Compressed-sensing accelerated magnetic resonance imaging of inner ear

Clinical utility of single-shot echo-planar diffusion-weighted imaging using L1-regularized iterative sensitivity encoding in prostate MRI

We investigated the ability of echo-planar imaging with L1-regularized iterative sensitivity encoding-based diffusion-weighted imaging (DWI) to improve the image quality and reduce the scanning time in prostate magnetic resonance imaging. We retrospectively analyzed 109 cases of prostate magnetic resonance imaging. We compared variables in the quantitative and qualitative assessments among 3 imaging groups: conventional parallel imaging-based DWI (PI-DWI) with an acquisition time of 3 minutes 15 seconds; echo-planar imaging with L1-regularized iterative sensitivity encoding-based DWI (L1-DWI) with a normal acquisition time (L1-DWINEX12) of 3 minutes 15 seconds; and L1-DWI with a half acquisition time (L1-DWINEX6) of 1 minute 45 seconds. As a quantitative assessment, the signal-to-noise ratio (SNR) of DWI (SNR-DWI), the contrast-to-noise ratio (CNR) of DWI (CNR-DWI), and the CNR of apparent diffusion coefficient were measured. As a qualitative assessment, the image quality and visual detectability of prostate carcinoma were evaluated. In the quantitative analysis, L1-DWINEX12 showed significantly higher SNR-DWI than PI-DWI (P = .0058) and L1-DWINEX6 (P < .0001). In the qualitative analysis, the image quality score for L1-DWINEX12 was significantly higher than those of PI-DWI and L1-DWINEX6. A non-inferiority assessment demonstrated that L1-DWINEX6 was non-inferior to PI-DWI in terms of both quantitative CNR-DWI and qualitative grading of image quality with a <20% inferior margin. L1-DWI successfully demonstrated a reduced scanning time while maintaining good image quality.

Bio-Inspired In-Sensor Compression and Computing Based on Phototransistors

The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium-gallium-zinc-oxide thin film phototransistors exhibit the optoelectronic switching and light-tunable synaptic characteristics for in-sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in-sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in-sensor computing. By integrating these two systems, a neuromorphic system for high-efficiency in-sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human-computer interactions and the Internet of Things.

Compressed-sensing accelerated magnetic resonance imaging of inner ear

Objective

To compare conventional method and compressed‐sensing (CS) accelerated 3D balanced fast field echo imaging (bFFE) of inner ear.

Methods

Twenty patients with suspected inner ear disease underwent CS accelerated 3D‐bFFE (CS‐bFFE) and conventional 3D‐bFFE (Con‐bFFE) by a 3T MRI. The overall image quality, motion artifacts, and image quality of specific structures of inner ear were assessed on ordinal scales by three radiologists who were blinded to the scan protocols. Kendall W test was used to evaluate interobserver agreement and Wilcoxon test was performed to compare the image quality and motion artifacts between CS‐bFFE and Con‐bFFE.

Results

The acquisition duration of CS‐bFFE (1 min 53 s) was 49% faster than Con‐bFFE. Three radiologists had good inter‐observer agreement of image quality (Kendall W value of 0.829 for CS‐bFFE and 0.815 for Con‐bFFE) and motion artifacts evaluation (Kendall W value of 0861 for CS‐bFFE and 0.707 for Con‐bFFE). The better overall image quality of CS‐bFFE was assessed (4.93 ± 0.23 for CS‐bFFE, 4.53 ± 0.70 for Con‐bFFE, Z = −2.254, p = 0.024). The image quality score of facial and cochlear nerve gained higher in CS‐bFFE (4.93 ± 0.23 for CS‐bFFE, 4.58 ± 0.64 for Con‐bFFE, Z = −2.094, p = 0.036). No significant difference of motion artifacts (p = 0.050) between CS‐bFFE and Con‐bFFE.

Conclusions

The CS‐bFFE improves image quality and reduces acquisition time significantly, and it is a feasible MRI protocol for inner ear imaging.