Magnetic susceptibility differences at the heart-lung interface introduce B₀-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B₀-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B₀ shimming pipeline to mitigate them. A motion-resolved B₀ mapping sequence was implemented at 3 T to quantify cardiac and respiratory-induced B₀ variations. A motion-adaptive shimming framework was then developed and validated through numerical simulations and human imaging studies. B₀-field homogeneity and T₂* mapping accuracy were assessed in multiple breath-hold positions using standard and motion-adaptive shimming. Respiratory motion significantly altered myocardial B₀ fields (p < 0.01), whereas cardiac motion had minimal impact (p = 0.49). Compared with conventional scanner shimming, motion-adaptive B₀ shimming yielded significantly improved field uniformity across both inspiratory (post-shim SD_ratio: 0.68 ± 0.10 vs. 0.89 ± 0.11; p < 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p < 0.05) breath-hold states. Corresponding improvements in myocardial T₂* map homogeneity were observed, with reduced coefficient of variation (0.44 ± 0.19 vs. 0.39 ± 0.22; 0.59 ± 0.30 vs. 0.46 ± 0.21; both p < 0.01). The proposed motion-adaptive B₀ shimming approach effectively compensates for respiration-induced B₀ fluctuations, enhancing field homogeneity and reducing off-resonance artifacts. This strategy improves the robustness and reproducibility of T₂* mapping, enabling more reliable high-field cardiac MRI.

Neuromodulation of the centromedian nucleus (CM) of the thalamus has shown promise in treating refractory epilepsy, particularly for idiopathic generalized epilepsy and Lennox-Gastaut syndrome. However, precise targeting of CM remains challenging. The combination of deep learning reconstruction (DLR) and fast gray matter acquisition T1 inversion recovery (FGATIR) offers potential improvements in visualization of CM for deep brain stimulation (DBS) targeting. The goal of the study was to evaluate the visualization of the putative CM on DLR-FGATIR and its alignment with atlas-defined CM boundaries, with the aim of facilitating direct targeting of CM for neuromodulation. This retrospective study included 12 patients with drug-resistant epilepsy treated with thalamic neuromodulation by using DLR-FGATIR for direct targeting. Postcontrast-T1-weighted MRI, DLR-FGATIR, and postoperative CT were coregistered and normalized into Montreal Neurological Institute (MNI) space and compared with the Morel histologic atlas. Contrast-to-noise ratios were measured between CM and neighboring nuclei. CM segmentations were compared between an experienced rater, a trainee rater, the Morel atlas, and the Thalamus Optimized Multi Atlas Segmentation (THOMAS) atlas (derived from expert segmentation of high-field MRI) by using the Sorenson-Dice coefficient (Dice score, a measure of overlap) and volume ratios. The number of electrode contacts within the Morel atlas CM was assessed. On DLR-FGATIR, CM was visible as an ovoid hypointensity in the intralaminar thalamus. Contrast-to-noise ratios were highest (<i>P</i> < .001) for the mediodorsal and medial pulvinar nuclei. Dice score with the Morel atlas CM was higher (median 0.49, interquartile range 0.40-0.58) for the experienced rater (<i>P</i> < .001) than the trainee rater (0.32, 0.19-0.46) and no different (<i>P</i> = .32) than the THOMAS atlas CM (0.56, 0.55-0.58). Both raters and the THOMAS atlas tended to under-segment the lateral portion of the Morel atlas CM, reflected by smaller segmentation volumes (<i>P</i> < .001). All electrodes targeting CM based on DLR-FGATIR traversed the Morel atlas CM. DLR-FGATIR permitted visualization and delineation of CM commensurate with a group atlas derived from high-field MRI. This technique provided reliable guidance for accurate electrode placement within CM, highlighting its potential use for direct targeting.

Reduced signal-to-noise ratio (SNR) in hyperpolarized ¹²⁹Xe MR images can affect accurate quantification for research and diagnostic evaluations. Thus, this study explores the application of supervised deep learning (DL) denoising, traditional (Trad) and Noise2Noise (N2N) and unsupervised Noise2void (N2V) approaches for ¹²⁹Xe MR imaging. The DL denoising frameworks were trained and tested on 952 ¹²⁹Xe MRI data sets (421 ventilation, 125 diffusion-weighted, and 406 gas-exchange acquisitions) from healthy subjects and participants with cardiopulmonary conditions and compared with the block matching 3D denoising technique. Evaluation involved mean signal, noise standard deviation (SD), SNR, and sharpness. Ventilation defect percentage (VDP), apparent diffusion coefficient (ADC), membrane uptake, red blood cell (RBC) transfer, and RBC:Membrane were also evaluated for ventilation, diffusion, and gas-exchange images, respectively. Denoising methods significantly reduced noise SDs and enhanced SNR (p < 0.05) across all imaging types. Traditional ventilation model (Trad_vent) improved sharpness in ventilation images but underestimated VDP (bias = -1.37%) relative to raw images, whereas N2N_vent overestimated VDP (bias = +1.88%). Block matching 3D and N2V_vent showed minimal VDP bias (≤ 0.35%). Denoising significantly reduced ADC mean and SD (p < 0.05, bias ≤ - 0.63 × 10^-2). The values of Trad_vent and N2N_vent increased mean membrane and RBC (p < 0.001) with no change in RBC:Membrane. Denoising also reduced SDs of all gas-exchange metrics (p < 0.01). Low SNR may impair the potential of ¹²⁹Xe MRI for clinical diagnosis and lung function assessment. The evaluation of supervised and unsupervised DL denoising methods enhanced ¹²⁹Xe imaging quality, offering promise for improved clinical interpretation and diagnosis.

Quantitative magnetic resonance imaging (qMRI) has attracted more and more attention in clinical diagnosis and medical sciences due to its capability to non-invasively characterize tissue properties. Nevertheless, most qMRI methods are time-consuming and sensitive to motion, making them inadequate for quantifying organs with physiological movement. In this context, single-shot multiple overlapping-echo detachment (MOLED) imaging technique has been presented, but its acquisition efficiency and image quality are limited when the field of view (FOV) is smaller than the object, especially for abdominal organs and myocardium. A novel single-shot reduced FOV qMRI method was developed based on MOLED (termed rFOV-MOLED). This method combines zonal oblique multislice (ZOOM) and outer volume suppression (OVS) techniques to reduce the FOV and suppress signals outside the FOV. A deep neural network was trained using synthetic data generated from Bloch simulations to achieve high-quality T₂ map reconstruction from rFOV-MOLED iamges. Numerical simulation, water phantom and in vivo abdominal and myocardial imaging experiments were performed to evaluate the method. The coefficient of variation and repeatability index were used to evaluate the reproducibility. Multiple statistical analyses were utilized to evaluate the accuracy and significance of the method, including linear regression, Bland-Altman analysis, Wilcoxon signed-rank test, and Mann-Whitney U test, with the p-value significance level of 0.05. Experimental results show that rFOV-MOLED achieved excellent performance in reducing aliasing signals due to FOV reduction. It provided T₂ maps closely resembling the reference maps. Moreover, it gave finer tissue details than MOLED and was quite repeatable. rFOV-MOLED can ultrafast and stably provide accurate T2 maps for myocardium and specific abdominal organs with improved acquisition efficiency and image quality.

Ultrasound imaging systems rely on accurate point spread function (PSF) estimation to support advanced image quality enhancement techniques such as deconvolution and speckle reduction. Phase aberration, caused by sound speed inhomogeneity within biological tissue, is inevitable in ultrasound imaging. It distorts the PSF by increasing sidelobe level and introducing asymmetric amplitude, making PSF estimation under phase aberration highly challenging. In this work, we propose a deep learning framework for estimating phase-aberrated PSFs using U-Net and complex U-Net architectures, operating on RF and complex k-space data, respectively, with the latter demonstrating superior performance. Synthetic phase aberration data, generated using the near-field phase screen model, is employed to train the networks. We evaluate various loss functions and find that log-compressed B-mode perceptual loss achieves the best performance, accurately predicting both the mainlobe and near sidelobe regions of the PSF. Simulation results validate the effectiveness of our approach in estimating PSFs under varying levels of phase aberration. Furthermore, we demonstrate that more accurate PSF estimation improves performance in a downstream phase aberration correction task, highlighting the broader utility of the proposed method.

Passive acoustic mapping (PAM) is a promising tool for monitoring acoustic cavitation activities in the applications of ultrasound therapy. Data-adaptive beamformers for PAM have better image quality compared with time exposure acoustics (TEA) algorithms. However, the computational cost of data-adaptive beamformers is considerably expensive. In this work, we develop a deep beamformer based on a generative adversarial network that can switch between different transducer arrays and reconstruct high-quality PAM images directly from radiofrequency ultrasound signals with low computational cost. The deep beamformer was trained on a dataset consisting of simulated and experimental cavitation signals of single and multiple microbubble clouds measured by different (linear and phased) arrays covering 1-15 MHz. We compared the performance of the deep beamformer to TEA and three different data-adaptive beamformers using simulated and experimental test dataset. Compared with TEA, the deep beamformer reduced the energy spread area by 27.3%-77.8% and improved the image signal-to-noise ratio by 13.9-25.1 dB on average for the different arrays in our data. Compared with the data-adaptive beamformers, the deep beamformer reduced the computational cost by three orders of magnitude achieving 10.5 ms image reconstruction speed in our data, while the image quality was as good as that of the data-adaptive beamformers. These results demonstrate the potential of the deep beamformer for high-resolution monitoring of microbubble cavitation activities for ultrasound therapy.

This clinically oriented review explores the technical advancements of simultaneous PET/magnetic resonance (MR) imaging to provide an overview of the addressed obstacles over time, current challenges, and future trends in the field. In particular, advanced attenuation and motion correction techniques and MR-guided PET reconstruction frameworks were reviewed, and the state-of-the-art PET/MR systems and their strengths were discussed. Overall, PET/MR holds great potential in various clinical applications, including oncology, neurology, and cardiology. However, it requires continued optimization in hardware, algorithms, and clinical protocols to achieve broader adoption and be included in the routine clinical standards.

To compare the lesion detectability of hypovascular liver metastases between 70-keV and 40-keV images from dual energy-computed tomography (CT) reconstructed with deep-learning image reconstruction (DLIR). This multi-institutional, retrospective study included adult patients both pre- and post-treatment for gastrointestinal adenocarcinoma. All patients underwent contrast-enhanced CT with reconstruction at 40-keV and 70-keV. Liver metastases were confirmed using gadoxetic acid-enhanced magnetic resonance imaging. Four radiologists independently assessed lesion conspicuity (per-patient and per-lesion) using a 5-point scale. A radiologic technologist measured image noise, tumor-to-liver contrast, and contrast-to-noise ratio (CNR). Quantitative and qualitative results were compared between 70-keV and 40-keV images. The study included 138 patients (mean age, 69 ± 12 years; 80 men) with 208 liver metastases. Seventy-one patients had liver metastases, while 67 did not. Primary cancer sites included 68 cases of pancreas, 50 colorectal, 12 stomach, and 8 gallbladder/bile duct. No significant difference in per-patient lesion detectability was found between 70-keV images (sensitivity, 71.8-90.1%; specificity, 61.2-85.1%; accuracy, 73.9-79.7%) and 40-keV images (sensitivity, 76.1-90.1%; specificity, 53.7-82.1%; accuracy, 71.7-79.0%) (p = 0.18-> 0.99). Similarly, no significant difference in per-lesion lesion detectability was observed between 70-keV (sensitivity, 67.3-82.2%) and 40-keV images (sensitivity, 68.8-81.7%) (p = 0.20-> 0.99). However, Image noise was significantly higher at 40 keV, along with greater tumor-to-liver contrast and CNRs for both hepatic parenchyma and tumors (p < 0.01). There was no significant difference in hypovascular liver metastases detectability between 70-keV and 40-keV images using the DLIR technology.

To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) with motion reduction algorithm (SR-DLR-M) in mitigating aorta motion artifacts compared to SR-DLR and deep learning reconstruction with motion reduction algorithm (DLR-M). This retrospective study included 86 patients (mean age, 65.0 ± 14.1 years; 53 males) who underwent contrast-enhanced CT including the chest region. CT images were reconstructed with SR-DLR-M, SR-DLR, and DLR-M. Circular or ovoid regions of interest were placed on the aorta, and the standard deviation of the CT attenuation was recorded as quantitative noise. From the CT attenuation profile along a line region of interest that intersected the left common carotid artery wall, edge rise slope and edge rise distance were calculated. Two readers assessed the images based on artifact, sharpness, noise, structure depiction, and diagnostic acceptability (for aortic dissection). Quantitative noise was 7.4/5.4/8.3 Hounsfield unit (HU) in SR-DLR-M/SR-DLR/DLR-M. Significant differences were observed between SR-DLR-M vs. SR-DLR and DLR-M (p < 0.001). Edge rise slope and edge rise distance were 107.1/108.8/85.8 HU/mm and 1.6/1.5/2.0 mm, respectively, in SR-DLR-M/SR-DLR/DLR-M. Statistically significant differences were detected between SR-DLR-M vs. DLR-M (p ≤ 0.001 for both). Two readers scored artifacts in SR-DLR-M as significantly better than those in SR-DLR (p < 0.001). Scores for sharpness, noise, and structure depiction in SR-DLR-M were significantly better than those in DLR-M (p ≤ 0.005). Diagnostic acceptability in SR-DLR-M was significantly better than that in SR-DLR and DLR-M (p < 0.001). SR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.

Computed tomography (CT) is one of the most widely used non-invasive imaging modalities for medical diagnosis. In clinical practice, CT images are usually acquired with large slice thicknesses due to the high cost of memory storage and operation time, resulting in an anisotropic CT volume with much lower inter-slice resolution than in-plane resolution. Since such inconsistent resolution may lead to difficulties in disease diagnosis, deep learning-based volumetric super-resolution methods have been developed to improve inter-slice resolution. Most existing methods conduct single-image super-resolution on the through-plane or synthesize intermediate slices from adjacent slices; however, the anisotropic characteristic of 3D CT volume has not been well explored. In this paper, we propose a novel cross-view texture transfer approach for CT slice interpolation by fully utilizing the anisotropic nature of 3D CT volume. Specifically, we design a unique framework that takes high-resolution in-plane texture details as a reference and transfers them to low-resolution through-plane images. To this end, we introduce a multi-reference non-local attention module that extracts meaningful features for reconstructing through-plane high-frequency details from multiple in-plane images. Through extensive experiments, we demonstrate that our method performs significantly better in CT slice interpolation than existing competing methods on public CT datasets including a real-paired benchmark, verifying the effectiveness of the proposed framework. The source code of this work is available at https://github.com/khuhm/ACVTT.