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Background: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. Methods: Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test–retest reproducibility ADPKD patients. Results: Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test–retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. Conclusions: Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted. [ABSTRACT FROM AUTHOR]

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired. [ABSTRACT FROM AUTHOR]

Solid electrolyte interphase (SEI) formation and H2O activity reduction in Water‐in‐Salt electrolytes (WiSE) with an enlarged stability window of 3.0 V have provided the feasibility of the high‐energy‐density aqueous Li‐ion batteries. Here, we extend the cathodic potential of WiSE by rationally controlling intermolecular interaction and interphase chemistry with the introduction of trimethyl phosphate (TMP) into WiSE. The TMP not merely limits the H2O activity via the strong interaction between TMP and H2O but also contributes to the formation of reinforced SEI involving phosphate and LiF by manipulating the Li+ solvation structure. Thus, water‐tolerance LiMn2O4 (LMO)||Li4Ti5O12 (LTO) full cell with a P/N ratio of 1.14 can be assembled and achieve a long cycling life of 1000 times with high coulombic efficiency of >99.9 %. This work provides a promising insight into the cost‐effective practical manufacture of LMO||LTO cells without rigorous moisture‐free requirements. [ABSTRACT FROM AUTHOR]

To drive electrical vehicles for long‐range, the energy density of Li‐ion batteries must be further enhanced, which requires high‐energy cathode materials. Among them, Ni‐rich LiNixMnyCo1−x–yO2 (x > 0.5, NMC) is one of the most promising candidates. However, traditional poly‐crystal (PC) NMC materials, whose particles are secondary clusters consisting of many primary crystalline particles, are susceptible to pulverization along the inter grain/particle boundaries, resulting in poor cycle stability. Recently, single crystal (SC)‐NMC cathodes are proposed as they are believed to have several merits such as high structural integrity, lower interface with electrolyte, and potentially better energy storage performance. Nevertheless, several queries, such as the merits of SC structure and the mechanism for their structure degradation, are still controversial and required to be addressed. Herein, the synthesis parameters, properties, energy storage as well as safety of SC‐ and PC‐NMC cathodes with various Ni contents are critically reviewed, for clarifying the merits and drawbacks of SC. Furthermore, the study focuses on the recent advances of insight mechanisms and strategies for stabilizing the structure/interface by doping, morphology engineering, surface coating, and composition tailoring. The remaining challenges, safety concerns, and perspectives to enhance the electrochemical performance of SC‐NMC cathodes for electric vehicle application are followed. [ABSTRACT FROM AUTHOR]

LiNixCoyMnzO2 (x+y+z=1)||graphite lithium‐ion battery (LIB) chemistry promises practical applications. However, its low‐temperature (≤ −20 °C) performance is poor because the increased resistance encountered by Li+ transport in and across the bulk electrolytes and the electrolyte/electrode interphases induces capacity loss and battery failures. Though tremendous efforts have been made, there is still no effective way to reduce the charge transfer resistance (Rct) which dominates low‐temperature LIBs performance. Herein, we propose a strategy of using low‐polarity‐solvent electrolytes which have weak interactions between the solvents and the Li+ to reduce Rct, achieving facile Li+ transport at sub‐zero temperatures. The exemplary electrolyte enables LiNi0.8Mn0.1Co0.1O2||graphite cells to deliver a capacity of ≈113 mAh g−1 (98 % full‐cell capacity) at 25 °C and to remain 82 % of their room‐temperature capacity at −20 °C without lithium plating at 1/3C. They also retain 84 % of their capacity at −30 °C and 78 % of their capacity at −40 °C and show stable cycling at 50 °C. [ABSTRACT FROM AUTHOR]