High-resolution Imaging Research Articles

Direct high-resolution observation of feedback and chemical enrichment in the circumgalactic medium at redshift z ∼ 2.8

The circumgalactic medium (CGM) plays a vital role in galaxy evolution, however, studying the emission from CGM is challenging due to its low surface brightness and the complexities involved in interpreting resonant lines such as Lyman-alpha ( The near-infrared coverage, unprecedented sensitivity, and high spatial resolution of the James Webb Space Telescope (JWST) enable us to study the optical strong lines associated with the extended ``nebulae'' at redshifts of 2--3. These lines serve as diagnostic tools to infer the physical conditions in the massive CGM gas reservoir of these systems. In deep medium-band images taken by the JWST, we serendipitously discovered the emission from the CGM surrounding a massive interacting galaxy system at a redshift of z ∼ 2.8, known to be embedded in a bright extended (100 kpc) ``nebula.'' This is the first time that the lines have been detected from a ``nebula.'' The JWST images reveal that the CGM gas actually resides in narrow (∼ 2.5 kpc) filamentary structures with strong emission, tracing the same extent as the emission. An analysis of the suggests that the emitting CGM is fully ionized and is energetically dominated by mechanical heating. We also find that the inferred density and pressure are higher than those commonly predicted by simulations of the CGM. We conclude that the observed CGM emission originates from the gas expelled by the episodic feedback processes, cooling down and enriching the CGM, while traveling a distance of at least 60 kpc. These observations demonstrate how intensive feedback processes shape gas distribution and properties in the CGM around massive halos. While access to such deep, high-resolution imaging opens up a new discovery space for investigating the CGM, it also challenges numerical simulations with respect to explaining and reproducing the exquisitely complex structures revealed by the observations.

High-resolution imaging of the radio source associated with Project Hephaistos Dyson Sphere Candidate G

Abstract We present high-resolution e-MERLIN and EVN (e-VLBI) observations of a radio source associated with Dyson Sphere candidate G, identified as part of Project Hephaistos. The radio source, VLASS J233532.86-000424.9, is resolved into 3 compact components and shows the typical characteristics of a radio-loud AGN. In particular, the EVN observations show that it has a brightness temperature in excess of 108 K. No radio emission is detected at the position of the M-dwarf star. This result confirms our earlier hypothesis, that at least some of the Dyson Sphere candidates of project Hephaistos are contaminated by obscured, background AGN, lying close to the line of sight of otherwise normal galactic stars. High-resolution radio observations of other Dyson Sphere candidates can be useful in distinguishing truly promising candidates from those contaminated by background sources.

Efficient and Robust Europium(III)‐Based Hybrid Lanthanide Scintillators for Advanced X‐Ray Imaging

AbstractScintillators that convert ionizing radiation into low‐energy photons are essential for medical diagnostics and industrial inspections. Despite advances in X‐ray scintillators, challenges remain in achieving high efficiency, environmental compatibility, stability, and flexibility. Here, we present experimental investigations of a new type of europium(III)‐based hybrid ternary complex scintillators for improved X‐ray detection and imaging. Benefiting from the synergistic interaction between dual organic ligands and lanthanide ions, the Eu(TTA)3Phen complex demonstrates exceptional radioluminescence and light yield under X‐ray excitation, with a detection limit of 19.97 nGy s−1, well below typical radiation doses used in medical diagnostics. Moreover, lanthanide complex Eu(TTA)3Phen exhibited excellent thermal and photostability, showing minimal degradation even after extended X‐ray exposure. By integrating with flexible polymer matrices, a high‐transmission Eu(TTA)3Phen‐PMMA composite film was fabricated for X‐ray radiography, demonstrating high spatial resolution (<10 um) and superior image quality across various target samples. These findings hold substantial promise for next‐generation X‐ray imaging applications, offering high sensitivity, stability, flexibility, and versatility, making them ideally suited for advanced radiographic systems.

Efficient and Robust Europium(III)-Based Hybrid Lanthanide Scintillators for Advanced X-Ray Imaging.

Scintillators that convert ionizing radiation into low-energy photons are essential for medical diagnostics and industrial inspections. Despite advances in X-ray scintillators, challenges remain in achieving high efficiency, environmental compatibility, stability, and flexibility. Here, we present experimental investigations of a new type of europium(III)-based hybrid ternary complex scintillators for improved X-ray detection and imaging. Benefiting from the synergistic interaction between dual organic ligands and lanthanide ions, the Eu(TTA)3Phen complex demonstrates exceptional radioluminescence and light yield under X-ray excitation, with a detection limit of 19.97 nGy s-1, well below typical radiation doses used in medical diagnostics. Moreover, lanthanide complex Eu(TTA)3Phen exhibited excellent thermal and photostability, showing minimal degradation even after extended X-ray exposure. By integrating with flexible polymer matrices, a high-transmission Eu(TTA)3Phen-PMMA composite film was fabricated for X-ray radiography, demonstrating high spatial resolution (<10 um) and superior image quality across various target samples. These findings hold substantial promise for next-generation X-ray imaging applications, offering high sensitivity, stability, flexibility, and versatility, making them ideally suited for advanced radiographic systems.

The correlation study between posterior fossa crowding and classical trigeminal neuralgia

ObjectiveTo explore the correlation between posterior fossa crowding and the occurrence of classical trigeminal neuralgia (TN).MethodsA total of 60 patients diagnosed with classical TN and 60 age- and sex-matched healthy volunteers were included as a control group for a case-control study. All subjects underwent high-resolution 3D magnetic resonance imaging (MRI) examinations (including 3D-FIESTA and 3D-TOF MRA sequences). The original data were subjected to 3D reconstruction and measurement of posterior fossa volume (PFV) and hindbrain volume (HBV) using 3D-slicer software. The posterior fossa crowding index (PFCI) was calculated as HBV/PFV × 100%. Finally, data were analyzed using SPSS 22.0 statistical software.ResultsThe average PFCI in patients with TN was 85.0% ± 3.9%, compared to 82.7% ± 3.9% in the control group, with a significant statistical difference (P = 0.025). Female patients with TN had a more crowded posterior fossa than male patients (86.4% ± 3.8% vs. 83.4% ± 3.4%, P = 0.033). Multiple linear regression analysis showed that a higher PFCI was associated with being female (P = 0.022), younger age (P = − 0.003), and being a patient with TN (P = − 0.023).ConclusionPatients with PTN have a more crowded posterior fossa compared to the healthy control group. A higher PFCI is associated with being female, younger age, and being a patient with TN. Posterior fossa crowding may be a risk factor for neurovascular conflict (NVC), making it more likely to lead to the occurrence of TN.

MALDI-MSI: A potential game changer in forensic sciences.

Matrix-assisted laser Desorption/Ionization Mass Spectrometry Imaging (MALDI MSI) is an analytical technique used for the spatial mapping of drugs, explosives, and organic samples, making it a game-changer in Forensic examination. It detects a wide range of biomolecules in their native state without specific tags, antibodies, labels, and dyes. This review aims to highlight the advancement of MALDI-MSI over time and its impact on Forensic Science due to high-resolution molecular imaging. To foster the development of forensic investigations the utility of MALDI-MSI in six different broad areas, Latent Fingerprinting division,forensic toxicology division,Crime Scene Reconstruction and investigation division, Sex crimes, forensic trichology division, question document analysis, is explored in this review. MALDI-MSI possesses a unique strength of molecular imaging of biomolecules without complex preparation from diverse sample types. In the future, the sensitivity and detection limits of MALDI-MSI can be enhanced and its instrumental size should be reduced to perform on-site investigation.

White Matter Fiber Bundle Alterations Correlate with Gait and Cognitive Impairments in Parkinson's Disease based on HARDI Data.

The neuroanatomical basis of white matter fiber tracts in gait impairments in individuals suffering from Parkinson's Disease (PD) is unclear. Twenty-four individuals living with PD and 29 Healthy Controls (HCs) were included. For each participant, two-shell High Angular Resolution Diffusion Imaging (HARDI) and high-resolution 3D structural images were acquired using the 3T MRI. Diffusion-weighted data preprocessing was performed using the orientation distribution function to trace the main fiber tracts in PD individuals. Clinical characteristics between the two groups were compared, and the correlation between the FA value and behavioral data was analyzed. Quantitative gait and clinical parameters were recorded in PD at ON and OFF states, respectively. The mean tract-specific FA values of the right Cingulum Cingulate (rCC) were statistically different between the PD group and the HC group (p =0.047). The FA value of 34-58 equidistant nodes in rCC was positively correlated with Mini-Mental State Examination (MMSE) (r=0.527, p=0.024), Berg Balance Scale (BBS)-OFF (r=0.480, p =0.040), and BBS-ON (r=0.528, p =0.024) scores, while it was negatively correlated with the MDS-UPDRS-III-ON score (r=-0.502, p =0.030). Regarding the gait analysis, the FA value was significantly correlated with velocity, cadence, and stride time of the pace and rhythm domains in both 'ON' and 'OFF' states, respectively (p<0.05). This study served as an initial exploration to establish that HARDI sequences could be employed as a robust tool for analyzing microstructural alterations in white matter fiber bundles among PD patients, although the sample size was small. We confirmed microstructural integrity impairment of rCC to be significantly associated with both gait and cognitive deficits in patients with PD. Early detection of microstructural changes in rCC and targeted treatment can help improve behavioral disorders. In the future, we intend to further integrate multimodal data with assessments of patient behavior both prior to and following intervention. We will validate our findings within an independent cohort to monitor disease progression and evaluate the efficacy of therapeutic interventions.

Advances in Cardiovascular Multimodality Imaging in Patients with Marfan Syndrome

Marfan syndrome (MFS) is a genetic disorder affecting connective tissue, often leading to cardiovascular complications such as aortic aneurysms and mitral valve prolapse. Cardiovascular multimodality imaging plays a crucial role in the diagnosis, monitoring, and management of MFS patients. This review explores the advancements in echocardiography, cardiovascular magnetic resonance (CMR), cardiac computed tomography (CCT), and nuclear medicine techniques in MFS. Echocardiography remains the first-line tool, essential for assessing aortic root, mitral valve abnormalities, and cardiac function. CMR provides detailed anatomical and functional assessments without radiation exposure, making it ideal for long-term follow-up. CT offers high-resolution imaging of the aorta, crucial for surgical planning, despite its ionizing radiation. Emerging nuclear medicine techniques, though less common, show promise in evaluating myocardial involvement and inflammatory conditions. This review underscores the importance of a comprehensive imaging approach to improve outcomes and guide interventions in MFS patients. It also introduces novel aspects of multimodality approaches, emphasizing their impact on early detection and management of cardiovascular complications in MFS.

Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network

The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. Specifically, the U-shaped end-to-end neural network which contains down-sampling and up-sampling parts is proposed to improve the physical spatial resolution limited by the array aperture. The single target and multiple cases are considered separately. In both cases, the normalized loss on the testing sets declines rapidly, and the predicted high-resolution images own great agreement with the ground truth eventually. Further improvements in resolution are focused on, that is, compressing the predicted high-resolution image to its physical spatial resolution limitation. The results show that the trained end-to-end neural network could map high resolution targets to the impulse responses at the same location and amplitude with an uncertain target number. The proposed convolutional neural network approach could give a practical alternative to improve the physical spatial resolution of underwater sonar images.

Nondestructive testing of railway embankments by measuring multi-modal dispersion of surface waves induced by high-speed trains with linear geophone arrays

To effectively address engineering challenges and risks, it is crucial to characterize mechanical properties of near-surface environments. The Multichannel Analysis of Surface Waves (MASW) has proven to be a valuable active seismic imaging technique by providing near-surface shear (S)-wave velocities estimations. However, its application to urban areas requires further development. This study leverages well-constrained experimental sites to assess the viability of a passive-MASW technique, utilizing seismic waves induced by high-speed train traffic instead of conventional active sources. We suggest employing short 96-geophone uniform linear arrays to capture surface waves in a broad frequency band (10-200 Hz). Train passages are automatically detected and categorized regarding to the train travel direction. Seismic interferometry and phase-weighted stack techniques are applied to generate virtual shot-gathers that are transformed into high-resolution multi-modal dispersion images. Our results demonstrate a strong coherence between the picked dispersion curves from the passive-MASW approach and those obtained through traditional active MASW with a hammer source. We discuss the validity of higher modes and explore array density limits to ensure reliable results. Our findings highlight that seismic interferometry, coupled with a high phase-weighted stack power, effectively recovers energy at high frequencies, enhancing the characterization of multi-modal surface-wave dispersion associated with thin near-surface layers.

Isolated inflammatory involvement of the occipital artery in giant cell arteritis and polymyalgia rheumatica: findings from a retrospective analysis and the critical role of MRI in diagnosis.

Diagnosis of Giant Cell Arteritis (GCA) and Polymyalgia rheumatica (PMR) may be challenging as many patients present with non-specific symptoms. Superficial cranial arteries are predilection sites of inflammatory affection. Ultrasound is typically the diagnostic tool of first choice supplementary to clinical and laboratory examination. Inflammation of temporal arteries can be detected sonographically with high reliability. However, due to the vessel's course and location, occipital arteries evade sonographic detectability. The aim of our study was to evaluate the infestation pattern of superficial cranial arteries in GCA and PMR patients with special focus on the occipital arteries. 90 treatment-naïve patients with clinically and/or histologically proven GCA and/or PMR (51 GCA, 20 PMR, 10 GCA-PMR) were included in the study. All patients underwent contrast-enhanced, fat-suppressed, high-resolution black blood 2D T1-weighted spin echo imaging at 3T MRI. Images were read by three different readers independently. Temporal and occipital arteries were assessed regarding vasculitic affection. Circumferential mural hyperenhancement and thickening of the vessel wall ≥ 600μm was considered positive for vasculitis. 9/90 (10%) of all patients revealed inflammatory changes of the occipital artery only. Prevalence of isolated inflammatory affection of occipital artery was even higher in the GCA subgroup with 7/51 (14%) patients. 14% of GCA patients and 10% of GCA-PMR patients present with signs of inflammation of the occipital artery only. Since the occipital artery is not accessible to routine ultrasound examination, MRI renders incremental value in the diagnosis of GCA and PMR patients.

Deep imaging of LepR+ stromal cells in optically cleared murine bone hemisections

Tissue clearing combined with high-resolution confocal imaging is a cutting-edge approach for dissecting the three-dimensional (3D) architecture of tissues and deciphering cellular spatial interactions under physiological and pathological conditions. Deciphering the spatial interaction of leptin receptor-expressing (LepR+) stromal cells with other compartments in the bone marrow is crucial for a deeper understanding of the stem cell niche and the skeletal tissue. In this study, we introduce an optimized protocol for the 3D analysis of skeletal tissues, enabling the visualization of hematopoietic and stromal cells, especially LepR+ stromal cells, within optically cleared bone hemisections. Our method preserves the 3D tissue architecture and is extendable to other hematopoietic sites such as calvaria and vertebrae. The protocol entails tissue fixation, decalcification, and cryosectioning to reveal the marrow cavity. Completed within approximately 12 days, this process yields highly transparent tissues that maintain genetically encoded or antibody-stained fluorescent signals. The bone hemisections are compatible with diverse antibody labeling strategies. Confocal microscopy of these transparent samples allows for qualitative and quantitative image analysis using Aivia or Bitplane Imaris software, assessing a spectrum of parameters. With proper storage, the fluorescent signal in the stained and cleared bone hemisections remains intact for at least 2–3 months. This protocol is robust, straightforward to implement, and highly reproducible, offering a valuable tool for tissue architecture and cellular interaction studies.

Fast quantum ghost imaging with a single-photon-sensitive time-stamping camera.

Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging.

Accurate shear estimation with fourth-order moments

Abstract As imaging surveys progress in exploring the large-scale structure of the Universe through the use of weak gravitational lensing, achieving subpercent accuracy in estimating shape distortions caused by lensing, or shear, is imperative for precision cosmology. In this paper, we extend the Fourier Power Function Shapelets (FPFS) shear estimator using fourth-order shapelet moments and combine it with the original second-order shear estimator to reduce galaxy shape noise. We calibrate this novel shear estimator analytically to a subpercent level accuracy using the AnaCal framework. This higher-order shear estimator is tested with realistic image simulations, and after analytical correction for the detection/selection bias and noise bias, the multiplicative shear bias |m| is below 3 × 10−3 (99.7 % confidence interval) for both isolated and blended galaxies. Once combined with the second-order FPFS shear estimator, the shape noise is reduced by ∼35 % for isolated galaxies in simulations with Hyper Suprime-Cam (HSC) and Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) observational conditions. However, for blended galaxies, the effective number density does not significantly improve with the combination of the two estimators. Based on these results, we recommend exploration of how this framework can further reduce the systematic uncertainties in shear due to PSF leakage and modeling error, and potentially provide improved precision in shear inference in high-resolution space-based images.

High-resolution long-distance depth imaging LiDAR with ultra-low timing jitter superconducting nanowire single-photon detectors

High-resolution long-distance depth imaging LiDAR with ultra-low timing jitter superconducting nanowire single-photon detectors

Single-distance nano-holotomography with coded apertures.

High-resolution phase-contrast 3D imaging using nano-holotomography typically requires collecting multiple tomograms at varying sample-to-detector distances, usually 3 to 4. This multi-distance approach limits temporal resolution, making it impractical for operando studies. Moreover, shifting the sample complicates reconstruction, requiring precise alignment, registration, and interpolation to correct for shift-dependent magnification on the detector. In response, we propose and validate through simulations a novel, to the best of our knowledge, single-distance approach that leverages coded apertures to structure beam illumination while the sample rotates. This approach relies on a joint reconstruction scheme, which integrates phase retrieval with 3D tomography, ensuring data consistency and achieving artifact-free reconstructions from a single distance, unlocking dynamic experiments.

Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application.

This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) -ResNet, DenseNet, and EfficientNet-were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

MoS2–Plasmonic Hybrid Platforms: Next-Generation Tools for Biological Applications

The combination of molybdenum disulfide (MoS2) with plasmonic nanomaterials has opened up new possibilities in biological applications by combining MoS2’s biocompatibility and high surface area with the optical sensitivity of plasmonic metals. These MoS2–plasmonic hybrid systems hold great promise in areas such as biosensing, bioimaging, and phototherapy, where their complementary properties facilitate improved detection, real-time visualization, and targeted therapeutic interventions. MoS2’s adjustable optical features, combined with the plasmon resonance of noble metals such as gold and silver, enhance signal amplification, enabling detailed imaging and selective photothermal or photodynamic therapies while minimizing effects on healthy tissue. This review explores various synthesis strategies for MoS2–plasmonic hybrids, including seed-mediated growth, in situ deposition, and heterojunction formation, which enable tailored configurations optimized for specific biological applications. The primary focus areas include highly sensitive biosensors for detecting cancer and infectious disease biomarkers, high-resolution imaging of cellular dynamics, and the development of phototherapy methods that allow for accurate tumor ablation through light-induced thermal and reactive oxygen species generation. Despite the promising advancements of MoS2–plasmonic hybrids, translating these platforms into clinical practice requires overcoming considerable challenges, such as synthesis reproducibility, toxicity, stability in physiological conditions, targeted delivery, and scalable manufacturing. Addressing these challenges is essential for realizing their potential as next-generation tools in diagnostics and targeted therapies.

Time-lapse shear-wave tomography at a test dyke with changing water levels

Although seismic methods using S waves can offer high-resolution images of the shallow soil layers, the use of body S-wave tomography for near-surface water monitoring remains underexplored, and the quantitative interpretation of any observed changes in S-wave velocity ( V S) in the field conditions is challenging. We conduct a time-lapse S-wave tomography experiment on a field-scale test dike with controlled water levels, allowing for detailed examination of how VS responds to water infiltration. Our results demonstrate that VS decreases progressively, starting from the high-water-side slope and extending across the dike, as the water level rises, with the most significant changes occurring in the sand body and not in the clay cover. The maximum reduction in VS is approximately 40–60 m/s, corresponding to approximately 25%–30% reduction from the initial condition. We use the squared velocity ratio to evaluate the relative contributions of bulk density and shear modulus to VS changes. In the initially unsaturated zone, both these factors contribute significantly to the observed VS changes as the zone becomes fully saturated. In fully saturated zones, we assess the changes in the effective stress using the squared VS ratio. Although the low-water side of the dike shows stress changes that are consistent with numerical modeling, the high-water side shows large stress changes than expected, possibly due to excess pore pressure during the dynamic flow conditions. These findings highlight the potential of body S-wave tomography for high-resolution, near-surface hydrologic monitoring, and provide insights into the complex interactions between physical properties that influence VS changes under varying water levels in field environments.

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers. Brain MRI examinations including MPRAGE taken at a single institution for investigation of mild cognitive impairment, dementia and epilepsy between January 2020 and December 2022 were included retrospectively. Images taken in 2020 or 2021 were assigned to training and validation datasets, and images from 2022 were used for the test dataset. Using the training and validation set, we determined one model using visual evaluation by radiologists with reference to image quality metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The test dataset was evaluated by visual assessment and quality metrics. Voxel-based morphometric analysis was also performed, and we evaluated Dice score and volume differences between generated and original images of major structures were calculated as absolute symmetrized percent change. Training, validation, and test datasets comprised 340 patients (mean age, 56.1 ± 24.4years; 195 women), 36 patients (67.3 ± 18.3years, 20 women), and 193 patients (59.5 ± 24.4years; 111 women), respectively. The test dataset showed: PSNR, 35.4 ± 4.91; SSIM, 0.871 ± 0.058; and LPIPS 0.045 ± 0.017. No overfitting was observed. Dice scores for the segmentation of main structures ranged from 0.788 (left amygdala) to 0.926 (left ventricle). Quadratic weighted Cohen kappa values of visual score for medial temporal lobe between original and generated images were 0.80-0.88. Images generated using our DL-based model can be used for post-processing and visual evaluation of medial temporal lobe atrophy.