Deep Learning-reconstructed Parallel Accelerated Imaging for Knee MRI

BACKGROUND: Deep learning (DL) can improve image quality by removing noise from accelerated MRI. OBJECTIVE: To compare the quality of various accelerated imaging applications in knee MRI with and without DL. METHOD: We analyzed 44 knee MRI scans from 38 adult patients using the DL-reconstructed parallel acquisition technique (PAT) between May 2021 and April 2022. The participants underwent sagittal fat-saturated T2-weighted turbo-spin-echo accelerated imaging without DL (PAT-2 [2-fold parallel accelerated imaging], PAT-3, and PAT-4) and with DL (DL with PAT-3 [PAT-3DL] and PAT-4 [PAT-4DL]). Two readers independently evaluated subjective image quality (diagnostic confidence of knee joint abnormalities, subjective noise and sharpness, and overall image quality) using a 4-point grading system (1-4, 4=best). Objective image quality was assessed based on noise (noise power) and sharpness (edge rise distance). RESULTS: The mean acquisition times for PAT-2, PAT-3, PAT-4, PAT-3DL, and PAT-4DL sequences were 2:55, 2:04, 1:33, 2:04, and 1:33 min, respectively. Regarding subjective image quality, PAT-3DL and PAT-4DL scored higher than PAT-2. Objectively, DL-reconstructed imaging had significantly lower noise than PAT-3 and PAT-4 (P <0.001), but the results were not significantly different from those for PAT-2 (P >0.988). Objective image sharpness did not differ significantly among the imaging combinations (P =0.470). The inter-reader reliability ranged from good to excellent (κ = 0.761­0.832). CONCLUSION: PAT-4DL imaging in knee MRI exhibits similar subjective image quality, objective noise, and sharpness levels compared with conventional PAT-2 imaging, with an acquisition time reduction of 47%.

Transcriptomic Profiling Analysis of Castration-Resistant Prostate Cancer Cell Lines Treated with Chronic Intermittent Hypoxia.

Castration-resistant prostate cancer (CRPC) is still a major concern in men's health, with 375,000 cancer deaths annually. Hypoxia, which is a marked characteristic of advanced solid tumors, has been suggested to induce prostate cancer towards CRPC, metastasis and treatment resistance. To evaluate the effect of hypoxia on prostate cancer, two and five cycles of hypoxia and reoxygenation were administered using 22Rv1 cell lines and denominated as 22Rv1-CI and 22Rv1-PCI, respectively. Cancer cell migration was promoted in 22Rv1-CI compared to controls, and the expression of COL13A1 was significantly up-regulated in 22Rv1-CI according to differentially expressed gene analysis of RNA sequencing among groups. Cancer cell migration was impeded in a wound healing assay after transfecting si-COL13A1. Moreover, the expression of COL13A1 was also higher in the cell line originating from bone metastatic prostate cancer compared to other cell lines. Using the open database GEO, we also confirmed that the expression of COL13A1 was higher in bone metastatic prostate cancer tissue than in localized prostate cancer tissue in patients. Therefore, COL13A1 may be closely related to the bony metastasis of prostate cancer, and our findings may provide valuable information on the pathophysiology of the metastatic niche induced by hypoxia in patients with CRPC.

Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI.

OBJECTIVES: This study aimed to examine various combinations of parallel imaging (PI) and simultaneous multislice (SMS) acceleration imaging using deep learning (DL)-enhanced and conventional reconstruction. The study also aimed at comparing the diagnostic performance of the various combinations in internal knee derangement and provided a quantitative evaluation of image sharpness and noise using edge rise distance (ERD) and noise power (NP), respectively. MATERIALS AND METHODS: The data from adult patients who underwent knee magnetic resonance imaging using various DL-enhanced acquisitions between June 2021 and January 2022 were retrospectively analyzed. The participants underwent conventional 2-fold PI and DL protocols with 4- to 8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS], P3S2, and P4S2). Three readers evaluated the internal knee derangement and the overall image quality. The diagnostic performance was calculated using consensus reading as a standard reference, and we conducted comparative evaluations. We calculated the ERD and NP for quantitative evaluations of image sharpness and noise, respectively. Interreader and intermethod agreements were calculated using Fleiss κ. RESULTS: A total of 33 patients (mean age, 49 ± 19 years; 20 women) were included in this study. The diagnostic performance for internal knee derangement and the overall image quality were similar among the evaluated protocols. The NP values were significantly lower using the DL protocols than with conventional imaging ( P < 0.001), whereas the ERD values were similar among these methods ( P > 0.12). Interreader and intermethod agreements were moderate-to-excellent (κ = 0.574-0.838) and good-to-excellent (κ = 0.755-1.000), respectively. In addition, the mean acquisition time was reduced by 47% when using DL with P2S2, by 62% with P3S2, and by 71% with P4S2, compared with conventional P2 imaging (2 minutes and 55 seconds). CONCLUSIONS: The combined use of DL-enhanced 8-fold acceleration imaging (4-fold PI with 2-fold SMS) showed comparable performance with conventional 2-fold PI for the evaluation of internal knee derangement, with a 71% reduction in acquisition time.

Verification of the performance of the Bispectral Index as a hypnotic depth indicator during dexmedetomidine sedation.

BACKGROUND: Differences in the effects of propofol and dexmedetomidine sedation on electroencephalogram patterns have been reported previously. However, the reliability of the Bispectral Index (BIS) value for assessing the sedation caused by dexmedetomidine remains debatable. The purpose of this study is to evaluate the correlation between the BIS value and the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) scale in patients sedated with dexmedetomidine. METHODS: Forty-two patients (age range, 20-80 years) who were scheduled for elective surgery under spinal anesthesia were enrolled in this study. Spinal anesthesia was performed using 0.5% bupivacaine, which was followed by dexmedetomidine infusion (loading dose, 0.5-1 µg/kg for 10 min; maintenance dose, 0.3-0.6 µg/kg/h). The MOAA/S score was used to evaluate the level of sedation, and the Vital Recorder program was used to collect data (vital signs and BIS values). RESULTS: A total of 215082 MOAA/S scores and BIS data pairs were analyzed. The baseline variability of the BIS value was 7.024%, and the decrease in the BIS value was associated with a decrease in the MOAA/S score. The correlation coefficient and prediction probability between the two measurements were 0.566 (P < 0.0001) and 0.636, respectively. The mean ± standard deviation values of the BIS were 87.22 ± 7.06, 75.85 ± 9.81, and 68.29 ± 12.65 when the MOAA/S scores were 5, 3, and 1, respectively. Furthermore, the cut-off BIS values in the receiver operating characteristic analysis at MOAA/S scores of 5, 3, and 1 were 82, 79, and 73, respectively. CONCLUSION: The BIS values were significantly correlated with the MOAA/S scores. Thus, the BIS along with the clinical sedation scale might prove useful in assessing the hypnotic depth of a patient during sedation with dexmedetomidine.

Prediction of bone mineral density in CT using deep learning with explainability.

Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score = - 1.0 ), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.

Machine-Learning-Guided Prediction Models of Critical Temperature of Cuprates.

Cuprates have been at the center of long debate regarding their superconducting mechanism; therefore, predicting the critical temperatures of cuprates remains elusive. Herein, using machine learning and first-principles calculations, we predict the maximum superconducting transition temperature (Tc,max) of hole-doped cuprates and suggest the functional form for Tc,max with the root-mean-square-error of 3.705 K and R2 of 0.969. We have found that the Bader charge of apical oxygen, the bond strength between apical atoms, and the number of superconducting layers are essential to estimate Tc,max. Furthermore, we predict the Tc,max of hypothetical cuprates generated by replacing apical cations with other elements. Among the hypothetical structures, the cuprates with Ga show the highest predicted Tc,max values, which are 71, 117, and 131 K for one, two, and three CuO2 layers, respectively. These findings suggest that machine learning could guide the design of new high-Tc superconductors in the future.

Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma.

PURPOSE: To evaluate apparent diffusion coefficient (ADC) histogram parameters that show correlations with prognostic factors and subtypes of breast cancer. MATERIALS AND METHODS: At 3.0T, various ADC histogram parameters were calculated including the entire tumor volume in 173 invasive ductal carcinomas: the minimum, 10th percentile, mean, median, 90th percentile, and maximum. ADC parameters were correlated with prognostic factors and subtype. RESULTS: The mean ADCmedian value was significantly higher in the group with lymph node metastasis, HER2 positivity, and a Ki-67 value <14% than in the group with negativity for lymph node metastasis, HER2 negativity, and a Ki-67 value ≥14% (0.907, 0.978, and 0.941 vs. 0.735, 0.778, and 0.761 × 10(-3) mm(2) /s, respectively) (P < 0.01). There was no significant correlation between ADCmedian and tumor size, histologic grade, estrogen receptor expression, and progesterone receptor expression (P = 0.272, 0.113, 0.261, and 0.181, respectively). For most ADC parameters except for ADCmin , the mean of variable ADC parameters of HER2-positive, luminal A, luminal B-HER2(+), triple-negative, and luminal B-HER2(-) diseases were arranged in descending order (1.175, 0.936, 0.863, 0.811, and 0.665 × 10(-3) mm(2) /s in ADCmedian , respectively) with statistical significant difference (P < 0.001). In multivariate analysis, histologic grade, the Ki-67 index, and HER2 expression were statistically significant explanatory prognostic factors for ADCmedian and the Ki-67 index had the most robust effects on ADC parameters (standardized coefficient = -0.317). CONCLUSION: Various ADC parameters were correlated with prognostic factors and subtype, except for ADCmin . HER2 positivity showed high ADC values and high Ki-67 index revealed low ADC values.