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.     

Discriminating between bronchiolar adenoma,adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung with CT

PurposeTo identify computed tomography (CT) features that may help distinguish bronchiolar adenoma (BA) from lung adenocarcinomas in situ (AIS) and minimally invasive adenocarcinomas (MIA) among lung lesions presenting as ground-glass nodules (GGNs).Materials and methodsA total of 140 patients with GGNs confirmed by surgery and pathology, were reviewed retrospectively. There were 68 men and 72 women with a mean age of 64.3 ± 8.9 (SD) years (range: 31 – 85 years). The CT features of BA, AIS, and MIA were analyzed and compared. CT features, including percentage of solid component, maximum diameter of solid component, lesion density, location, margin, shape, pseudo-cavitation, calcification, ill-defined peripheral opacity, and air bronchogram, were analyzed using multivariate logistic regression and receiver operating characteristic curves.ResultsThere were 11/140 (7.9%) patients with BA (mean age, 67.7 ± 7.5 [SD]; range 45 – 77 years), 63/140 (45.0%) patients with AIS (mean age, 62.5 ± 8.6 [SD]; range 36 – 69 years) and 66/140 (47.1%) patients with MIA (mean age, 63.5 ± 7.9 [SD]; range 35 – 72 years). By comparison with AIS and MIA, significantly different CT features of BA included tumor size, solid component diameters, low CT attenuation of the ground-glass component, irregular shape, ill-defined peripheral opacity, pseudo-cavitation, and abnormal pulmonary vein. Ill-defined peripheral opacity (odds ratio, 1.060; 95% confidence interval [CI]: 1.020 – 1.380) and pseudo-cavitation (odds ratio, 1.236; 95% CI: 1.070 – 1.565) were variables independently associated with the diagnosis of BA.ConclusionCT provides morphological features that allow differentiating between BA and AIS-MIA among lung lesions presenting as GGNs.     

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

Differentiating between active and latent tuberculosis with chest computed tomography

PurposeThe purpose of this study was to evaluate the capabilities of chest computed tomography (CT) in distinguishing between active and latent tuberculosis in patients positive for interferon-gamma release assay (IGRA) testing, and to compare the performance of CT with that of quantitative IGRA testing in a low incidence setting.Materials and methodsPatients with latent or active tuberculosis define by an IGRA positive test were retrospectively recruited. Sensitivity, specificity and accuracy were determined for CT variables and quantitative IGRA results. Final diagnosis of active tuberculosis was based on clinical data and microbiological culture. Univariable and multivariable analyses were performed using logistic regression model to identify CT variables associated with the diagnosis of active tuberculosis.ResultsA total of 92 patients with positive IGRA results who underwent CT examination were included. There were 54 men and 38 women with a mean age of 53.5 ± 18 (SD) years (range: 40–68 years). Of them, 22 patients (24%) had positive Mycobacterium tuberculosis culture and 70 (76%) had latent tuberculosis. Among CT variables, consolidation had the greatest sensitivity (77%; 95%CI: 60–95%) and “tree-in-bud” the greatest specificity (97%; 95% CI: 93–100%) for the diagnosis of active tuberculosis. At univariable analysis “tree-in-bud”, splenic calcification and non-calcified lung nodules were the significant variables independently associated with active tuberculosis. At multivariable analysis, the adjusted odds ratio of “tree-in-bud” was 42.91 (95% CI: 5.62–327.42). Using an optimal threshold of 51 spots, quantitative IGRA yielded 64% sensitivity (95% CI: 44–84%) and 61% specificity (95% CI: 50–73%) for the diagnosis of active tuberculosis.ConclusionsIn a low incidence setting, chest CT, especially when “tree-in-bud” pattern is present, is superior to quantitative IGRA testing to identify patients with active tuberculosis among those with positive IGRA testing.     

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.     

MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma

PurposeTo determine the capabilities of MRI-based traditional radiomics and computer-vision (CV) nomogram for predicting lymphovascular space invasion (LVSI) in patients with endometrial carcinoma (EC).Materials and methodsA total of 184 women (mean age, 52.9 ± 9.0 [SD] years; range, 28–82 years) with EC were retrospectively included. Traditional radiomics features and CV features were extracted from preoperative T2-weighted and dynamic contrast-enhanced MR images. Two models (Model 1, the radiomics model; Model 2, adding CV radiomics signature into the Model 1) were built. The performance of the models was evaluated by the area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohorts. A nomogram based on clinicopathological metrics and radiomics signatures was developed. The predictive performance of the nomogram was assessed by AUC of the ROC in the training and test cohorts.ResultsFor predicting LVSI, the AUC values of Model 1 in the training and test cohorts were 0.79 (95% confidence interval [CI]: 0.702–0.889; accuracy: 65.9%; sensitivity: 88.8%; specificity: 57.8%) and 0.75 (95% CI: 0.585–0.914; accuracy: 69.5%; sensitivity: 85.7%; specificity: 62.5%), respectively. The AUC values of Model 2 in the training and test cohorts were 0.93 (95% CI: 0.875–0.991; accuracy: 94.9%; sensitivity: 91.6%; specificity: 96.0%) and 0.81 (95% CI: 0.666–0.962; accuracy: 71.7%; sensitivity: 92.8%; specificity: 62.5%), respectively. The discriminative ability of Model 2 was significantly improved compared to Model 1 (Net Reclassification Improvement [NRI] = 0.21; P = 0.04). Based on histologic grade, FIGO stage, Rad-score and CV-score, AUC values of the nomogram to predict LVSI in the training and test cohorts were 0.98 (95% CI: 0.955–1; accuracy: 91.6%; sensitivity: 91.6%; specificity: 96.0%) and 0.92 (95% CI: 0.823–1; accuracy: 91.3%; sensitivity: 78.5%; specificity: 96.8%), respectively.ConclusionsMRI-based traditional radiomics and computer-vision nomogram are useful for preoperative risk stratification in patients with EC and may facilitate better clinical decision-making.     

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.     

CT and MRI features of sclerosing angiomatoid nodular transformation of the spleen: A report of 18 patients with pathologic correlation

PurposeThe purpose of this study was to describe the computed tomography (CT) and magnetic resonance imaging (MRI) features of sclerosing angiomatoid nodular transformation (SANT) of the spleen and correlate imaging features with those obtained at histopathologic analysis.Materials and methodsA total of 18 patients (9 men, 9 women; mean age, 42.2 ± 10.7 [standard deviation (SD)] years; range, 23–59 years) with histopathologically confirmed SANT were retrospectively evaluated. The presenting symptoms, gross pathologic changes, and histopathologic and correlative immunohistochemical results were recorded. CT (n = 8) and MRI (n = 12) features were analyzed by two radiologists and included number, size, shape, boundary, attenuation, signal intensity, and enhancement patterns.ResultsSeventeen patients (17/18; 94%) had a single SANT without specific clinical symptoms and one patient (1/18; 6%) had multiple SANTs with left-upper-quadrant bloating and pain. The largest lesion diameter exceeded 3 cm. On plain CT images, SANTs were slightly hypoattenuating in seven patients (7/8; 88%), isoattenuating in one patient (1/8; 13%), and contained calcification in two patients (2/8; 25%). On T2-weighted MR images, SANTs displayed hypointensity in ten patients (10/12; 83.3%), isointensity in one patient (1/12; 8%) and hyperintensity in one patient (1/12; 8%). On T2-weighted images, stellate or scattered fibrous scars were observed in all patients (12/12; 100%). On diffusion-weighted images, SANTs appeared as heterogenous or homogeneous hypointense in 12 patients (12/12; 100%). Compared to out-of-phase images, SANTs displayed decreased local signal intensity on in-phase images in 12 patients (12/12; 100%). On enhanced CT and MRI images, SANTs had clear boundaries (17/18; 94%), oval (7/18; 39%) or lobular (7/18; 39%) shape, displayed progressive centripetal enhancement (18/18; 100%), spoke-wheel pattern (14/18; 78%), nodular enhancement (11/18; 61%), or delayed enhancement of central fibrous scar (9/18; 50%).ConclusionsSANT of the spleen predominantly manifests as a solid, single, oval or lobular, and well-defined lesion with a fibrous scar and occasional calcification. Typical enhancement characteristics include progressive and centripetal enhancement, spoke-wheel pattern, nodular enhancement, and delayed enhancement of central fibrous scar. Hypointensity on T2- and diffusion-weighted images are due to hemosiderin deposition and fibrous tissue.     

MRI-based whole-tumor radiomics to classify the types of pediatric posterior fossa brain tumor

BackgroundDifferential diagnosis between medulloblastoma (MB), ependymoma (EP) and astrocytoma (PA) is important due to differing medical treatment strategies and predicted survival. The aim of this study was to investigate non-invasive MRI-based radiomic analysis of whole tumors to classify the histologic tumor types of pediatric posterior fossa brain tumor and improve the accuracy of discrimination, using a random forest classifier.MethodsMRI images of 99 patients, with 59 MBs, 13 EPs and 27 PAs histologically confirmed by surgery and pathology before treatment, were included in this retrospective study. Registration was performed between the three sequences, and high- throughput features were extracted from manually segmented tumors on MR images of each case. The forest-based feature selection method was adopted to select the top ten significant features. Finally, the results were compared and analyzed according to the classification.ResultsThe top ten contributions according to the classifier of wavelet features all came from the ADC sequence. The random forest classifier achieved 100% accuracy on the training data and validated the best accuracy (0.938): sensitivity = 1.000, 0.948 and 0.808, specificity = 0.952, 0.926 and 1.000 for EP, MB and PA, respectively.ConclusionA random forest classifier based on the ADC sequence of the whole tumor provides more quantitative information than TIWI and T2WI in differentiating pediatric posterior fossa brain tumors. In particular, the histogram percentile value showed great superiority, which added diagnostic value in pediatric neuro-oncology.     

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.     

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.     

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.     

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.     

Prediction of tumor grade and lymphovascular space invasion in endometrial adenocarcinoma with MR imaging-based radiomic analysis

PurposeTo evaluate the capabilities of two-dimensional magnetic resonance imaging (MRI)-based texture analysis features, tumor volume, tumor short axis and apparent diffusion coefficient (ADC) in predicting histopathological high-grade and lymphovascular space invasion (LVSI) in endometrial adenocarcinoma.Materials and methodsSeventy-three women (mean age: 66 ± 11.5 [SD] years; range: 45–88 years) with endometrial adenocarcinoma who underwent MRI of the pelvis at 1.5-T before hysterectomy were retrospectively included. Texture analysis was performed using TexRAD® software on T2-weighted images and ADC maps. Primary outcomes were high-grade and LVSI prediction using histopathological analysis as standard of reference. After data reduction using ascending hierarchical classification analysis, a predictive model was obtained by stepwise multivariate logistic regression and performances were assessed using cross-validated receiver operator curve (ROC).ResultsA total of 72 texture features per tumor were computed. Texture model yielded 52% sensitivity and 75% specificity for the diagnosis of high-grade tumor (areas under ROC curve [AUC] = 0.64) and 71% sensitivity and 59% specificity for the diagnosis of LVSI (AUC = 0.59). Volumes and tumor short axis were greater for high-grade tumors (P = 0.0002 and P = 0.004, respectively) and for patients with LVSI (P = 0.004 and P = 0.0279, respectively). No differences in ADC values were found between high-grade and low-grade tumors and for LVSI. A tumor short axis ≥ 20 mm yielded 95% sensitivity and 75% specificity for the diagnosis of high-grade tumor (AUC = 0.86).ConclusionMRI-based texture analysis is of limited value to predict high grade and LVSI of endometrial adenocarcinoma. A tumor short axis ≥ 20 mm is the best predictor of high grade and LVSI.     

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.     

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 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.     

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.     

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.