Rationale and objectivesA new microcatheter was recently developed claiming to reduce beads reflux in drug-eluting bead transarterial chemoembolization (DEB-TACE). The aim of this study was to compare the reflux control microcatheter ability versus a standard microcatheter for TACE treatment in patients with hepatocellular carcinoma. Material and methodsPatients were prospectively included between November 2017 and February 2022. They received a DEB-TACE treatment with charged radiopaque beads using standard microcatheters or the SeQure reflux control microcatheter (Guerbet, France) and were assigned respectively to a control and a test group. Beads distribution mismatch was evaluated between the targeted territory on treatment planning CBCT and beads’ spontaneous opacities on the post-intervention CBCT and the 1-month CT scanner. ResultsTwenty-three patients (21 men, median age 64 years [12.5 years]) with 37 hepatocellular carcinoma nodules were treated. The control group consisted of 13 patients – 19 nodules, while the test group included ten patients - 18 nodules. Non target embolization (NTE) was found in 20 % (2/10) of patients in the test group and 85 % (11/13) in the control group. NTE involved only an adjacent segment in the test group while it affected the adjacent biliary sector or even the contralateral liver lobe in the control group. No complication linked to NTE was found in the test group, while it led to one case of ischemic cholangitis and another case of biloma in the control group. ConclusionThe reflux control microcatheter may be efficient in reducing NTE and thus eventual adverse events in comparison to standard of care end-hole microcatheters.

BackgroundAbdominal radiographs remain useful in newborns. Given the high radiation sensitivity of this population, it is necessary to optimize acquisition techniques to minimize radiation exposure. ObjectiveEvaluate the effects of three additional filtrations on radiation dose and image quality in abdominal X-rays of newborns using an anthropomorphic phantom. Material and methodAbdominal radiographs of an anthropomorphic newborn phantom were performed using acquisition parameters ranging from 55 to 70 kV and from 0.4 to 2.5 mAs, without and with three different additional filtrations: 0.1 mm copper (Cu) + 1 mm aluminum (Al), 0.2 mm copper + 1 mm aluminum, and 2 mm aluminum. For each X-ray the dose area product (DAP) was measured, the signal-to-noise ratio (SNR) was calculated, and image quality (IQ) was evaluated by two blinded radiologists using the absolute visual grading analysis (VGA) method. ResultsAdding an additional filtration resulted in a significant reduction in DAP, with a decrease of 42% using 2 mm Al filtration, 65% with 0.1 mm Cu + 1 mm Al filtration, and 78% with 0.2 mm Cu + 1 mm Al filtration (p < 0.01). The addition of 2 mm aluminum filtration does not significantly decrease the SNR (p = 0.31), CNR (p = 0.52) or the IQ (p = 0.12 and 0.401 for reader 1 and 2, respectively). However, adding copper-containing filtration leads to a significant decrease in, SNR, CNR and IQ. ConclusionAdding a 2 mm Al additional filtration for abdominal radiographs in newborns can significantly reduce the radiation dose without causing a significant decrease in image quality.

ObjectivesNon-invasive subtyping of hepatocellular adenomas (HCA) remains challenging for several subtypes, thus carrying different levels of risks and management. The goal of this study is to devise a multivariable diagnostic model based on basic clinical features (age and sex) combined with MRI-radiomics and to evaluate its diagnostic performance. MethodsThis single-center retrospective case-control study included all consecutive patients with HCA identified within the pathological database from our institution from January 2003 to April 2018 with MRI examination (T2, T1-no injection/injection-arterial-portal); volumes of interest were manually delineated in adenomas and 38 textural features were extracted (LIFEx, v5.10). Qualitative (i.e., visual on MRI) and automatic (computer-assisted) analysis were compared. The prognostic scores of a multivariable diagnostic model based on basic clinical features (age and sex) combined with MRI-radiomics (tumor volume and texture features) were assessed using a cross-validated Random Forest algorithm. ResultsVia visual MR-analysis, HCA subgroups could be classified with balanced accuracies of 80.8 % (I-HCA or ß-I-HCA, the two being indistinguishable), 81.8 % (H-HCA) and 74.4 % (sh-HCA or ß-HCA also indistinguishable). Using a model including age, sex, volume and texture variables, HCA subgroups were predicted (multivariate classification) with an averaged balanced accuracy of 58.6 %, best=73.8 % (sh-HCA) and 71.9 % (ß-HCA). I-HCA and ß-I-HCA could be also distinguished (binary classification) with a balanced accuracy of 73 %. ConclusionMultiple HCA subtyping could be improved using machine-learning algorithms including two clinical features, i.e., age and sex, combined with MRI-radiomics. Future HCA studies enrolling more patients will further test the validity of the model.

BackgroundDual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs. MethodsDECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train (N = 28) and held-out test (N = 30) sets. To perform cross-validation, the train set was split into seven folds. The images were presented to two musculoskeletal radiologists, who independently identified green-encoded voxels. Two 3D Unet-based DL models, Segresnet and SwinUNETR, were trained, and the Dice similarity coefficient (DSC), sensitivity, and specificity were reported as the segmentation metrics. ResultsSegresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively. ConclusionIn this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout.

PurposeThe aim of this study was to report the safety and tumor response rate of combined transarterial radioembolization (TARE) through the intrahepatic arteries and transarterial chemoembolization (TACE) through the extrahepatic feeding arteries (EHFA) in patients with hepatocellular carcinoma (HCC). MethodsPatients with HCC, who had both intrahepatic and extrahepatic arterial supply visible on preinterventional multiphase CT and were treated between 2016 and 2021 with a combination of TACE and TARE on the same nodule, were retrospectively included. Epidemiological, clinical, biological, and radiological characteristics were recorded. Safety and tumor response were assessed at 6 months. ResultsNine patients (8 men, median age 62 years [IQR: 54–72 years]) were included. Seven patients had previous treatments on the target nodule (TARE: 5; TACE: 2). The median longest axis (LA) of the lesion was 70 mm (IQR: 60–79 mm). Three patients had portal vein invasion (VP3). The EHFA originated from the right diaphragmatic artery (n = 6), the right adrenal artery (n = 2), and the left gastric artery (n = 1). The LA of the tumor portion treated with TACE was 47 mm (range: 35–64 mm). The ratio between the LA of the entire lesion and the LA treated with TACE was 1.44 (range: 1.27–1.7). One major complication occurred: acute on chronic liver failure. Median follow-up was 23 months (range: 16–29 months). Seven patients underwent further treatment: on the same lesion (n = 2), on newly appeared nodules (n = 2), and systemic treatment (n = 3). At 6-month follow-up, seven patients showed a local objective response. Time-to-progression was 13 (3.5–19) months. ConclusionThe combination of TARE and extrahepatic TACE for HCC with both intrahepatic and extrahepatic arterial supplies seems feasible and safe. Further studies are needed to validate the effectiveness of these preliminary results.

ObjectiveThe objective of this study was to evaluate the clinical feasibility of deep learning reconstruction-accelerated thin-slice single-breath-hold half-Fourier single-shot turbo spin echo imaging (HASTEDL) for detecting pancreatic lesions, in comparison with two conventional T2-weighted imaging sequences: compressed-sensing HASTE (HASTECS) and BLADE. MethodsFrom March 2022 to January 2023, a total of 63 patients with suspected pancreatic-related disease underwent the HASTEDL, HASTECS, and BLADE sequences were enrolled in this retrospectively study. The acquisition time, the pancreatic lesion conspicuity (LCP), respiratory motion artifact (RMA), main pancreatic duct conspicuity (MPDC), overall image quality (OIQ), signal-to-noise ratio (SNR), and contrast-noise-ratio (CNR) of the pancreatic lesions were compared among the three sequences by two readers. ResultsThe acquisition time of both HASTEDL and HASTECS was 16 s, which was significantly shorter than that of 102 s for BLADE. In terms of qualitative parameters, Reader 1 and Reader 2 assigned significantly higher scores to the LCP, RMA, MPDC, and OIQ for HASTEDL compared to HASTECS and BLADE sequences; As for the quantitative parameters, the SNR values of the pancreatic head, body, tail, and lesions, the CNR of the pancreatic lesion measured by the two readers were also significantly higher for HASTEDL than for HASTECS and BLADE sequences. ConclusionsCompared to conventional T2WI sequences (HASTECS and BLADE), deep-learning reconstructed HASTE enables thin slice and single-breath-hold acquisition with clinical acceptable image quality for detection of pancreatic lesions.

Rationale and objectivesTo improve the image quality of CT pulmonary angiography (CTPA) using a patient-specific timing protocol. Material and methodsA total of 24 swine (48.5 ± 14.3 kg) underwent continuous contrast-enhanced dynamic CT acquisition over 30 s to capture the pulmonary arterial input function (AIF). Multiple contrast injections were made under different cardiac outputs (1.4–5.1 L/min), resulting in a total of 154 AIF curves. The volume scans with maximal enhancement in these AIF curves were retrospectively selected as the reference standard (group A). Two prospective CTPA protocols with bolus-tracking were then simulated using these AIF curves: one used a fixed delay of 5 s between triggering and CTPA acquisition (group B), while the other used a specific delay based on one-half of the contrast injection duration (group C). The mean attenuation, signal-to-noise (SNR) and contrast-to-noise ratios (CNR) between the three groups were then compared using independent sample t-test. Subjective image quality scores were also compared using Wilcoxon-Mann-Whitney test. ResultsThe mean attenuation of pulmonary arteries for group A, B and C (expressed in [HU]) were 870.1 ± 242.5 HU, 761.1 ± 246.7 HU and 825.2 ± 236.8 HU, respectively. The differences in the mean SNR and CNR between Group A and Group C were not significant (SNR: 65.2 vs. 62.4, CNR: 59.6 vs. 56.4, both p > 0.05), while Group B was significantly lower than Group A (p < 0.05). ConclusionThe image quality of CT pulmonary angiography is significantly improved with a timing protocol determined using contrast injection delivery time, as compared with a standard timing protocol with a fixed delay between bolus triggering and image acquisition.

PurposeTo determine the impact of an artificial intelligence software predicting malignancy in the management of incidentally discovered lung nodules. Materials and methodsIn this retrospective study, all lung nodules ≥ 6 mm and ≤ 30 mm incidentally discovered on emergency CT scans performed between June 1, 2017 and December 31, 2017 were assessed. Artificial intelligence software using deep learning algorithms was applied to determine their likelihood of malignancy: most likely benign (AI score < 50%), undetermined (AI score 50–75%) or probably malignant (AI score > 75%). Predictions were compared to two-year follow-up and Brock's model. ResultsNinety incidental pulmonary nodules in 83 patients were retrospectively included. 36 nodules were benign, 13 were malignant and 41 remained indeterminate at 2 years follow-up.AI analysis was possible for 81/90 nodules. The 34 benign nodules had an AI score between 0.02% and 96.73% (mean = 48.05 ± 37.32), while the 11 malignant nodules had an AI score between 82.89% and 100% (mean = 93.9 ± 2.3). The diagnostic performance of the AI software for positive diagnosis of malignant nodules using a 75% malignancy threshold was: sensitivity = 100% [95% CI 72%-100%]; specificity = 55.8% [38–73]; PPV = 42.3% [23–63]; NPV = 100% [82–100]. With its apparent high NPV, the addition of an AI score to the initial CT could have avoided a guidelines-recommended follow-up in 50% of the benign pulmonary nodules (6/12 nodules). ConclusionArtificial intelligence software using deep learning algorithms presents a strong NPV (100%, with a 95% CI 82–100), suggesting potential use for reducing the need for follow-up of nodules categorized as benign.

Rationale and objectivesTo support cardiac MR acquisitions during breathing without ECG, we developed software to mitigate the effects of respiratory displacement of the heart. The algorithm resolves respiratory motions and cardiac cycles from DICOM files. The new software automatically detects heartbeats from expiration and inspiration to decrease apparent respiratory motion. Materials and methodsOur software uses principal component analysis to resolve respiratory motions from cardiac cycles. It groups heartbeats from expiration and inspiration to decrease apparent respiratory motion. The respiratory motion correction was evaluated on short-axis views (acquired with compressed sensing) of 11 healthy subjects and 8 cardiac patients. Two expert radiologists, blinded to the processing, assessed the dynamic images in terms of blood-myocardial contrast, endocardial interface definition, and motion artifacts. ResultsThe smallest correlation coefficients between end-systolic frames of the original dynamic scans averaged 0.79. After segregation of cardiac cycles by respiratory phase, the mean correlation coefficients between cardiac cycles were 0.94±0.03 at end-expiration and 0.90±0.08 at end-inspiration. The improvements in correlation coefficients were significant in paired t-tests for healthy subjects and heart patients at end-expiration. Clinical assessment preferred cardiac cycles during end-expiration, which maintained or enhanced scores in 90% of healthy subjects and 83% of the heart patients. Performance remained high with arrhythmia and irregular breathing present. ConclusionHeartbeats collected from end-expiration mitigate respiratory motion and are accessible by applying the new software to DICOM files from real-time CMR. Inspiratory heartbeats are also accessible for examination of arrhythmias or abnormalities at end-inspiration.

PurposeTo compare tomosynthesis performance to radiography for the differentiation of sacroiliitis versus normal or degenerative changes in sacroiliac joints in patients with suspected axial spondyloarthritis (SpA). Materials and methodsRadiography, tomosynthesis and CT of sacroiliac joints (29 patients) were performed on the same day in consecutive patients with suspected SpA. The examinations were retrospectively read independently, blinded by two radiologists (one junior and one senior, and twice by one junior). Interobserver and intraobserver agreement was evaluated using the kappa coefficient. Effective doses for each imaging sensitivity, specificity and accuracy were assessed and compared with CT as gold standard. ResultsCT detected 15/58 joints with sacroiliitis. The imaging sensitivity, specificity and accuracy were 60%, 84% and 44%, respectively, for radiography and 87%, 91% and 77% for tomosynthesis. The mean effective dose for tomosynthesis was significantly lower than that of CT (5-fold less) and significantly higher than that of radiography (8-fold more). ConclusionTomosynthesis is superior to radiography for sacroiliitis detection in patients with suspected SpA, with 5-fold less radiation exposure than CT.