Traditional approaches for molecular imaging of Parkinson's disease (PD) in vivo require radioactive isotopes, lengthy scan times, or deliver only low spatial resolution. Recent advances in saturation transfer-based PD magnetic resonance imaging (MRI) have provided biochemical insights, although the image contrast is semi-quantitative and nonspecific. Here, we combined a rapid molecular MRI acquisition paradigm with deep learning based reconstruction for multi-metabolite quantification of glutamate, mobile proteins, semisolid, and mobile macromolecules in an acute MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) mouse model. The quantitative parameter maps are in general agreement with the histology and MR spectroscopy, and demonstrate that semisolid magnetization transfer (MT), amide, and aliphatic relayed nuclear Overhauser effect (rNOE) proton volume fractions may serve as PD biomarkers.

High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for precise clinical diagnosis and advancing our understanding of fetal brain development. This necessitates reliable slice-to-volume registration (SVR) for motion correction and super-resolution reconstruction (SRR) techniques. Traditional approaches have their limitations, but deep learning (DL) offers the potential in enhancing SVR and SRR. However, most of DL methods require large-scale external 3D high-resolution (HR) training datasets, which is challenging in clinical fetal MRI. To address this issue, we propose an unsupervised iterative joint SVR and SRR DL framework for 3D isotropic HR volume reconstruction. Specifically, our method conceptualizes SVR as a function that maps a 2D slice and a 3D target volume to a rigid transformation matrix, aligning the slice to the underlying location within the target volume. This function is parameterized by a convolutional neural network, which is trained by minimizing the difference between the volume slicing at the predicted position and the actual input slice. For SRR, a decoding network embedded within a deep image prior framework, coupled with a comprehensive image degradation model, is used to produce the HR volume. The deep image prior framework offers a local consistency prior to guide the reconstruction of HR volumes. By performing a forward degradation model, the HR volume is optimized by minimizing the loss between the predicted slices and the acquired slices. Experiments on both large-magnitude motion-corrupted simulation data and clinical data have shown that our proposed method outperforms current state-of-the-art fetal brain reconstruction methods. The source code is available at https://github.com/DeepBMI/SUFFICIENT.

Phase-sensitive inversion recovery late gadolinium enhancement (LGE) improves tissue contrast, however it is challenging to combine with a free-breathing acquisition. Deep-learning (DL) algorithms have growing applications in cardiac magnetic resonance imaging (CMR) to improve image quality. We compared a novel combination of a free-breathing single-shot phase-sensitive LGE with respiratory triggering (FB-PS) sequence with DL noise reduction reconstruction algorithm to a conventional segmented phase-sensitive LGE acquired during breath holding (BH-PS). 61 adult subjects (29 male, age 51 ± 15) underwent clinical CMR (1.5 T) with the FB-PS sequence and the conventional BH-PS sequence. DL noise reduction was incorporated into the image reconstruction pipeline. Qualitative metrics included image quality, artifact severity, diagnostic confidence. Quantitative metrics included septal-blood border sharpness, LGE sharpness, blood-myocardium apparent contrast-to-noise ratio (CNR), LGE-myocardium CNR, LGE apparent signal-to-noise ratio (SNR), and LGE burden. The sequences were compared via paired t-tests. 27 subjects had positive LGE. Average time to acquire a slice for FB-PS was 4-12 s versus ~32-38 s for BH-PS (including breath instructions and break time in between breath hold). FB-PS with medium DL noise reduction had better image quality (FB-PS 3.0 ± 0.7 vs. BH-PS 1.5 ± 0.6, p < 0.0001), less artifact (4.8 ± 0.5 vs. 3.4 ± 1.1, p < 0.0001), and higher diagnostic confidence (4.0 ± 0.6 vs. 2.6 ± 0.8, p < 0.0001). Septum sharpness in FB-PS with DL reconstruction versus BH-PS was not significantly different. There was no significant difference in LGE sharpness or LGE burden. FB-PS had superior blood-myocardium CNR (17.2 ± 6.9 vs. 16.4 ± 6.0, p = 0.040), LGE-myocardium CNR (12.1 ± 7.2 vs. 10.4 ± 6.6, p = 0.054), and LGE SNR (59.8 ± 26.8 vs. 31.2 ± 24.1, p < 0.001); these metrics further improved with DL noise reduction. A FB-PS sequence shortens scan time by over 5-fold and reduces motion artifact. Combined with a DL noise reduction algorithm, FB-PS provides better or similar image quality compared to BH-PS. This is a promising solution for patients who cannot hold their breath.

This study aims to evaluate the effectiveness of a deep learning (DL)-enhanced four-fold parallel acquisition technique (P4) in improving prostate MR image quality while optimizing scan efficiency compared to the traditional two-fold parallel acquisition technique (P2). Patients undergoing prostate MRI with DL-enhanced acquisitions were analyzed from January 2024 to July 2024. The participants prospectively received T2-weighted sequences in all imaging planes using both P2 and P4. Three independent readers assessed image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR). Significant differences in contrast and gray-level properties between P2 and P4 were identified through radiomics analysis (p <.05). A total of 51 participants (mean age 69.4 years ± 10.5 years) underwent P2 and P4 imaging. P4 demonstrated higher CNR and SNR values compared to P2 (p <.001). P4 was consistently rated superior to P2, demonstrating enhanced image quality and greater diagnostic precision across all evaluated categories (p <.001). Furthermore, radiomics analysis confirmed that P4 significantly altered structural and textural differentiation in comparison to P2. The P4 protocol reduced T2w scan times by 50.8%, from 11:48 min to 5:48 min (p <.001). In conclusion, P4 imaging enhances diagnostic quality and reduces scan times, improving workflow efficiency, and potentially contributing to a more patient-centered and sustainable radiology practice.

Photoacoustic computed tomography (PACT) is an emerging hybrid imaging modality with potential applications in biomedicine. A major roadblock to the widespread adoption of PACT is the limited number of detectors, which gives rise to spatial aliasing and manifests as streak artifacts in the reconstructed image. A brute-force solution to the problem is to increase the number of detectors, which, however, is often undesirable due to escalated costs. In this study, we present a novel self-supervised learning approach, to overcome this long-standing challenge. We found that small blocks of PACT channel data show similarity at various downsampling rates. Based on this observation, a neural network trained on downsampled data can reliably perform accurate interpolation without requiring densely-sampled ground truth data, which is typically unavailable in real practice. Our method has undergone validation through numerical simulations, controlled phantom experiments, as well as ex vivo and in vivo animal tests, across multiple PACT systems. We have demonstrated that our technique provides an effective and cost-efficient solution to address the under-sampling issue in PACT, thereby enhancing the capabilities of this imaging technology.

The exponential growth of medical imaging has created significant challenges in data storage, transmission, and management for healthcare systems. In this vein, efficient compression becomes increasingly important. Unlike natural image compression, medical image compression prioritizes preserving diagnostic details and structural integrity, imposing stricter quality requirements and demanding fast, memory-efficient algorithms that balance computational complexity with clinically acceptable reconstruction quality. Meanwhile, the medical imaging family includes a plethora of modalities, each possessing different requirements. For example, 2D medical image (e.g., X-rays, histopathological images) compression focuses on exploiting intra-slice spatial redundancy, while volumetric medical image faces require handling intra-slice and inter-slice spatial correlations, and 4D dynamic imaging (e.g., time-series CT/MRI, 4D ultrasound) additionally demands processing temporal correlations between consecutive time frames. Traditional compression methods, grounded in mathematical transforms and information theory principles, provide solid theoretical foundations, predictable performance, and high standardization levels, with extensive validation in clinical environments. In contrast, deep learning-based approaches demonstrate remarkable adaptive learning capabilities and can capture complex statistical characteristics and semantic information within medical images. This comprehensive survey establishes a two-facet taxonomy based on data structure (2D vs 3D/4D) and technical approaches (traditional vs learning-based), thereby systematically presenting the complete technological evolution, analyzing the unique technical challenges, and prospecting future directions in medical image compression.

Magnetic resonance imaging (MRI) is crucial in pediatric radiology; however, the prolonged scan time is a major drawback that often requires sedation. Deep learning reconstruction (DLR) is a promising method for accelerating MRI acquisition. To evaluate the clinical feasibility of accelerated brain MRI with DLR in pediatric neuroimaging, focusing on image quality compared to conventional MRI. In this retrospective study, 116 pediatric participants (mean age 7.9 ± 5.4 years) underwent routine brain MRI with three reconstruction methods: conventional MRI without DLR (C-MRI), conventional MRI with DLR (DLC-MRI), and accelerated MRI with DLR (DLA-MRI). Two pediatric radiologists independently assessed the overall image quality, sharpness, artifacts, noise, and lesion conspicuity. Quantitative image analysis included the measurement of image noise and coefficient of variation (CoV). DLA-MRI reduced the scan time by 43% compared with C-MRI. Compared with C-MRI, DLA-MRI demonstrated higher scores for overall image quality, noise, and artifacts, as well as similar or higher scores for lesion conspicuity, but similar or lower scores for sharpness. DLC-MRI demonstrated the highest scores for all the parameters. Despite variations in image quality and lesion conspicuity, the lesion detection rates were 100% across all three reconstructions. Quantitative analysis revealed lower noise and CoV for DLA-MRI than those for C-MRI. Interobserver agreement was substantial to almost perfect (weighted Cohen's kappa = 0.72-0.97). DLR enabled faster MRI with improved image quality compared with conventional MRI, highlighting its potential to address prolonged MRI scan times in pediatric neuroimaging and optimize clinical workflows.

While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F. A moving platform simulated respiratory motion in a NEMA-IEC body phantom with varying amplitudes (0, 7, 10, 20, 30 mm) and target-to-background ratios (2, 5, 10.5). Scans were reconstructed with and without DDMC, and the spherical targets' maximal and mean recovery coefficient (RC) and contrast-to-noise ratio (CNR) were measured. DDMC results in higher RC values in the target spheres. CNR values increase for small, high-motion affected targets but decrease for larger spheres with smaller amplitudes. A sub-analysis shows that DDMC increases the contrast of the sphere along with a 36% increase in background noise. While DDMC significantly enhances contrast (RC), its impact on detectability (CNR) is less profound due to increased background noise. CNR improves for small targets with high motion amplitude, potentially enhancing the detectability of low-uptake lesions. Given that the increased background noise may reduce detectability for targets unaffected by motion, we suggest that DDMC reconstructions are used best in addition to non-DDMC reconstructions.

Deep learning-based fast kV-switching CT (DL-FKSCT) generates complete sinograms for fast kV-switching dual-energy CT (DECT) scans by using a trained neural network to restore missing views. Such restoration significantly enhances the image quality of coronary CT angiography (CCTA), and the allowable heart rate (HR) may vary between DECT and single-energy CT (SECT). This study aimed to examine HR's effect onCCTA using DL-FKSCT. We scanned stenotic coronary artery phantoms attached to a pulsating cardiac phantom with DECT and SECT modes on a DL-FKSCT scanner. The phantom unit was operated with simulated HRs ranging from 0 (static) to 50-70 beats per minute (bpm). The sharpness and stenosis ratio of the coronary model were quantitatively compared between DECT and SECT, stratified by simulated HR settings using the paired t-test (significance was set at p < 0.01 with a Bonferroni adjustment for multiple comparisons). Regarding image sharpness, DECT showed significant superiority over SECT. In terms of the stenosis ratio compared to a static image reference, 70 keV virtual monochromatic image in DECT exhibited errors exceeding 10 % at HRs surpassing 65 bpm (p < 0.01), whereas 120 kVp SECT registered errors below 10 % across all HR settings, with no significant differences observed. In DL-FKSCT, DECT exhibited a lower upper limit of HR than SECT. Therefore, HR control is important for DECT scans in DL-FKSCT.

BackgroundHigher-resolution magnetic resonance imaging sequences are needed for the early detection of pancreatic cancer.PurposeTo compare the quality of our novel T2-weighted, high-contrast, thin-slice imaging sequence, with an improved spatial resolution and deep learning-based reconstruction (three-shot turbo spin-echo with deep learning-based reconstruction [3S-TSE-DLR]), for imaging the pancreas with imaging using three conventional sequences (half-Fourier acquisition single-shot turbo spin-echo [HASTE], fat-suppressed 3D T1-weighted [FS-3D-T1W] imaging, and magnetic resonance cholangiopancreatography [MRCP]).Material and MethodsPancreatic images of 50 healthy volunteers acquired with 3S-TSE-DLR, HASTE, FS-3D-T1W imaging, and MRCP were compared by two diagnostic radiologists. A 5-point scale was used for assessing motion artifacts, pancreatic margin sharpness, and the ability to identify the main pancreatic duct (MPD) on 3S-TSE-DLR, HASTE, and FS-3D-T1W imaging, respectively. The ability to identify MPD via MRCP was also evaluated.ResultsArtifact scores (the higher the score, the fewer the artifacts) were significantly higher for 3S-TSE-DLR than for HASTE, and significantly lower for 3S-TSE-DLR than for FS-3D-T1W imaging, for both radiologists. Sharpness scores were significantly higher for 3S-TSE-DLR than for HASTE and FS-3D-T1W imaging, for both radiologists. The rate of identification of MPD was significantly higher for 3S-TSE-DLR than for FS-3D-T1W imaging, for both radiologists, and significantly higher for 3S-TSE-DLR than for HASTE for one radiologist. The rate of identification of MPD was not significantly different between 3S-TSE-DLR and MRCP.Conclusion3S-TSE-DLR provides better image sharpness than conventional sequences, can identify MPD equally as well or better than HASTE, and shows identification performance comparable to that of MRCP.