-one patients (10 men, 11 women; mean age, 86 ± 4.9 years [SD]; age range: 71–92 years) with severe aortic stenosis underwent standard pre-TAVI CT with additional cardiac SSDE-CT acquisition 7 minutes after intravenous administration of iodinated contrast material and myocardial MRI including pre- and post-contrast T1-maps. Myocardial ECV and standard deviation (σECV) were calculated in the 16-segments model. ECV provided by SSDE-CT was compared to ECV provided by MRI, which served as the reference. Analyses were performed on a per-segment basis and on a per-patient involving the mean value of the 16-segments.ResultsECV was slightly overestimated by SSDE-CT (29.9 ± 4.6 [SD] %; range: 20.9%–48.3%) compared to MRI (29.1 ± 3.9 [SD] %; range: 22.0%–50.7%) (P < 0.0001) with a bias and limits of agreement of +2.3% (95%CI: −16.1%– + 20.6%) and +2.5% (95%CI: −2.1%– + 7.1%) for per-segment and per-patient-analyses, respectively. Good (r = 0.81 for per-segment-analysis) to excellent (r = 0.97 for per-patient-analysis) linear relationships (both P < 0.0001) were obtained. The σECV was significantly higher at SSDE-CT (P < 0.0001). Additional radiation dose from CT was 1.89 ± 0.38 (SD) mSv (range: 1.48–2.47 mSv).ConclusionA single additional SSDE-CT acquisition added at the end of a standard pre-TAVI CT protocol can provide ECV measurement with good to excellent linear relationship with MRI.     

Focal splenic lesions: Imaging spectrum of diseases on CT,MRI and PET/CT

The spleen can be affected by a variety of diseases. Some of them are readily identified as variations of normal or benign diseases on imaging. However, for a substantial number of focal splenic abnormalities, the diagnosis can be difficult so that histopathologic analysis may be required for a definite diagnosis. In this review, the typical splenic abnormalities that can be diagnosed with imaging with a high degree of confidence are illustrated. The complementary role of computed tomography (CT), magnetic resonance imaging and positron emission tomography/CT that helps make a diagnostic approach is discussed. Finally, current applications and future trends of radiomics and artificial intelligence for the diagnosis of splenic diseases are addressed.     

CT evaluation of unrepaired/incidental congenital cardiovascular diseases in adults

Congenital heart disease (CHD) affects approximately one million people in the USA with the number increasing by 5% each year. Patients are usually both diagnosed and treated in infancy, however many of them may have subclinical CHD that remains undiagnosed until late adulthood. Patients with complex CHD tend to be symptomatic and are diagnosed at a younger age than those with a single defect. CHDs can be divided into three categories, including cardiac, great vessels and coronary artery anomalies. Recent advances in computed tomography (CT) technology with faster acquisition time and improved spatial resolution allow for detailed evaluation of cardiac morphology and function. The concomitant increased utilization of CT has simultaneously led to more sensitive detection and more thorough diagnosis of CHD. Recognition of and understanding the imaging attributes specific to each anomaly is important for radiologists in order to make a correct and definite diagnosis. This article reviews the spectrum of CHDs, which persist into adulthood that may be encountered by radiologists on CT.     

Diagnostic performance of ultra-low dose versus standard dose CT for non-traumatic abdominal emergencies

PurposeThe purpose of this study was to compare the diagnostic performance of ultra-low dose (ULD) to that of standard (STD) computed tomography (CT) for the diagnosis of non-traumatic abdominal emergencies using clinical follow-up as reference standard.Materials and methodsAll consecutive patients requiring emergency abdomen-pelvic CT examination from March 2017 to September 2017 were prospectively included. ULD and STD CTs were acquired after intravenous administration iodinated contrast medium (portal phase). CT acquisitions were performed at 125 mAs for STD and 55 mAs for ULD. Diagnostic performance was retrospectively evaluated on ULD and STD CTs using clinical follow-up as a reference diagnosis.ResultsA total of 308 CT examinations from 308 patients (145 men; mean age 59.1 ± 20.7 (SD) years; age range: 18–96 years) were included; among which 241/308 (78.2%) showed abnormal findings. The effective dose was significantly lower with the ULD protocol (1.55 ± 1.03 [SD] mSv) than with the STD (3.67 ± 2.56 [SD] mSv) (P < 0.001). Sensitivity was significantly lower for the ULD protocol (85.5% [95%CI: 80.4–89.4]) than for the STD (93.4% [95%CI: 89.4–95.9], P < 0.001) whereas specificities were similar (94.0% [95%CI: 85.1–98.0] vs. 95.5% [95%CI: 87.0–98.9], respectively). ULD sensitivity was equivalent to STD for bowel obstruction and colitis/diverticulitis (96.4% [95%CI: 87.0–99.6] and 86.5% [95%CI: 74.3–93.5] for ULD vs. 96.4% [95%CI: 87.0–99.6] and 88.5% [95%CI: 76.5–94.9] for STD, respectively) but lower for appendicitis, pyelonephritis, abscesses and renal colic (75.0% [95%CI: 57.6–86.9]; 77.3% [95%CI: 56.0–90.1]; 90.5% [95%CI: 69.6–98.4] and 85% [95%CI: 62.9–95.4] for ULD vs. 93.8% [95%CI: 78.6–99.2]; 95.5% [95%CI: 76.2–100.0]; 100.0% [95%CI: 81.4–100.0] and 100.0% [95%CI: 80.6–100.0] for STD, respectively). Sensitivities were significantly different between the two protocols only for appendicitis (P = 0.041).ConclusionIn an emergency context, for patients with non-traumatic abdominal emergencies, ULD-CT showed inferior diagnostic performance compared to STD-CT for most abdominal conditions except for bowel obstruction and colitis/diverticulitis detection.     

Head-to-head comparison of lung perfusion with dual-energy CT and SPECT-CT

PurposeTo 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).Materials and methodsA 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: 18–88 years). 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.ResultsStrong 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 (P < 0.0001) using a significantly lower equivalent dose of 4.1 ± 2.3 (SD) mSv (range: 1.9–11.85 mSv) compared to an equivalent dose of 5.3 ± 1.1 (SD) mSv (range: 2.8–7.3 mSv) for SPECT-CT, corresponding to a 21.2% dose reduction (P = 0.0004).ConclusionDECT imaging shows strong quantitative correlations and qualitative agreements with SPECT-CT for the evaluation of lung perfusion.     

CT evaluation of patent artery after percutaneous cryoablation of renal cell carcinoma

PurposeThe purpose of this retrospective study was to determine the incidence of persistent patent artery after percutaneous cryoablation of renal cell carcinoma (RCC) and the relationship between patent arteries one month after cryoablation and early tumor progression.Materials and methodsOne hundred and fifty-nine patients (112 men, 47 women; mean age, 63.6 ± 14.6 [SD] years; age range: 21–91 years) who underwent percutaneous cryoablation for 186 RCCs (mean diameter, 1.9 ± 0.6 [SD] cm; range: 0.7–4.0 cm) were retrospectively included. After cryoablation, patients underwent contrast-enhanced computed tomography (CT) with ≤ 2-mm slice thickness within one week from cryoablation, and at one, three, and six months. The time course of patent artery in the ablated renal parenchyma after cryoablation was the primary endpoint. The relationships between patent arteries one month after cryoablation and treatment effectiveness, tumor vascularity, tumor enhancement one month after cryoablation, tumor subtype, and renal function changes were evaluated as secondary endpoints.ResultsCT showed patent arteries in the ablated renal parenchyma within one week in 166 RCCs (89.2%), at one month in 54 RCCs (29.0%), at three months in 8 RCCs (4.3%), and at six months in 2 RCCs (1.1%). The presence of patent artery one month after cryoablation was significantly associated with tumor enhancement at the same time point (P = 0.015). There was no association between patent arteries one month after cryoablation and treatment effectiveness (P = 0.693).ConclusionPatent arteries in the ablated renal parenchyma are commonly observed on CT examination after percutaneous cryoablation of RCC. However, they gradually disappear and do not require specific treatment.     

Feasibility of lung imaging with a large field-of-view spectral photon-counting CT system

PurposeThe purpose of this study was to characterize the technical capabilities and feasibility of a large field-of-view clinical spectral photon-counting computed tomography (SPCCT) prototype for high-resolution (HR) lung imaging.Materials and methodsMeasurement of modulation transfer function (MTF) and acquisition of a line pairs phantom were performed. An anthropomorphic lung nodule phantom was scanned with standard (120 kVp, 62 mAs), low (120 kVp, 11 mAs), and ultra-low (80 kVp, 3 mAs) radiation doses. A human volunteer underwent standard (120 kVp, 63 mAs) and low (120 kVp, 11 mAs) dose scans after approval by the ethics committee. HR images were reconstructed with 1024 matrix, 300 mm field of view and 0.25 mm slice thickness using a filtered-back projection (FBP) and two levels of iterative reconstruction (iDose 5 and 9). The conspicuity and sharpness of various lung structures (distal airways, vessels, fissures and proximal bronchial wall), image noise, and overall image quality were independently analyzed by three radiologists and compared to a previous HR lung CT examination of the same volunteer performed with a conventional CT equipped with energy integrating detectors (120 kVp, 10 mAs, FBP).ResultsTen percent MTF was measured at 22.3 lp/cm with a cut-off at 31 lp/cm. Up to 28 lp/cm were depicted. While mixed and solid nodules were easily depicted on standard and low-dose phantom images, higher iDose levels and slice thicknesses (1 mm) were needed to visualize ground-glass components on ultra-low-dose images. Standard dose SPCCT images of in vivo lung structures were of greater conspicuity and sharpness, with greater overall image quality, and similar image noise (despite a flux reduction of 23%) to conventional CT images. Low-dose SPCCT images were of greater or similar conspicuity and sharpness, similar overall image quality, and lower but acceptable image noise (despite a flux reduction of 89%).ConclusionsA large field-of-view SPCCT prototype demonstrates HR technical capabilities and high image quality for high resolution lung CT in human.     

Diffusion-weighted MRI and PET/CT reproducibility in epithelial ovarian cancers during neoadjuvant chemotherapy

PurposeTo investigate the reproducibility of diffusion-weighted (DW) MRI and ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG)-Positron emission tomography/CT (PET/CT) in monitoring response to neoadjuvant chemotherapy in epithelial ovarian cancer.Materials and methodsTen women (median age, 67 years; range: 41.8–77.3 years) with stage IIIC-IV epithelial ovarian cancers were included in this prospective trial (NCT02792959) between 2014 and 2016. All underwent initial laparoscopic staging, four cycles of carboplatine-paclitaxel-based chemotherapy and interval debulking surgery. PET/CT and DW-MRI were performed at baseline (C0), after one cycle (C1) and before surgery (C4). Two nuclear physicians and two radiologists assessed five anatomic sites for the presence of ≥ 1 lesion. Target lesions in each site were defined and their apparent diffusion coefficient (ADC), maximal standardized uptake value (SUV-max), SUV-mean, SUL-peak, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were monitored (i.e., 10 patients × 5 sites × 3 time-points). Their relative early and late changes were calculated. Intra/inter-observer reproducibilities of qualitative and quantitative analysis were estimated with Kappa and intra-class correlation coefficients (ICCs).ResultsFor both modalities, inter- and intra-observer agreement percentages were excellent for initial staging but declined later for DW-MRI, leading to lower Kappa values for inter- and intra-observer variability (0.949 and 1 at C0, vs. 0.633 and 0.643 at C4, respectively) while Kappa values remained > 0.8 for PET/CT. Inter- and intra-observer ICCs were > 0.75 for SUV-max, SUL-peak, SUV-mean and their change regardless the time-point. ADC showed lower ICCs (range: 0.013–0.811). ANOVA found significant influences of the evaluation time, the measurement used (ADC, SUV-max, SUV-mean, SUV-max, SUL-peak, MTV or TLG) and their interaction on ICC values (P = 0.0023, P< 0.0001 and P =0.0028, respectively).ConclusionWhile both modalities demonstrated high reproducibility at baseline, only SUV-max, SUL-peak, SUV-mean and their changes maintained high reproducibility during chemotherapy.     

CT features of lung metastases from pancreatic adenocarcinoma: Correlation with histopathologic findings

PurposeThe purpose of this study was to evaluate the prevalence of an atypical, alveolar presentation of pulmonary metastases from pancreatic adenocarcinoma (PDAC) on computed tomography (CT) and to correlate CT features with those obtained at histopathologic analysis.Material and methodsA total of 76 patients with lung metastases from PDAC over a 10-year period (2009–2019) in a French university hospital were retrospectively included. There were 34 men and 42 women with a mean age of 67.6 ± 11.3 (SD) years (range: 38–89 years). CT features of PDAC were classified according to their presentations as usual metastatic pattern or atypical alveolar pattern; the atypical alveolar pattern corresponding to either ground glass nodules or opacities, solid nodules with a halo sign, “air-space” nodules with air bronchogram, or parenchymal consolidation. Imaging-histopathologic correlation was performed when tissue samples were available.ResultsPulmonary metastases were synchronous in 36 patients (36/76; 47%) and metachronous in 40 patients (40/76; 53%). A predominant alveolar presentation on CT was observed in 17 patients (17/76, 22%). Nodules with halo sign were the predominant alveolar pattern in 7 patients (7/17; 41%), air-space nodules were predominant in 4 patients (4/17; 24%) whereas pure ground glass nodules and consolidations were observed as predominant features in 3 patients (3/17; 18%) each. For 5 patients who had histopathological confirmation, alveolar metastases of PDAC were characterized by columnar tumor cells lining the alveolar wall, which was not seen in other radiological presentations, whereas there were no differences regarding mucin secretion between pulmonary metastases with alveolar presentation and those with typical pattern.ConclusionsLung metastases from PDAC may present with a so-called “alveolar” pattern on CT. This misleading CT features is found in 22% of patients with lung metastases from PDAC and is due to lepidic growth of the metastatic cells.     

Artificial intelligence solution to classify pulmonary nodules on CT

PurposeThe purpose of this study was to create an algorithm to detect and classify pulmonary nodules in two categories based on their volume greater than 100 mm³ or not, using machine learning and deep learning techniques.Materials and methodThe dataset used to train the model was provided by the organization team of the SFR (French Radiological Society) Data Challenge 2019. An asynchronous and parallel 3-stages pipeline was developed to process all the data (a data “pre-processing” stage; a “nodule detection” stage; a “classifier” stage). Lung segmentation was achieved using 3D U-NET algorithm; nodule detection was done using 3D Retina-UNET and classifier stage with a support vector machine algorithm on selected features. Performances were assessed using area under receiver operating characteristics curve (AUROC).ResultsThe pipeline showed good performance for pathological nodule detection and patient diagnosis. With the preparation dataset, an AUROC of 0.9058 (95% confidence interval [CI]: 0.8746–0.9362) was obtained, 87% yielding accuracy (95% CI: 84.83%–91.03%) for the “nodule detection” stage, corresponding to 86% specificity (95% CI: 82%–92%) and 89% sensitivity (95% CI: 84.83%–91.03%).ConclusionA fully functional pipeline using 3D U-NET, 3D Retina-UNET and classifier stage with a support vector machine algorithm was developed, resulting in high capabilities for pulmonary nodule classification.     

3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past,present and future

Three-dimensional (3D) imaging and post processing are common tasks used daily in many disciplines. The purpose of this article is to review the new postprocessing tools available. Although 3D imaging can be applied to all anatomical regions and used with all imaging techniques, its most varied and relevant applications are found with computed tomography (CT) data in musculoskeletal imaging. These new applications include global illumination rendering (GIR), unfolded rib reformations, subtracted CT angiography for bone analysis, dynamic studies, temporal subtraction and image fusion. In all of these tasks, registration and segmentation are two basic processes that affect the quality of the results. GIR simulates the complete interaction of photons with the scanned object, providing photorealistic volume rendering. Reformations to unfold the rib cage allow more accurate and faster diagnosis of rib lesions. Dynamic CT can be applied to cinematic joint evaluations a well as to perfusion and angiographic studies. Finally, more traditional techniques, such as minimum intensity projection, might find new applications for bone evaluation with the advent of ultra-high-resolution CT scanners. These tools can be used synergistically to provide morphologic, topographic and functional information and increase the versatility of CT.     

Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment

PurposeThe purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vertebra.Materials and methodsAn ensemble of 15 deep learning models with a two-dimensional U-net architecture with a 4-level depth and 18 initial filters were trained to segment MBM. The muscular surface values were computed from the predicted masks and corrected with the algorithm's estimated bias. Resulting mask prediction and surface prediction were assessed using Dice similarity coefficient (DSC) and root mean squared error (RMSE) scores respectively using ground truth masks as standards of reference.ResultsA total of 1025 individual CT slices were used for training and validation and 500 additional axial CT slices were used for testing. The obtained mean DSC and RMSE on the test set were 0.97 and 3.7 cm² respectively.ConclusionDeep learning methods using convolutional neural networks algorithm enable a robust and automated extraction of CT derived MBM for sarcopenia assessment, which could be implemented in a clinical workflow.     

COVID-19: A qualitative chest CT model to identify severe form of the disease

PurposeThe purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form.Materials and methodsA total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68 ± 14 (SD) years (range: 24–96 years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method.ResultsCentral involvement of lung parenchyma (P < 0.001), area of consolidation (P < 0.008), air bronchogram sign (P < 0.001), bronchiectasis (P < 0.001), traction bronchiectasis (P < 0.011), pleural effusion (P < 0.026), large involvement of either one of the upper lobes or of the middle lobe (P < 0.001) and total CT score ≥ 15 (P < 0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR) = 2.473], central involvement [OR = 2.760], pleural effusion [OR = 2.699]) and the semi-quantitative model (total CT score ≥ 15 [OR = 3.342], central involvement [OR = 2.344], pleural effusion [OR = 2.754]) with AUC of 0.722 (95% CI: 0.638–0.806) vs. 0.739 (95% CI: 0.656–0.823), respectively (P = 0.209).ConclusionWe have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.     

Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment

PurposeTo assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform.Materials and methodsAcquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDI_vol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d′) was computed to model the detection task of two enhanced iodine lesions as function of keV.ResultsNoise-magnitude was significantly reduced between 40 to 70 keV by ?56 ± 0% (SD) (range: ?56%–?55%) with FBP; ?56 ± 0% (SD) (?56%–?56%) with A3; and ?57 ± 1% (SD) (range: ?57%–?56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2 mg/mL, d’ values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d’ peaked at 60 keV. For the other simulated lesion, d’ values were highest at 40 keV and decreased beyond.ConclusionAt low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.     

PET/CT in the management of differentiated thyroid cancer

The standard treatment of differentiated thyroid cancer (DTC) consists of surgery followed by iodine-131 (¹³¹I) administration. Although the majority of DTC has a very good prognosis, more aggressive histologic subtypes convey a worse prognosis. Follow-up consists of periodically measurements of serum thyroglobulin, thyroglobulin antibodies and neck ultrasound and ¹²³I/¹³¹I whole-body scan. However, undifferentiated thyroid tumors have a lower avidity for radioiodine and the ability of DTC to concentrate ¹³¹I may be lost in metastatic disease. Positron emission tomography (PET)/computed tomography (CT) has been introduced in the evaluation of patients with thyroid tumors and the 2-[18F]-fluoro-2-deoxyd-glucose (¹⁸F-FDG) has been largely validated as marker of cell's metabolism. According to the 2015 American Thyroid Association guidelines, ¹⁸F-FDG PET/CT is recommended in the follow-up of high-risk patients with elevated serum thyroglobulin and negative ¹³¹I imaging, in the assessment of metastatic patients, for lesion detection and risk stratification and in predicting the response to therapy. It should be considered that well-differentiated iodine avid lesions could not concentrate ¹⁸F-FDG, and a reciprocal pattern of iodine and ¹⁸F-FDG uptake has been observed. Beyond ¹⁸F-FDG, other tracers are available for PET imaging of thyroid tumors, such as Iodine-124 (¹²⁴I), ¹⁸F-tetrafluoroborate and Gallium-68 prostate-specific membrane antigen. Moreover, the recent introduction of PET/MRI, offers now several opportunities in the field of patients with DTC. This review summarizes the evidences on the role of PET/CT in management of patients with DTC, focusing on potential applications and on elucidating some still debating points.