Opportunistic screening with multiphase contrast-enhanced dual-layer spectral CT for osteoblastic lesions in prostate cancer compared with bone scintigraphy.

Our study aimed to compare bone scintigraphy and dual-layer detector spectral CT (DLCT) with multiphase contrast enhancement for the diagnosis of osteoblastic bone lesions in patients with prostate cancer. The patients with prostate cancer and osteoblastic bone lesions detected on DLCT were divided into positive bone scintigraphy group (pBS) and negative bone scintigraphy group (nBS) based on bone scintigraphy. A total of 106 patients (57 nBS and 49 pBS) was included. The parameters of each lesion were measured from DLCT including Hounsfield unit (HU), 40-140 keV monochromatic HU, effective nuclear numbers (Zeff), and Iodine no water (InW) value in non-contrast phase (N), the arterial phase (A), and venous phase (V). The slope of the spectral curve at 40 and 100 keV, the different values of the parameters between A and N phase (A-N), V and N phase (V-N), and hybrid prediction model with multiparameters were used to differentiate pBS from nBS. Receiver operating characteristic analysis was performed to compare the area under the curve (AUC) for differentiating the pBS group from the nBS group. The value of conventional HU values, slope, and InW in A-N and V-N, and hybrid model were significantly higher in the pBS group than in the nBS group. The hybrid model of all significant parameters had the highest AUC of 0.988, with 95.5% sensitivity and 94.6% specificity. DLCT with arterial contrast enhancement phase has the potential to serve as an opportunistic screening tool for detecting positive osteoblastic bone lesions, corresponding to those identified in bone scintigraphy.

Understanding ADC variation by fat content effect using a dual-function MRI phantom.

BACKGROUND: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. METHODS: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the "iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ," IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. RESULTS: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R2 = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. CONCLUSIONS: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. RELEVANCE STATEMENT: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. KEY POINTS: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values are determined by GBD and FF. • The dual-function phantom showed the mutual ADC addition between FF and GBD.

Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding.

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.

Spleen-preserving distal pancreatectomy from multi-port to reduced-port surgery approach.

BACKGROUND: Minimally invasive pancreatic surgery via the multi-port approach has become a primary surgical method for distal pancreatectomy (DP) due to its advantages of lower wound pain and superior cosmetic results. Some studies have applied reduced-port techniques for DP in an attempt to enhance cosmetic outcomes due to the minimally invasive effects. Numerous recent review studies have compared multi-port laparoscopic DP (LDP) and multi-port robotic DP (RDP); most of these studies concluded multi-port RDP is more beneficial than multi-port LDP for spleen preservation. However, there have been no comprehensive reviews of the value of reduced-port LDP and reduced-port RDP. AIM: To search for and review the studies on spleen preservation and the clinical outcomes of minimally invasive DP that compared reduced-port DP surgery with multi-port DP surgery. METHODS: The PubMed medical database was searched for articles published between 2013 and 2022. The search terms were implemented using the following Boolean search algorithm: ("distal pancreatectomy" OR "left pancreatectomy" OR "peripheral pancreatic resection") AND ("reduced-port" OR "single-site" OR "single-port" OR "dual-incision" OR "single-incision") AND ("spleen-preserving" OR "spleen preservation" OR "splenic preservation"). A literature review was conducted to identify studies that compared the perioperative outcomes of reduced-port LDP and reduced-port RDP. RESULTS: Fifteen articles published in the period from 2013 to 2022 were retrieved using three groups of search terms. Two studies were added after manually searching the related papers. Finally, 10 papers were selected after removing case reports (n = 3), non-English language papers (n = 1), technique papers (n = 1), reviews (n = 1), and animal studies (n = 1). The common items were defined as items reported in more than five papers, and data on these common items were extracted from all papers. The ten studies included a total of 337 patients (females/males: 231/106) who underwent DP. In total, 166 patients (females/males, 106/60) received multi-port LDP, 126 (females/males, 90/36) received reduced-port LDP, and 45 (females/males, 35/10) received reduced-port RDP. CONCLUSION: Reduced-port RDP leads to a lower intraoperative blood loss, a lower postoperative pancreatic fistula rate, and shorter hospital stay and follow-up duration, but has a lower spleen preservation rate.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.

BACKGROUND: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography.

Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal-Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.

OBJECTIVES: To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS). METHODS: Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant. RESULTS: Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm2 for GT on DWI to 0.59 cm2 for prediction at an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. CONCLUSIONS: Combining an ADC threshold of 0.6 × 10-3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs. KEY POINTS: • Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10-3 mm2/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Multi-shot Diffusion-Weighted MRI With Multiplexed Sensitivity Encoding (MUSE) in the Assessment of Active Inflammation in Crohn's Disease.

BACKGROUND: Single-shot diffusion-weighted imaging (ssDWI) has been shown useful for detecting active bowel inflammation in Crohn's disease (CD) without MRI contrast. However, ssDWI suffers from geometric distortion and low spatial resolution. PURPOSE: To compare conventional ssDWI with higher-resolution ssDWI (HR-ssDWI) and multi-shot DWI based on multiplexed sensitivity encoding (MUSE-DWI) for evaluating bowel inflammation in CD, using contrast-enhanced MR imaging (CE-MRI) as the reference standard. STUDY TYPE: Prospective. SUBJECTS: Eighty nine patients with histological diagnosis of CD from previous endoscopy (55 male/34 female, age: 17-69 years). FIELD STRENGTH/SEQUENCES: ssDWI (2.7 mm × 2.7 mm), HR-ssDWI (1.8 mm × 1.8 mm), MUSE-DWI (1.8 mm × 1.8 mm) based on echo-planar imaging, T2-weighted imaging, and CE-MRI sequences, all at 1.5 T. ASSESSMENT: Five raters independently evaluated the tissue texture conspicuity, geometry accuracy, minimization of artifacts, diagnostic confidence, and overall image quality using 5-point Likert scales. The diagnostic performance (sensitivity, specificity and accuracy) of each DWI sequences was assessed on per-bowel-segment basis. STATISTICAL TESTS: Inter-rater agreement for qualitative evaluation of each parameter was measured by the intra-class correlation coefficient (ICC). Paired Wilcoxon signed-rank tests were performed to evaluate the statistical significance of differences in qualitative scoring between DWI sequences. A P value <0.05 was considered to be statistically significant. RESULTS: Tissue texture conspicuity, geometric distortions, and overall image quality were significantly better for MUSE-DWI than for ssDWI and HR-ssDWI with good agreement among five raters (ICC: 0.70-0.89). HR-ssDWI showed significantly poorer performance to ssDWI and MUSE-DWI for all qualitative scores and had the worst diagnostic performance (sensitivity of 57.0% and accuracy of 87.3%, with 36 undiagnosable cases due to severe artifacts). MUSE-DWI showed significantly higher sensitivity (97.5% vs. 86.1%) and accuracy (98.9% vs. 95.1%) than ssDWI for detecting bowel inflammation. DATA CONCLUSION: MUSE-DWI was advantageous in assessing bowel inflammation in CD, resulting in improved spatial resolution and image quality. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors.

Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function.

The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters Ktrans, ve, and vp, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal-Wallis test with a Dunn-Bonferroni post hoc analysis were performed. In simulation, Ktrans and ve increased as the peak in the AIF decreased, but vp increased when delay time in the AIF increased. In humans, the estimated Ktrans and ve were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

Classification of parotid gland tumors by using multimodal MRI and deep learning.

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

Imaging quality of PROPELLER diffusion-weighted MR imaging and its diagnostic performance in distinguishing pleomorphic adenomas from Warthin tumors of the parotid gland.

The aim of this study was to evaluate the imaging quality and diagnostic performance of fast spin echo diffusion-weighted imaging with periodically rotated overlapping parallel lines with enhanced reconstruction (FSE-PROP-DWI) in distinguishing parotid pleomorphic adenoma (PMA) from Warthin tumor (WT). This retrospective study enrolled 44 parotid gland tumors from 34 patients, including 15 PMAs and 29 WTs with waived written informed consent. All participants underwent 1.5 T diffusion-weighted imaging including FSE-PROP-DWI and single-shot echo-planar diffusion-weighted imaging (SS-EP-DWI). After imaging resizing and registration among T2WI, FSE-PROP-DWI and SS-EP-DWI, imaging distortion was quantitatively analyzed by using the Dice coefficient. Signal-to-noise ratio and contrast-to-noise ratio were qualitatively evaluated. The mean apparent diffusion coefficient (ADC) of parotid gland tumors was calculated. Wilcoxon signed-rank test was used for paired comparison between FSE-PROP-DWI versus SS-EP-DWI. Mann-Whitney U test was used for independent group comparison between PMAs versus WTs. Diagnostic performance was evaluated by receiver operating characteristics curve analysis. P < 0.05 was considered statistically significant. The Dice coefficient was statistically significantly higher on FSE-PROP-DWI than SS-EP-DWI for both tumors (P < 0.005). Mean ADC was statistically significantly higher in PMAs than WTs on both FSE-PROP-DWI and SS-EP-DWI (P < 0.005). FSE-PROP-DWI and SS-EP-DWI successfully distinguished PMAs from WTs with an AUC of 0.880 and 0.945, respectively (P < 0.05). Sensitivity, specificity, positive predictive value, negative predictive value and accuracy in diagnosing PMAs were 100%, 69.0%, 62.5%, 100% and 79.5% for FSE-PROP-DWI, and 100%, 82.8%, 75%, 100% and 88.6% for SS-EP-DWI, respectively. FSE-PROP-DWI is useful to distinguish parotid PMAs from WTs with less distortion of tumors but lower AUC than SS-EP-DWI.

Gender interactions between vertebral bone mineral density and fat content in the elderly: Assessment using fat-water MRI.

BACKGROUND: Adipose tissue is closely related to bone mass, bone quality, and bone fractures, but the connection between fat and bone is complex and gender-related. Fat-water magnetic resonance imaging (MRI) and MR spectroscopy (MRS) are very useful tools for identifying tissue fat. PURPOSE: To assess gender interactions between bone mineral density (BMD), bone marrow fat, and body mass index (BMI) in the elderly using fat-water MRI and MRS. STUDY TYPE: Prospective/cohort. POPULATION: Sixty-six women and 38 men (mean age, 62.3 years; range, 50-75 years), Asian. FIELD STRENGTH: A 1.5T MR equipped with a body and spine array coil. STEAM MRS and T2 * Dixon were performed. ASSESSMENT: Vertebral bone marrow fat ratio (MFR), BMI, and BMD were measured. Correlations between these variables and differences in bone density in MFR were assessed between participants, divided into three groups based on bone density. STATISTICAL TESTS: Multiple regression; Pearson tests; analysis of covariance; analysis of variance. RESULTS: Multiple regression analysis identified gender, vertebral bone MFR, and BMI as significant predictors of vertebral BMD (P < 0.001). Among the women, vertebral BMD was negatively correlated with vertebral MFR (P = 0.011), but among the men, it was positively correlated with BMI (P = 0.048), although this relationship was confounded by age and MFR. Moreover, vertebral bone marrow fat and BMI were indeed statistically uncorrelated in the elderly (P = 0.357 in women; P = 0.961 in men). DATA CONCLUSION: We found gender interactions between fat and bone in the elderly. Higher bone marrow fat was correlated with lower trabecular BMD in older women but not in men. On the other hand, the positive correlation between BMI and BMD was more pronounced in men than in women. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1382-1389.

Manipulating the Temperature of Sulfurization to Synthesize α-NiS Nanosphere Film for Long-Term Preservation of Non-enzymatic Glucose Sensors.

In this study, alpha nickel sulfide (α-NiS) nanosphere films have been successfully synthesized by electroplating the nickel nanosheet film on the indium tin oxide (ITO) glass substrate and sulfuring nickel-coated ITO glass substrate. First, we electrodeposited the nickel nanosheet films on the ITO glass substrates which were cut into a 0.5 × 1 cm2 size. Second, the nanosheet nickel films were annealed in vacuum-sealed glass ampoules with sulfur sheets at different annealing temperatures (300, 400, and 500 °C) for 4 h in vacuum-sealed glass ampoules. The α-NiS films were investigated by using X-ray diffraction (XRD), variable vacuum scanning electron microscopy (VVSEM), field emission scanning electron microscopy/energy dispersive spectrometer (FE-SEM/EDS), cyclic voltammogram (CV), electrochemical impedance spectroscopy (EIS), ultraviolet/visible/near-infrared (UV/Visible/NIR) spectra, and photoluminescence (PL) spectra. Many nanospheres were observed on the surface of the α-NiS films at the annealing temperature 400 °C for 4 h. We also used the high-resolution transmission electron microscopy (HR-TEM) for the analysis of the α-NiS nanospheres. We demonstrated that our α-NiS nanosphere film had a linear current response to different glucose concentrations. Additionally, our α-NiS nanosphere films were preserved at room temperature for five and a half years and were still useful for detecting glucose at low concentration.

Application of a commercial single-port device for robotic single-incision distal pancreatectomy: initial experience.

PURPOSE: Laparoscopic distal pancreatectomy has proven to be feasible and safe. Moreover, robotic surgery provides unique advantages for pancreatic procedures, although single-incision robotic pancreatic surgery is rarely discussed. We applied the single-port modified platform to accomplish robotic distal pancreatectomy in a series of patients. METHODS: The subjects of this study were ten patients who underwent robotic distal pancreatectomy in our hospital between July 1, 2015 and Dec 31, 2016. All patients were placed supine in the reverse Trendelenburg position with the legs abducted. Surgery was performed via a trans-umbilical 5.0-cm incision, using a modified single-port platform (LAGIPORT®) combined with the da Vinci Si Surgical System. The three arms and scope (30-degree up) were inserted through the LAGIPORT® and positioned in a triangle. Endoscopic ultrasound was used to localize the tumor and plan the resection margin. We recorded the surgical time, operation time, blood loss, postoperative pain score, hospital stay, and complications. RESULTS: The surgical time was 236 ± 32 min, the operation time was 172 ± 30 min, and the blood loss was 149 ± 65 ml. All patients underwent robot-assisted distal pancreatectomy without conversion. The average pain score on postoperative day (POD) 3 was 4.5 ± 1. Complications included subsplenic hematoma (n = 1) and minor pancreatic leakage (n = 2). There was no surgical mortality. CONCLUSIONS: Our results demonstrate the safety and efficiency of robotic single-incision distal pancreatectomy via the modified platform (LAGIPORT®).

Proton change of parotid glands after gustatory stimulation examined by magnetic resonance imaging.

The aim of this study was to investigate proton changes of the parotid gland after gustatory stimulation by semi-quantitative parameters and an empirical mathematical model (EMM) using high-temporal-resolution, double-echo, echo-planar imaging (EPI). Approved by a local institutional review board, this study examined 20 parotid glands from 10 healthy volunteers (male:female = 6: 4; age ± standard deviation =35.1 ± 14.1 years) with written informed consent obtained. All participants underwent 1.5-T, double-echo EPI with gustatory stimulation. Semi-quantitative parameters, including maximal drop ratio (MDR), time to peak (TTP), drop slope (DS), recovery slope (RS) and recovery ratio (RR), were calculated. The effect of temporal resolution on parotid functional parameters was evaluated. An EMM comprising an output function ( Sot=Aoe-kot+B) and an input function ( Sint=Ain1-e-kint) was also applied to fit all dynamic curves. Kruskal-Wallis test, Wilcoxon test, linear regression analysis and goodness of fit were used for statistical analysis. p < 0.05 was considered to be statistically significant. The signal intensity dropped significantly after gustatory stimulation on the proton density (PD) image (p < 0.01). MDR was 8.26% in the PD image. MDR and RR were negatively associated with time interval, whereas DS and TTP were significantly positively associated with time interval (all p < 0.05). EMM parametric values derived from PD-time curves of parotid glands were 12.04 ± 6.81%, 6.43 ± 4.23 min-1 , 88.73 ± 6.18%, 8.41 ± 4.86 min-1 and 1.09 ± 1.35 for Ao , ko , B, Ain and kin , respectively. Semi-quantitative functional parameters and EMM parameters using high-temporal-resolution, double-echo EPI allow the quantification of parotid proton changes after gustatory stimulation.