k-Space Deep Learning for Reference-free EPI Ghost Correction

Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. To take advantage of the even and odd-phase directional redundancy, the k-space data is divided into two channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi-coil k-space data into additional input channels. Then, our k-space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k-space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k-space data from both ghost and subsampling. Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster.The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin

Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion

Diffusion models have shown superior performance in image generation and manipulation, but the inherent stochasticity presents challenges in preserving and manipulating image content and identity. While previous approaches like DreamBooth and Textual Inversion have proposed model or latent representation personalization to maintain the content, their reliance on multiple reference images and complex training limits their practicality. In this paper, we present a simple yet highly effective approach to personalization using highly personalized (HiPer) text embedding by decomposing the CLIP embedding space for personalization and content manipulation. Our method does not require model fine-tuning or identifiers, yet still enables manipulation of background, texture, and motion with just a single image and target text. Through experiments on diverse target texts, we demonstrate that our approach produces highly personalized and complex semantic image edits across a wide range of tasks. We believe that the novel understanding of the text embedding space presented in this work has the potential to inspire further research across various tasks

Uterine Artery Doppler Velocimetry During Mid-second Trimester to Predict Complications of Pregnancy Based on Unilateral or Bilateral Abnormalities

We performed this study to evaluate uterine artery Doppler velocimetry (UADV) measurement of unilateral or bilateral abnormalities as a predictor of complications in pregnancy during the mid-second trimester (20-24 weeks). We enrolled 1,090 pregnant women who had undergone UADV twice: once between the 20th and 24th week (1st stage) and again between the 28th and 32nd week (2nd stage) of pregnancy, and then delivered at Yonsei Medical Center. UADV was performed bilaterally. Follow-up UADV was performed between the 28th and 32nd week, and the frequencies of pregnancy-induced hypertension (PIH), fetal growth restriction (FGR), and preterm delivery (before 34 weeks of gestation) were determined. Chi-squared and t-tests were used where appropriate, with p < .05 considered significant. According to the results of UADV performed between 20-24 weeks of gestation, 825 women (75.7%) were included in the normal group, 196 (18.0%) in the unilateral abnormality group, and 69 (6.3%) in the bilateral abnormality group. The incidences of FGR were 8.0%, 10.2%, and 26.1%, and the incidences of PIH were 0.1%, 3.6%, and 14.5%, respectively. The incidence of PIH was significantly lower in the normal group. The incidences of preterm delivery were 2.2%, 5.6%, and 8.7%, respectively. PIH developed in 46.7% of patients with bilateral abnormal findings in both the 1st and 2nd stage tests, and developed in none of the patients with normal findings in both tests. Abnormal results found by UADV performed between the 20-24th weeks of pregnancy, such as high S/D ratios regardless of placental location and the presence of an early diastolic notch, were associated with significant increases in the incidences of intrauterine growth restriction (IUGR) and PIH. This was true for both bilateral and unilateral abnormalities. Abnormal findings in bilateral UADV during the second trimester especially warrant close follow up for the detection of subsequent development of pregnancy complications