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.

Reconstructing high-quality images from corrupted measurements remains a fundamental challenge in medical imaging. Recently, deep unrolling (DUN) methods have emerged as a promising solution, combining the interpretability of traditional iterative algorithms with the powerful representation capabilities of deep learning. However, their performance is often limited by weak information flow between iterative stages and a constrained ability to capture global features across stages-limitations that tend to worsen as the number of iterations increases.
Approach: In this work, we propose a memory-enhanced and multi-domain learning-based deep unrolling network for interpretable, high-fidelity medical image reconstruction. First, a memory-enhanced module is designed to adaptively integrate historical outputs across stages, reducing information loss. Second, we introduce a cross-stage spatial-domain learning transformer (CS-SLFormer) to extract both local and non-local features within and across stages, improving reconstruction performance. Finally, a frequency-domain consistency learning (FDCL) module ensures alignment between reconstructed and ground truth images in the frequency domain, recovering fine image details.
Main Results: Comprehensive experiments evaluated on three representative medical imaging modalities (PET, MRI, and CT) show that the proposed method consistently outperforms state-of-the-art (SOTA) approaches in both quantitative metrics and visual quality. Specifically, our method achieved a PSNR of 37.835 dB and an SSIM of 0.970 in 1 $\%$ dose PET reconstruction.
Significance: This study expands the use of model-driven deep learning in medical imaging, demonstrating the potential of memory-enhanced deep unrolling frameworks for high-quality reconstructions.

This study aimed to explore whether an artificial intelligence iterative reconstruction (AIIR) algorithm combined with low-dose aortic computed tomography angiography (CTA) demonstrates clinical effectiveness in assessing preoperative access for transcatheter aortic valve implantation (TAVI). A total of 109 patients were prospectively recruited for aortic CTA scans and divided into two groups: group A (n = 51) with standard-dose CT examinations (SDCT) and group B (n = 58) with low-dose CT examinations (LDCT). Group B was further subdivided into groups B1 and B2. Groups A and B2 used the hybrid iterative algorithm (HIR: Karl 3D), whereas Group B1 used the AIIR algorithm. CT attenuation and noise of different vessel segments were measured, and the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated. Two radiologists, who were blinded to the study details, rated the subjective image quality on a 5-point scale. The effective radiation doses were also recorded for groups A and B. Group B1 demonstrated the highest CT attenuation, SNR, and CNR and the lowest image noise among the three groups (p < 0.05). The scores of subjective image noise, vessel and non-calcified plaque edge sharpness, and overall image quality in Group B1 were higher than those in groups A and B2 (p < 0.001). Group B2 had the highest artifacts scores compared with groups A and B1 (p < 0.05). The radiation dose in group B was reduced by 50.33% compared with that in group A (p < 0.001). The AIIR algorithm combined with low-dose CTA yielded better diagnostic images before TAVI than the Karl 3D algorithm.

Magnetic resonance imaging (MRI) typically utilizes multiple contrasts to assess different tissue features, but prolonged scanning increases the risk of motion artifacts. Compressive sensing MRI (CS-MRI) employs computational reconstruction algorithm to accelerate imaging. Full-sampled auxiliary MR images can effectively assist in the reconstruction of under-sampled target MR images. However, due to spatial offset and differences in imaging parameters, how to achieve cross-modal fusion is a key issue. In order to cope with this issue, we propose an end-to-end network integrating spatial registration and cascaded incremental reconstruction for multi-modal CS-MRI. Specifically, the proposed network comprises two stages: a coarse-to-fine spatial registration sub-network and a cascaded incremental reconstruction sub-network. The registration sub-network iteratively predicts deformation flow fields between under-sampled target images and full-sampled auxiliary images, gradually aligning them to mitigate spatial offsets. The cascaded incremental reconstruction sub-network adopts a new separated criss-cross window Transformer as the basic component and deploys them in dual-path to fuse inter-modal and intra-modal features from the registered auxiliary images and under-sampled target images. Through cascade learning, we can recover incremental details from fused features and continuously refine the target images. We validate our model using the IXI brain dataset, and the experimental results demonstrate that, compared to existing methods, our network exhibits superior performance.

To propose and evaluate an accelerated <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> quantification method that combines <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> -weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep learning models for <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment. This retrospective study utilized MRI data from 40 participants (30 OA patients and 10 healthy volunteers). A volume of PD-weighted anatomical FSE images and a volume of <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> -weighted images acquired at a non-zero spin-lock time were used as input to train deep learning models, including a 2D U-Net and a multi-layer perceptron (MLP). <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> maps generated by these models were compared with ground truth maps derived from a traditional non-linear least squares (NLLS) fitting method using four <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> -weighted images. Evaluation metrics included mean absolute error (MAE), mean absolute percentage error (MAPE), regional error (RE), and regional percentage error (RPE). The best-performed deep learning models achieved RPEs below 5% across all evaluated scenarios. This performance was consistent even in reduced acquisition settings that included only one PD-weighted image and one <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> -weighted image, where NLLS methods cannot be applied. Furthermore, the results were comparable to those obtained with NLLS when longer acquisitions with four <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> -weighted images were used. The proposed approach enables efficient <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn> <mi>ρ</mi></mrow> </msub> </mrow> <annotation>$$ {T}_{1\rho } $$</annotation></semantics> </math> mapping using PD-weighted anatomical images, reducing scan time while maintaining clinical standards. This method has the potential to facilitate the integration of quantitative MRI techniques into routine clinical practice, benefiting OA diagnosis and monitoring.

In pelvic MRI, Turbo Spin Echo (TSE) pulse sequences are used for T2-weighted imaging. However, its lengthy acquisition time increases the potential for artifacts. Deep learning (DL) reconstruction achieves reduced scan times without the degradation in image quality associated with other accelerated techniques. Unfortunately, a comprehensive assessment of DL-reconstruction in pelvic MRI has not been performed. The objective of this prospective study was to compare the performance of DL-TSE and conventional TSE pulse sequences in a broad spectrum of pelvic MRI indications. Fifty-five subjects (33 females and 22 males) were scanned at 3 T using DL-TSE and conventional TSE sequences in axial and/or oblique acquisition planes. Two radiologists independently assessed image quality in 6 categories: edge definition, vessel margin sharpness, T2 Contrast Dynamic Range, artifacts, overall image quality, and lesion features. The contrast ratio was calculated for quantitative assessment. A two-tailed sign test was used for assessment. The 2 readers found DL-TSE to deliver equal or superior image quality than conventional TSE in most cases. There were only 3 instances out of 24 where conventional TSE was scored as providing better image quality. Readers agreed on DL-TSE superiority/inferiority/equivalence in 67% of categories in the axial plane and 75% in the oblique plane. DL-TSE also demonstrated a better contrast ratio in 75% of cases. DL-TSE reduced scan time by approximately 50%. DL-accelerated TSE sequences generally provide equal or better image quality in pelvic MRI than standard TSE with significantly reduced acquisition times.

Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resolve Boost (DRB)-based reconstruction techniques of a single-shot echo-planar imaging (ssEPI) diffusion-weighted imaging (DWI) on image quality features in cerebral 3 T brain MRI and compared it with a state-of-the-art DWI sequence. In this prospective study, 24 patients received a standard of care ssEPI DWI and 5 additional adapted ssEPI DWI sequences, 3 of those with DRB reconstruction. Qualitative analysis encompassed rating of image quality, noise, sharpness, and artifacts. Quantitative analysis compared apparent diffusion coefficient (ADC) values region-wise between the different DWI sequences. Intraclass correlations, paired sampled t test, Wilcoxon signed rank test, and weighted Cohen κ were used. Compared with the reference standard, the acquisition time was significantly improved in accelerated DWI from 75 seconds up to 50% (39 seconds; P < 0.001). All tested DRB-reconstructed sequences showed significantly improved image quality, sharpness, and reduced noise ( P < 0.001). Highest image quality was observed for the combination of conventional acceleration and DL reconstruction. In singular slices, more artifacts were observed for DRB-reconstructed sequences ( P < 0.001). While in general high consistency was found between ADC values, increasing differences in ADC values were noted with increasing acceleration and application of DRB. Falsely pathological ADCs were rarely observed near frontal poles and optic chiasm attributable to susceptibility-related artifacts due to adjacent sinuses. In this comparative study, we found that the combination of conventional acceleration and DRB reconstruction improves image quality and enables faster acquisition of ssEPI DWI. Nevertheless, a tradeoff between increased acceleration with risk of stronger artifacts and high-resolution with longer acquisition time needs to be considered, especially for application in cerebral MRI.

This study explored the effects of using Intelligent Quick Magnetic Resonance (IQMR) image post-processing on image quality in Field of View Optimized and Constrained Single-Shot Diffusion-Weighted Imaging (FOCUS-DWI) sequences for prostate cancer detection, and assessed its efficacy in distinguishing malignant from benign lesions. The clinical data and MRI images from 62 patients with prostate masses (31 benign and 31 malignant) were retrospectively analyzed. Axial T2-weighted imaging with fat saturation (T2WI-FS) and FOCUS-DWI sequences were acquired, and the FOCUS-DWI images were processed using the IQMR post-processing system to generate IQMR-FOCUS-DWI images. Two independent radiologists undertook subjective scoring, grading using the Prostate Imaging Reporting and Data System (PI-RADS), diagnosis of benign and malignant lesions, and diagnostic confidence scoring for images from the FOCUS-DWI and IQMR-FOCUS-DWI sequences. Additionally, quantitative analyses, specifically, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), were conducted using T2WI-FS as the reference standard. The apparent diffusion coefficients (ADCs) of malignant and benign lesions were compared between the two imaging sequences. Spearman correlation coefficients were calculated to evaluate the associations between diagnostic confidence scores and diagnostic accuracy rates of the two sequence groups, as well as between the ADC values of malignant lesions and Gleason grading in the two sequence groups. Receiver operating characteristic (ROC) curves were utilized to assess the efficacy of ADC in distinguishing lesions. The qualitative analysis revealed that IQMR-FOCUS-DWI images showed significantly better noise suppression, reduced geometric distortion, and enhanced overall quality relative to the FOCUS-DWI images (P < 0.001). There was no significant difference in the PI-RADS scores between IQMR-FOCUS-DWI and FOCUS-DWI images (P = 0.0875), while the diagnostic confidence scores of IQMR-FOCUS-DWI sequences were markedly higher than those of FOCUS-DWI sequences (P = 0.0002). The diagnostic results of the FOCUS-DWI sequences for benign and malignant prostate lesions were consistent with those of the pathological results (P < 0.05), as were those of the IQMR-FOCUS-DWI sequences (P < 0.05). The quantitative analysis indicated that the PSNR, SSIM, and ADC values were markedly greater in IQMR-FOCUS-DWI images relative to FOCUS-DWI images (P < 0.01). In both imaging sequences, benign lesions exhibited ADC values markedly greater than those of malignant lesions (P < 0.001). The diagnostic confidence scores of both groups of sequences were significantly positively correlated with the diagnostic accuracy rate. In malignant lesions, the ADC values of the FOCUS-DWI sequences showed moderate negative correlations with the Gleason grading, while the ADC values of the IQMR-FOCUS-DWI sequences were strongly negatively associated with the Gleason grading. ROC curves indicated the superior diagnostic performance of IQMR-FOCUS-DWI (AUC = 0.941) compared to FOCUS-DWI (AUC = 0.832) for differentiating prostate lesions (P = 0.0487). IQMR-FOCUS-DWI significantly enhances image quality and improves diagnostic accuracy for benign and malignant prostate lesions compared to conventional FOCUS-DWI.

This study aimed to optimize the imaging time and image quality of T2WI-FS through the integration of Artificial Intelligence-Assisted Compressed Sensing (ACS) and respiratory triggering (RT). A prospective cohort study was conducted on one hundred thirty-four patients (99 males, 35 females; average age: 57.93 ± 9.40 years) undergoing liver MRI between March and July 2024. All patients were scanned using both breath-hold ACS-assisted T2WI (BH-ACS-T2WI) and respiratory-triggered ACS-assisted T2WI (RT-ACS-T2WI) sequences. Two experienced radiologists retrospectively analyzed regions of interest (ROIs), recorded primary lesions, and assessed key metrics including signal intensity (SI), standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), motion artifacts, hepatic vessel clarity, liver edge sharpness, lesion conspicuity, and overall image quality. Statistical comparisons were conducted using Mann-Whitney U test, Wilcoxon signed-rank test and intraclass correlation coefficient (ICC). Compared to BH-ACS-T2WI, RT-ACS-T2WI significantly reduced average imaging time from 38 s to 22.91 ± 3.36 s, achieving a 40 % reduction in scan duration. Additionally, RT-ACS-T2WI demonstrated superior performance across multiple parameters, including SI, SD, SNR, CNR, motion artifact reduction, hepatic vessel clarity, liver edge sharpness, lesion conspicuity (≤5 mm), and overall image quality (P < 0.05). Notably, the lesion detection rate was slightly higher with RT-ACS-T2WI (94 %) compared to BH-ACS-T2WI (90 %). The RT-ACS-T2WI sequence not only enhanced image quality but also reduced imaging time to approximately 23 s, making it particularly beneficial for patients unable to perform prolonged breath-holding maneuvers. This approach represents a promising advancement in optimizing liver MRI protocols.

The circulation of cerebrospinal and interstitial fluid plays a vital role in clearing metabolic waste from the brain, and its disruption has been linked to neurological disorders. However, directly measuring brain-wide fluid transport-especially in the deep brain-has remained elusive. Here, we introduce magnetic resonance artificial intelligence velocimetry (MR-AIV), a framework featuring a specialized physics-informed architecture and optimization method that reconstructs three-dimensional fluid velocity fields from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MR-AIV unveils brain-wide velocity maps while providing estimates of tissue permeability and pressure fields-quantities inaccessible to other methods. Applied to the brain, MR-AIV reveals a functional landscape of interstitial and perivascular flow, quantitatively distinguishing slow diffusion-driven transport (∼ 0.1 µm/s) from rapid advective flow (∼ 3 µm/s). This approach enables new investigations into brain clearance mechanisms and fluid dynamics in health and disease, with broad potential applications to other porous media systems, from geophysics to tissue mechanics.