| 1. |
- Li, Sirui, et al.
(författare)
-
Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging
- 2021
-
Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 31:11, s. 8197-8207
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. Materials and methods: Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. Results: FA, MD, MKA, MKI, MKT, μFA, and MKA/MKT differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MKI: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MKT and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MKI had the highest correlation coefficient (rs = 0.618). Conclusion: DIVIDE is a promising technique for glioma characterization and diagnosis. Key Points: • DIVIDE metrics MKIis related to cell density heterogeneity while MKAand μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKIwas larger than MKAwhich indicates predominant cell density heterogeneity in gliomas. • MKAand MKIincreased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKAin relation to the cell density heterogeneity metric MKI.
|
|
| 2. |
- Shen, Sirui, et al.
(författare)
-
A Hierarchical Parallel Discrete Gaussian Sampler for Lattice-Based Cryptography
- 2022
-
Ingår i: 2022 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 22). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1729-1733
-
Konferensbidrag (refereegranskat)abstract
- Discrete Gaussian sampling is one of the important components in lattice-based cryptosystems which are promising candidates for post-quantum cryptographic algorithms. For sufficient security and satisfactory performance, the Knuth-Yao algorithm is an efficient way to implement discrete Gaussian samplers. Nevertheless, most polynomials in lattice-based cryptography have 256 coefficients or more, which suffers from long latency to complete the sample generation. In this paper, the first parallel discrete Gaussian sampler with hierarchical structure is proposed, while keeping statistical distance to the actual distribution. Based on the imbalanced visiting frequency of the probability matrix, a three-stage generation strategy is adopted with hierarchical bit search units (BSUs) that can greatly reduce area consumption of the repeated costly lookup tables. Besides the architecture improvement, a lowest-set-bit scanning scheme is introduced to BSUs. Moreover, the parallelism of our design provides obfuscation ability against side-channel attacks (SCAs). A practical hardware implementation of discrete Gaussian distributions with sigma = 3.33 on the Xilinx Virtex-5 XC5VLX30 FPGA device spends 26.12 ns on average to generate 256 samples, consuming 994 slices. Results have verified its advantages of area efficiency over the state-of-the-arts (SOAs).
|
|
| 3. |
- Song, Wenqing, et al.
(författare)
-
Heterogeneous Reconfigurable Accelerator for Homomorphic Evaluation on Encrypted Data
- 2024
-
Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 12, s. 11850-11864
-
Tidskriftsartikel (refereegranskat)abstract
- Homomorphic encryption (HE) enables third -party servers to perform computations on encrypted user data while preserving privacy. Although conceptually attractive, the speed of software implementations of HE is almost impractical. To address this challenge, various domain -specific architectures have been proposed to accelerate homomorphic evaluation, but efficiency remains a bottleneck. In this paper, we propose a homomorphic evaluation accelerator with heterogeneous reconfigurable modular computing units (RCUs) for the Brakerski/Fan-Vercauteren (BFV) scheme. RCUs leverage operator abstraction to efficiently perform basic sub -operations of homomorphic evaluation such as residue number system (RNS) conversion, number theoretic transform (NTT), and other modular computations. By combining these sub -operations, complex homomorphic evaluation operations like multiplication, rotation, and addition are efficiently executed. To address the high demand for data access and improve memory efficiency, we design a coordinate -based address encoding strategy that enables in -place and conflict -free data access. Furthermore, specific optimizations are performed on the core sub -operations such as NTT and automorphism. The proposed architecture is implemented on Xilinx Virtex-7 and UltraScale+ FPGA platforms and evaluated for polynomials of length 4096. Compared to state-of-the-art accelerators with the same parameter set, our accelerator achieves the following advantages: 1) 2.04x to 3.33x reduction in the area -time product (ATP) for the key sub -operation NTT, 2) 1.08x to 7.42x reduction in latency for homomorphic multiplication with higher area efficiency, and 3) support for a wider range of homomorphic evaluation operations, including rotation, compared to other BFV-based accelerators.
|
|
| 4. |
- Sun, Wenbo, et al.
(författare)
-
Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI
-
Ingår i: Magnetic Resonance Materials in Physics, Biology and Medicine. - 0968-5243.
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. Materials and methods: Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80–200 ms) with seven b-values (0–3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. Results: HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p <.05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). Discussion: HM-MRI can aid in non-invasive diagnosis of gliomas.
|
|
