Appropriate pancreatic phase image acquisition by free-breathing dynamic contrast-enhanced pancreatic MRI using stack-of-stars radial sampling and Compressed SENSE.

PURPOSE: To evaluate the feasibility of a free-breathing sequence (4D FreeBreathing) combined with Compressed SENSE in dynamic contrast-enhanced pancreatic MRI and compare it with a breath-holding sequence (eTHRIVE). METHOD: Patients who underwent pancreatic MRI, either eTHRIVE or 4D FreeBreathing, from April 2022 to November 2023 were included in this retrospective study. Two radiologists, who were unaware of the scan sequence, independently and randomly reviewed the images at the precontrast, pancreatic, portal venous, and equilibrium phases and assigned confidence scores for motion and streaking artifacts, pancreatic sharpness, and overall image quality using a 5-point scale. Furthermore, the radiologists assessed the appropriateness of the scan timing of the pancreatic phase. Mann-Whitney U and Fisher's exact tests were conducted to compare the confidence scores and adequacy of the pancreatic phase scan timing between eTHRIVE and 4D FreeBreathing. RESULTS: Overall, 48 patients (median age, 71 years; interquartile range, 64-77 years; 24 women) were included. Among them, 20 patients (42%) were scanned using 4D FreeBreathing. The 4D FreeBreathing showed moderate streaking artifact but improved motion artifact (P <.001-.17) at all phases. Pancreatic sharpness and overall image quality were almost comparable between two sequences (P = .17-.96). All 20 examinations in 4D FreeBreathing showed appropriate pancreatic phase images, but only 16 (57%; P <.001 for reviewer 1) and 18 (64%; P = .003 for reviewer 2) examinations showed it in eTHRIVE. CONCLUSION: The use of 4D FreeBreathing combined with Compressed SENSE was feasible in pancreatic MRI and provided appropriate pancreatic phase images in all examinations.

Diagnostic ability of diffusion-weighted imaging using echo planar imaging with compressed SENSE (EPICS) for differentiating hepatic hemangioma and liver metastasis.

PURPOSE: To assess the diagnostic abilities of diffusion-weighted imaging (DWI) with parallel imaging (PI-DWI) and that with Compressed SENSE (EPICS-DWI) for differentiating hepatic hemangiomas (HHs) and liver metastases (LMs). METHOD: This prospective study included 30 participants with HH and/or LM who underwent PI-DWI and EPICS-DWI. Two radiologists assessed the DWI images and assigned confidence scores for hepatic lesions conspicuity using 4-point scale. One of the radiologists additionally calculated the contrast-to-noise ratio (CNR) and measured ADC value of the hepatic lesions. The conspicuity, CNR, and ADC values were compared between the two sequences. A receiver operating characteristic (ROC) analysis was performed to assess the diagnostic abilities of the two sequences for differentiating HHs and LMs. RESULTS: The conspicuity of LMs was better in EPICS-DWI than in PI-DWI (P < .05 in both radiologists). The CNR of LMs was higher in EPICS-DWI than in PI-DWI (P = .008). No difference was found in the CNR of HHs (P = .52), ADC values for HHs (P = .79), and LMs (P = .29) between the two sequences. To differentiate between HHs and LMs, the cutoff ADC values were 1.38 × 10-3 mm2/s in PI-DWI and 1.37 × 10-3 mm2/s in EPICS-DWI. The area under the ROC curve (P = .86), sensitivity (P > .99), and specificity (P > .99) did not vary. CONCLUSIONS: The LMs were more visible in EPICS-DWI than in PI-DWI. However, the cutoff ADC values and diagnostic abilities for differentiating HHs and LMs were almost comparable between the two sequences.

Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction.

PURPOSE: To evaluate the image quality and visibility of pancreatic ductal adenocarcinoma (PDAC) in 80-kVp pancreatic CT protocol and compare them between hybrid-iterative reconstruction (IR) and deep-learning image reconstruction (DLIR) algorithms. METHOD: A total of 56 patients who underwent 80-kVp pancreatic protocol CT for pancreatic disease evaluation from January 2022 to July 2022 were included in this retrospective study. Among them, 20 PDACs were observed. The CT raw data were reconstructed using 40% adaptive statistical IR-Veo (hybrid-IR group) and DLIR at medium- and high-strength levels (DLIR-M and DLIR-H groups, respectively). The CT attenuation of the abdominal aorta, pancreas, and PDAC (if present) at the pancreatic phase and those of the portal vein and liver at the portal venous phase; background noise; signal-to-noise ratio (SNR) of these anatomical structures; and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. The confidence scores for the image noise, overall image quality, and visibility of PDAC were qualitatively assigned using a five-point scale. Quantitative and qualitative parameters were compared among the three groups using Friedman test. RESULTS: The CT attenuation of all anatomical structures were comparable among the three groups (P = .26-.86), except that of the pancreas (P = .001). Background noise was lower (P <.001) and SNRs (P <.001) and tumor-to-pancreas CNR (P <.001) were higher in the DLIR-H group than those in the other two groups. The image noise, overall image quality, and visibility of PDAC were better in the DLIR-H group than in the other two groups (P <.001-.003). CONCLUSION: In 80-kVp pancreatic CT protocol, DLIR at a high-strength level improved image quality and visibility of PDAC.

Comparison of image quality and pancreatic ductal adenocarcinoma conspicuity between the low-kVp and dual-energy CT reconstructed with deep-learning image reconstruction algorithm.

PURPOSE: To compare the image quality and conspicuity of pancreatic ductal adenocarcinoma (PDAC) between the low-kVp and dual-energy pancreatic protocol CT reconstructed with deep-learning image reconstruction (DLIR). METHOD: A cohort of 111 consecutive patients (median age, 72 years; 56 men) undergoing a pancreatic protocol CT were retrospectively analyzed. Among them, 58 patients underwent 80-kVp CT (80-kVp group), and 53 patients underwent dual-energy CT and reconstructed at 40-keV (40-keV group). The medium-strength level of DLIR were used in both groups. Quantitative measurements, qualitative image quality, PDAC conspicuity, and dose-length product (DLP) were compared between the two groups using Mann-Whitney U test. RESULTS: A total of 20 and 16 PDACs were found in the 80-kVp and 40-keV groups, respectively. CT numbers of the vasculatures and parenchymal organs (P <.001 for all) and the background noise at both pancreatic and portal venous phases (P <.001) were higher in the 40-keV group than in the 80-kVp group. The signal-to-noise ratio (SNR) of all anatomical structures (P <.001-0.005), except for the liver in reviewer 2 (P =.47), and the tumor-to-pancreas contrast-to-noise ratio (CNR; P <.001-0.01) were higher in the 40-keV group than in the 80-kVp group. No difference was found in the image quality at both phases (P =.30-0.90). PDAC conspicuity was better in the 40-keV group than in the 80-kVp group (P =.007-0.03). DLP at pancreatic (275 vs. 313 mGy*cm; P =.05) and portal venous phases (743 vs. 766 mGy*cm; P =.20) was comparable between the two groups. CONCLUSION: Under the same DLP, virtual monoenergetic images at 40-keV demonstrated higher SNR and tumor-to-pancreas CNR and better PDAC conspicuity compared to the 80-kVp setting.

Ultra-Fine Bubble Distributions in a Plant Factory Observed by Transmission Electron Microscope with a Freeze-Fracture Replica Technique.

Water containing ultra-fine bubbles (UFB) may promote plant growth. But, as UFBs are too small to distinguish from other impurities in a nutrient solution, it is not known if UFBs survive transport from the water source to the rhizosphere. Here we use the freeze-fracture replica method and a transmission electron microscope (TEM) to observe UFBs in the nutrient solutions used in a crop-growing system known as a plant factory. In this factory, TEM images taken from various points in the supply line indicate that the concentration of UFBs in the nutrient solution is conserved, starting from their addition to the nutrient solution in the buffer tank, through the peat-moss layer, all the way to the rhizosphere. Measurements also show that a thin film formed on the surface of UFBs in the nutrient solution, with greater film thickness at the rhizosphere. This film is considered to be made from the accumulation of impurities coming from solute and the peat-moss layer.

One Model, Two Enzymes: Activation of Hydrogen and Carbon Monoxide.

The ability to catalyze the oxidation of both H2 and CO in one reaction pot would be a major boon to hydrogen technology since CO is a consistent contaminant of H2 supplies. Here, we report just such a catalyst, with the ability to catalyze the oxidation of either or both H2 and CO, based on the pH value. This catalyst is based on a NiIr core that mimics the chemical function of [NiFe]hydrogenase in acidic media (pH 4-7) and carbon monoxide dehydrogenase in basic media (pH 7-10). We have applied this catalyst in a demonstration fuel cell using H2 , CO, and H2 /CO (1/1) feeds as fuels for oxidation at the anode. The power density of the fuel cell depends on the pH value in the media of the fuel cell and shows a similar pH dependence in a flask. We have isolated and characterized all intermediates in our proposed catalytic cycles.