Inter-scanner super-resolution of 3D cine MRI using a transfer-learning network for MRgRT.

Objective. Deep-learning networks for super-resolution (SR) reconstruction enhance the spatial-resolution of 3D magnetic resonance imaging (MRI) for MR-guided radiotherapy (MRgRT). However, variations between MRI scanners and patients impact the quality of SR for real-time 3D low-resolution (LR) cine MRI. In this study, we present a personalized super-resolution (psSR) network that incorporates transfer-learning to overcome the challenges in inter-scanner SR of 3D cine MRI.Approach: Development of the proposed psSR network comprises two-stages: (1) a cohort-specific SR (csSR) network using clinical patient datasets, and (2) a psSR network using transfer-learning to target datasets. The csSR network was developed by training on breath-hold and respiratory-gated high-resolution (HR) 3D MRIs and their k-space down-sampled LR MRIs from 53 thoracoabdominal patients scanned at 1.5 T. The psSR network was developed through transfer-learning to retrain the csSR network using a single breath-hold HR MRI and a corresponding 3D cine MRI from 5 healthy volunteers scanned at 0.55 T. Image quality was evaluated using the peak-signal-noise-ratio (PSNR) and the structure-similarity-index-measure (SSIM). The clinical feasibility was assessed by liver contouring on the psSR MRI using an auto-segmentation network and quantified using the dice-similarity-coefficient (DSC).Results. Mean PSNR and SSIM values of psSR MRIs were increased by 57.2% (13.8-21.7) and 94.7% (0.38-0.74) compared to cine MRIs, with the reference 0.55 T breath-hold HR MRI. In the contour evaluation, DSC was increased by 15% (0.79-0.91). Average time consumed for transfer-learning was 90 s, psSR was 4.51 ms per volume, and auto-segmentation was 210 ms, respectively.Significance. The proposed psSR reconstruction substantially increased image and segmentation quality of cine MRI in an average of 215 ms across the scanners and patients with less than 2 min of prerequisite transfer-learning. This approach would be effective in overcoming cohort- and scanner-dependency of deep-learning for MRgRT.

Fabrication of a multi-walled carbon nanotube-deposited glass fiber air filter for the enhancement of nano and submicron aerosol particle filtration and additional antibacterial efficacy.

We grew multi-walled carbon nanotubes (MWCNTs) on a glass fiber air filter using thermal chemical vapor deposition (CVD) after the filter was catalytically activated with a spark discharge. After the CNT deposition, filtration and antibacterial tests were performed with the filters. Potassium chloride (KCl) particles (<1 µm) were used as the test aerosol particles, and their number concentration was measured using a scanning mobility particle sizer. Antibacterial tests were performed using the colony counting method, and Escherichia coli (E. coli) was used as the test bacteria. The results showed that the CNT deposition increased the filtration efficiency of nano and submicron-sized particles, but did not increase the pressure drop across the filter. When a pristine glass fiber filter that had no CNTs was used, the particle filtration efficiencies at particle sizes under 30 nm and near 500 nm were 48.5% and 46.8%, respectively. However, the efficiencies increased to 64.3% and 60.2%, respectively, when the CNT-deposited filter was used. The reduction in the number of viable cells was determined by counting the colony forming units (CFU) of each test filter after contact with the cells. The pristine glass fiber filter was used as a control, and 83.7% of the E. coli were inactivated on the CNT-deposited filter.

[Diagnosis of irritable bowel syndrome: a systematic review].

Irritable bowel syndrome(IBS) is a very common functional gastrointestinal disorder characterized by abdominal discomfort, bloating, and disturbed defecation. Patients with IBS have a tendency to visit physicians more frequently than those without IBS, thus annual economic consequences of IBS in the Western countries are substantial. Therefore, guidelines for the diagnosis and treatment of IBS patients have been designed to give a favored effect on the Department of Gastroenterology's overall performance. A variety of criteria have been developed to identify a combination of symptoms to diagnose IBS, including Manning and Rome I, II, and III criteria. Overall, Manning's criteria had a pooled sensitivity and specificity, 78% and 72%, respectively. In addition, the Rome I criteria had a sensitivity and specificity, 71% and 85%, respectively. However, none described the accuracy of Rome II and III yet. Alarm features such as rectal bleeding and nocturnal pain offer little discriminative value in separating patients with IBS from those with organic diseases. Even though anemia and weight loss have poor sensitivity for organic diseases, they offer very good specificity. Since specific biomarker of IBS is not yet available, diagnostic tests are frequently performed to exclude organic diseases. However, the accuracy of diagnostic tests is disappointing. CBC, chemistry, thyroid function test, stool exam, ultrasonography, hydrogen breath test, erythrocyte sedimentation rate, and C-reactive protein have all very limited accuracy in discriminating IBS from organic diseases. This systemic review is targeted to establish the strategy of IBS treatment, which is very necessary for the current clinical practice.