Home sleep apnea testing: an accuracy study

Sleep and Breathing - There are no studies comparing tests performed at home with those carried out in the laboratory, using the same device. The only studies that have been performed...     

Correction to: Disease burden and treatment sequence of polymyositis and dermatomyositis patients in Japan: a real-world evidence study

Clinical Rheumatology -     

Repeated hepatic resection for colorectal liver metastases: is this concept safe and feasible?

European Surgery - The beneficial outcomes of hepatectomy in patients with colorectal metastases have encouraged the attempts of repeated hepatectomy in patients with recurrent disease. Although...     

Fatal cases of hemorrhagic fever with renal syndrome in Udmurtia,Russia, 2010 to 2019

European Journal of Clinical Microbiology & Infectious Diseases - Hemorrhagic fever with renal syndrome (HFRS) continues to be a cause of death in Europe. Our aim was to describe the clinical...     

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast and accurate image reconstruction for both single-slice (SS) and simultaneous multislice (SMS) acquisitions. Three-dimensional U-Net–based image enhancement architectures were evaluated for high-resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1-SPIRiT, MB = 2 DESIRE, and MB = 2 SMS-slice-L1-SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1-SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1-SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high-quality image reconstruction for both SS and SMS MB = 2 whole-heart high-resolution spiral perfusion imaging.     

Future perspectives: Novel Frontiers in Cancer Metastasis

Clinical & Experimental Metastasis -     

Initial assessment of α-synuclein structure in platelets

Journal of Thrombosis and Thrombolysis - Over the last few years data from our group have indicated that α-synuclein is important in development of immune cells as well as potentially...     

The current status of surgical pilonidal sinus disease therapy in Germany

European Surgery - Treatment of pilonidal sinus disease (PSD) requires a tailored approach. A national guideline was published in 2014. The current status of surgical PSD therapy...