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Various studies have been conducted to reduce the blurring caused by movement in cine magnetic resonance imaging (MRI) of the heart. This study proposed a blind deconvolution method using a total variation regularization algorithm to remove blurring in cardiac cine magnetic resonance (MR) images. The MR data were acquired using a rat cardiac cine sequence in an open format. We investigated a blind deconvolution method with a total variation regularization, incorporating a 3-dimensional point-spread function on cardiac cine MRI. The gradient of magnitude (GM) and perceptual sharpness index (PSI) were used to evaluate the usefulness of the proposed deblurring method. We confirmed that the proposed method can reduce temporal blur relatively efficiently compared with the generalized variation-based deblurring algorithm. In particular, the GM and PSI values of the cardiac cine MR image corrected using the proposed method were improved by approximately 7.59 and 4.18 times, respectively, compared with the degraded image. We achieved improved image quality by validating a blind deconvolution method using a total variation regularization algorithm on the cardiac cine MR images of small animals.

An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient’s dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system.

In this randomized trial involving young adults with suspected appendicitis, low-dose abdominal computed tomography (CT) was noninferior to standard-dose abdominal CT, with similar rates of negative appendectomy and appendiceal perforation in the two study groups. Owing to the many advantages that computed tomography (CT) has over other diagnostic tests, including ultrasonography, 1 – 3 CT has assumed a paramount position in the evaluation of adults with suspected appendicitis. Despite historical debate, 4 the increased use of CT has been consistently found to coincide with a reduction in the rate of negative (unnecessary) appendectomies without an increase in the rate of appendiceal perforations — two important reciprocal measures of quality of care that represent, respectively, a false positive diagnosis and a delayed diagnosis. 5 – 10 The routine use of CT in patients suspected of having appendicitis has also been reported . . .

Hair loss is one of the most common skin problems experienced by more than half of the world's population. In East Asia, medicinal herbs have been used widely in clinical practice to treat hair loss. Recent studies, including systematic literature reviews, indicate that medicinal herbs may demonstrate potential effects for hair loss treatment. In a previous study, we identified medical herbs used frequently for alopecia treatment. Herein, we explored the potential novel therapeutic mechanisms of 20 vital medicinal herbs for alopecia treatment that could distinguish them from known mechanisms of conventional drugs using network pharmacology analysis methods. We determined the herb-ingredient–target protein networks and ingredient-associated protein (gene)-associated pathway networks and calculated the weighted degree centrality to define the strength of the connections. Data showed that 20 vital medicinal herbs could exert therapeutic effects on alopecia mainly mediated via regulation of various target genes and proteins, including acetylcholinesterase (AChE), phospholipase A2 (PLA2) subtypes, ecto-5-nucleotidase (NTE5), folate receptor (FR), nicotinamide N -methyltransferase (NNMT), and quinolinate phosphoribosyltransferase (QPRT). Findings regarding target genes/proteins and pathways of medicinal herbs associated with alopecia treatment offer insights for further research to better understand the pathogenesis and therapeutic mechanism of medicinal herbs for alopecia treatment with traditional herbal medicine.

Reducing radiation exposure in CT imaging is critical, particularly in routine and repeat examinations. Deep learning image reconstruction (DLIR) has emerged as a key approach for maintaining diagnostic quality at low-dose acquisition settings. This study compared two DLIR algorithms of Philips Precise Image (PI) and GE TrueFidelity (TF) under an 80 kVp low-dose CT scenario, using the AAPM CIRS-610 phantom to replicate clinical imaging conditions. The phantom’s linearity, high-resolution, and artifact modules were scanned with Philips CT 5300 and GE Revolution CT scanners at low-dose parameters. Images were reconstructed using five DLIR presets, including PI (Smoother, Standard, Sharper) and TF (Middle, High), and evaluated with eight quantitative metrics, including SNR, CNR, nRMSE, PSNR, SSIM, FSIM, UQI, GMSD, and gradient magnitude. TF-High delivered the highest SNR (115.0–118.0 across modules), representing a 54–57% improvement over PI-Smoother, and achieved superior PSNR and the lowest GMSD, reflecting better preservation of structure in low-dose images. PI-Sharper provided the strongest gradient magnitude, emphasizing fine edge detail. Under low-dose CT conditions, TF-High offered the optimal balance of noise suppression and structure fidelity, while PI-Sharper highlighted fine detail enhancement. These findings show that DLIR settings must be tailored to clinical needs when operating under low-dose imaging protocols.

This study aimed to confirm the presence of gingival inflammation through image analysis of the papillary gingiva using intra-oral photographs (IOPs) before and after orthodontic treatment and to confirm the possibility of using gingival image analysis for gingivitis screening. Five hundred and eighty-eight (n  =  588) gingival sites from the IOPs of 98 patients were included. Twenty-five participants who had completed their orthodontic treatments and were aged between 20 and 37 were included. Six points on the papillary gingiva were selected in the maxillary and mandibular anterior incisors. The red/green (R/G) ratio values were obtained for the selected gingival images and the modified gingival index (GI) was compared. The change in the R/G values during the orthodontic treatment period appeared in the order of before orthodontic treatment (BO), mid-point of orthodontic treatment (MO), three-quarters of the way through orthodontic treatment (TO), and immediately after debonding (IDO), confirming that it was similar to the change in the GI. The R/G value of the gingiva in the image correlated with the GI. Therefore, it could be used as a major index for gingivitis diagnosis using images.

Posterior acoustic shadowing is a fundamental physical phenomenon associated with calcified stones in ultrasound image, yet it has not been fully exploited in automated ultrasound analysis. This study aimed to develop an explainable, semi-automatic rule-based framework that explicitly incorporates posterior acoustic shadow characteristics for stone detection and localization in a clinically guided manner. A rule-based framework was designed to generate stone candidates using morphological enhancement and to evaluate them through local contrast analysis, posterior shadow region assessment, and shape-based penalties. A composite score integrating these features was used to rank candidates. The method was evaluated on 52 kidney stone and 66 gallbladder stone ultrasound images, stratified into three diagnostic confidence categories. Performance was assessed using an ablation study and centroid distance error measured in pixels relative to expert-defined references. In the 50–60% confidence group, the accuracy increased from 0.29 to 0.64 for kidney stones and from 0.30 to 0.60 for gallbladder stones when posterior shadow information was included. Centroid distance errors in the ≥80% confidence group were 1.26 ± 0.28 mm for kidney stones and 1.44 ± 0.91 mm for gallbladder stones. The proposed framework enhances diagnostic confidence by leveraging physically grounded posterior acoustic shadow analysis and provides a reproducible augmentation to conventional ultrasound-based stone assessment.

Background Herpes zoster (HZ) is a painful viral disease caused by varicella-zoster virus reactivation. Postherpetic neuralgia (PHN), characterized by severe pain, is its most frequent complication. Due to the limitations of western medicine (WM) in managing these conditions, there is increasing demand for Korean medicine (KM). However, Korean medicine clinical practice guidelines (KM-CPGs) for HZ and PHN are not yet established. This study investigates current clinical practices and perceptions of Korean medicine doctors (KMDs) regarding HZ and PHN to facilitate KM-CPGs development. Methods An online survey was conducted from May 6 to May 15, 2024. Participants were notified via short message services and email. The survey, based on literature reviews, expert consultations, and previous surveys, was divided into sections on clinical practice status, diagnosis, treatment, progress, prognosis, prevention, and perceptions. Results A total of 1,122 KMDs responded. Pain or sensory abnormalities were most frequently considered in diagnoses and prognoses. KMDs used an average of 4.72 KM treatments. Acupuncture (93.2%) was the most commonly used treatment, followed by herbal medicine (75.6%) and pharmacopuncture (63.8%). Herbal medicine was rated as the most important treatment (94.6%), followed by acupuncture (90.1%) and pharmacopuncture (75.3%). The primary treatment goal was to alleviate pain or sensory abnormalities. Significant differences were observed across KMDs’ experience groups in the perceptions of objective diagnostic tools (heart rate variability/Ryodoraku and digital infrared thermal imaging). Experience levels also affected the perceived importance of acupuncture and herbal medicine, with longer experience groups generally rating them higher. Notably, the > 10 years group rated the safety of acupuncture and herbal medicine significantly higher than the < 5 years group ( p  < 0.001). Conclusion This first large-scale study addresses a critical gap, establishing a strong clinical consensus among KMDs prioritizing pain relief using a multimodal strategy (acupuncture, herbal medicine, and pharmacopuncture). The findings highlight the urgent need to standardize objective diagnostic protocols and provide an evidence-informed foundation for developing KM-CPGs, which can effectively complement WM in improving patient outcomes for HZ and PHN.

We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS. The study population was adult (≥20 years) patients visiting ED for suspected infection. Target event was septic shock within 24 h after arrival. Demographics, vital signs, level of consciousness, chief complaints (CC) and initial blood test results were used as predictors. CC were embedded into 16-dimensional vector space using singular value decomposition. Six base learners including support vector machine, gradient-boosting machine, random forest, multivariate adaptive regression splines and least absolute shrinkage and selection operator and ridge regression and their ensembles were tested. We also trained and tested MLP networks with various setting. A total of 49,560 patients were included and 4817 (9.7%) had septic shock within 24 h. All ML classifiers significantly outperformed qSOFA score, MEWS and their age-sex adjusted versions with their AUROC ranging from 0.883 to 0.929. The ensembles of the base classifiers showed the best performance and addition of CC embedding was associated with statistically significant increases in performance. ML classifiers significantly outperforms clinical scores in screening septic shock at ED triage.

Accurate intracranial artery segmentation from digital subtraction angiography (DSA) is critical for neurovascular diagnosis and intervention planning. Vascular extraction, which combines preprocessing methods and deep learning models, yields a high level of results, but limited preprocessing results constrain the improvement of results. We propose a texture-based contrast enhancement preprocessing framework integrated with the nnU-Net model to improve vessel segmentation in time-sequential DSA images. The method generates a combined feature mask by fusing local contrast, local entropy, and brightness threshold maps, which is then used as input for deep learning-based segmentation. Segmentation performance was evaluated using the DIAS dataset with various standard quantitative metrics. The proposed preprocessing significantly improved segmentation across all metrics compared to both the baseline and contrast-limited adaptive histogram equalization (CLAHE). Using nnU-Net, the method achieved a Dice Similarity Coefficient (DICE) of 0.83 ± 0.20 and an Intersection over Union (IoU) of 0.72 ± 0.14, outperforming CLAHE (DICE 0.79 ± 0.41, IoU 0.70 ± 0.23) and the baseline (DICE 0.65 ± 0.15, IoU 0.47 ± 0.20). Most notably, vessel connectivity (VC) dropped by over 65% relative to unprocessed images, indicating marked improvements in VC and topological accuracy. This study demonstrates that combining texture-based preprocessing with nnU-Net delivers robust, noise-tolerant, and clinically interpretable segmentation of intracranial arteries from DSA.