Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images

Identifying clustering of cholera cases using geospatial analysis in Kolkata and surrounding districts: data from patients at tertiary care referral hospitals

Background

Cholera cases have increased globally across the Eastern Mediterranean, Africa, Southeast Asia, and parts of Europe since early 2024. This study aims to identify cholera hotspots and understand the spatial distribution of cholera in Kolkata and surrounding regions, a key cholera reservoir. Additionally, we examine sociodemographic factors and aspects related to water, sanitation, and hygiene (WASH).

Methods

Cholera clusters were detected using kernel density estimation and spatial autocorrelation through Global Moran's-I statistics, with local cluster patterns examined using Local Moran's-I statistics. Cholera cases from August 2021 to December 2023, treated at two tertiary care facilities in Kolkata: Infectious Diseases and Beleghata General Hospital and Dr. B C Roy Post Graduate Institute of Paediatric Sciences Hospital were included. Additionally, through a case-control study, 196 culture-confirmed cholera cases and 764 age/sex-matched neighborhood controls were enrolled, to investigate cholera risk factors.

Findings

Spatial analysis revealed a concentration of 196 cholera cases in Kolkata and its surrounding regions of Howrah, Hooghly, and North and South 24 Parganas. Hotspot analysis showed significant clustering in several Kolkata wards (31, 33, 56, 46, 57, 58, 59, 61, 66, 71, and 107), particularly in the northern, central, and east Kolkata wetlands areas (Global Moran's I statistic = 0.14, p < 0.001). These clusters had proximity between cases, with a median distance of 187.7 m, and 25.5% of cases as close as 73.9 m apart, suggesting localized transmission. Hotspots were identified with an average distance of 1600 m between them. Local Moran's I analysis found dense "high-high" clusters in these areas (p < 0.01), with a mean Moran's I index of 0.3, (range 0.1-4.6). The case-control study revealed that males were more likely to contract cholera, with an adjusted odds ratio of 2.4 (p < 0.01). There was no significant association found between cholera infection and sociodemographic factors or various WASH practices.

Interpretation

The findings emphasize the importance of targeted interventions, especially in identified hotspots, to mitigate cholera transmission. Addressing Socio-economic, and environmental factors especially improvement in WASH practices may further enhance prevention effects.

Funding

The author KK, received funding from the program of the Japan Initiative for Global Research Network on Infectious Diseases, (grant id: JP23wm0125004), from the Ministry of Education, Culture, Sports, Science and Technology in Japan, and Japan Agency for Medical Research and Development.

In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,ω $$ \omega $$ , in addition to the diffusion tensor,D $$ \mathbf{D} $$ , and relaxation,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , correlations. AD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on theirD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

Three-stage segmentation of lung region from CT images using deep neural networks

BACKGROUND

Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists' experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases.

METHODS

We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively.

RESULTS

The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95.

CONCLUSION

The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement.