Objective To analyse the prevalence, incidence and clinical relevance of pancreatic cysts detected as incidental finding in a population-based longitudinal study. Design A total of 1077 participants (521 men, mean age 55.8±12.8 years) of 2333 participants from the population-based Study of Health in Pomerania (SHIP) underwent magnetic resonance cholangiopancreaticography (MRCP) at baseline (2008–2012). MRCP was analysed for pancreatic cysts with a diameter ≥2 mm. 676/1077 subjects received a 5-year follow-up (2014–2016). The prevalence and incidence of pancreatic cysts (weighted for study participation) were assessed in association to age, gender and suspected epidemiological risk factors. Mortality follow-up was performed in 2015 for all SHIP participants (mean follow-up period 5.9 years, range 3.2–7.5 years). Results At baseline pancreatic cysts had a weighted prevalence of 49.1%, with an average number of 3.9 (95% CI 3.2 to 4.5) cysts per subject in the subgroup harbouring cysts. Cyst size ranged from 2 to 29 mm. Prevalence (p<0.001), number (p=0.001) and maximum size (p<0.001) increased significantly with age. The 5-year follow-up revealed a weighted incidence of 12.9% newly detected pancreatic cysts. 57.1% of the subjects initially harbouring pancreatic cysts showed an increase in number and/or maximum cyst size. Of all subjects undergoing MRCP, no participant died of pancreatic diseases within mortality follow-up. Conclusion The prevalence of pancreatic cysts in the general population is unexpectedly high, and their number and size increase with age. Overall, no pancreatic cancer was observed in this collective during a 5-year follow-up. Nevertheless, prospective follow-up imaging showed minimal progress in more than 50%. Only about 6% of cysts and 2.5% of the study group initially presented with cysts of more than 1 cm and thus might be clinically meaningful.

Diffusion-weighted MRI−based virtual elastography shows potential to be an alternative to MR elastography for the noninvasive evaluation of liver fibrosis without the need for any mechanical vibration. Background Diffusion-weighted (DW) MRI–based elastography has recently been proposed for noninvasive liver fibrosis staging but requires evaluation in a larger number of patients. Purpose To compar...

Objectives: To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods: This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results: Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions: Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points: • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.

Pneumothoraces are the most frequently occurring complications of CT-guided percutaneous transthoracic pulmonary biopsies (PTPB). The aim of this study was to evaluate the influence of pre-diagnostic lung emphysema on the incidence and extent of pneumothoraces and to establish a risk stratification for the evaluation of the pre-procedure complication probability. CT-guided PTPB of 100 pre-selected patients (mean age 67.1±12.8 years) were retrospectively enrolled from a single center database of 235 PTPB performed between 2012–2014. Patients were grouped according to pneumothorax appearance directly after PTPB (group I: without pneumothorax, n = 50; group II: with pneumothorax, n = 50). Group II was further divided according to post-interventional treatment (group IIa: chest tube placement, n = 24; group IIb: conservative therapy, n = 26). For each patient pre-diagnostic percentage of emphysema was quantified using CT density analysis. Emphysema stages were compared between groups using bivariate analyses and multinomial logistic regression analyses. Emphysema percentage was significantly associated with the occurrence of post-interventional pneumothorax (p = 0.006). Adjusted for potential confounders (age, gender, lesion size and length of interventional pathway) the study yielded an OR of 1.07 (p = 0.042). Absolute risk of pneumothorax increased from 43.4% at an emphysema rate of 5% to 73.8% at 25%. No differences could be seen in patients with pneumothorax between percentage of emphysema and mode of therapy (p = 0.721). The rate of lung emphysema is proportionally related to the incidence of pneumothorax after CT-guided PTPB and allows pre-interventional risk stratification. There is no association between stage of emphysema and post-interventional requirement of chest tube placement.