An efficient compression scheme for 4-D medical images using hierarchical vector quantization and motion compensation

Advancements in health informatics: finite element insights into medial open-wedge high tibial osteotomy and lateral meniscal tears

Knee medial compartment osteoarthritis is effectively treated by a medial open-wedge high tibial osteotomy (MOWHTO). The feasibility and safety of MOWHTO for mild lateral meniscal tears are unknown. This study examined the feasibility and safety of knee joint weight-bearing line ratio (WBLr) adjustment during MOWHTO with lateral meniscal injuries. We used a healthy adult male's lower extremities computed tomography scans and knee joint magnetic resonance imaging images to create a normal fine element (FE) model. Based on this model, we generated nine FE models for the MOWHTO operation (WBLr: 40-80%) and 15 models for various lateral meniscal injuries. A compressive load of 650N was applied to all cases to calculate the von Mises stress (VMS), and the intact lateral meniscus' maximal VMS at 77.5% WBLr was accepted as the corrective upper limit stress. Our experimental results show that mild lateral meniscal tears can withstand MOWHTO, while severe tears cannot. Our findings expand the use of MOWHTO and provide a theoretical direction for practical decisions in patients with lateral meniscal injuries.

Gene expression insights: Chronic stress and bipolar disorder: A bioinformatics investigation

Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD.

Low-complexity three-dimensional discrete Hartley transform approximations for medical image compression

The discrete Hartley transform (DHT) is a useful tool for medical image coding. The three-dimensional DHT (3D DHT) can be employed to compress medical image data, such as magnetic resonance and X-ray angiography. However, the computation of the 3D DHT involves several multiplications by irrational quantities, which require floating-point arithmetic and inherent truncation errors. In recent years, a significant progress in wireless and implantable biomedical devices has been achieved. Such devices present critical power and hardware limitations. The multiplication operation demands higher hardware, power, and time consumption than other arithmetic operations, such as addition and bit-shifts. In this work, we present a set of multiplierless DHT approximations, which can be implemented with fixed-point arithmetic. We derive 3D DHT approximations by employing tensor formalism. Such proposed methods present prominent computational savings compared to the usual 3D DHT approach, being appropriate for devices with limited resources. The proposed transforms are applied in a lossy 3D DHT-based medical image compression algorithm, presenting practically the same level of visual quality (>98% in terms of SSIM) at a considerable reduction in computational effort (100% multiplicative complexity reduction). Furthermore, we implemented the proposed 3D transforms in an ARM Cortex-M0+ processor employing the low-cost Raspberry Pi Pico board. The execution time was reduced by ∼70% compared to the usual 3D DHT and ∼90% compared to 3D DCT.

A rapid compression technique for 4-D functional MRI images using data rearrangement and modified binary array techniques

Compression techniques are vital for efficient storage and fast transfer of medical image data. The existing compression techniques take significant amount of time for performing encoding and decoding and hence the purpose of compression is not fully satisfied. In this paper a rapid 4-D lossy compression method constructed using data rearrangement, wavelet-based contourlet transformation and a modified binary array technique has been proposed for functional magnetic resonance imaging (fMRI) images. In the proposed method, the image slices of fMRI data are rearranged so that the redundant slices form a sequence. The image sequence is then divided into slices and transformed using wavelet-based contourlet transform (WBCT). In WBCT, the high frequency sub-band obtained from wavelet transform is further decomposed into multiple directional sub-bands by directional filter bank to obtain more directional information. The relationship between the coefficients has been changed in WBCT as it has more directions. The differences in parent–child relationships are handled by a repositioning algorithm. The repositioned coefficients are then subjected to quantization. The quantized coefficients are further compressed by modified binary array technique where the most frequently occurring value of a sequence is coded only once. The proposed method has been experimented with fMRI images the results indicated that the processing time of the proposed method is less compared to existing wavelet-based set partitioning in hierarchical trees and set partitioning embedded block coder (SPECK) compression schemes [1]. The proposed method could also yield a better compression performance compared to wavelet-based SPECK coder. The objective results showed that the proposed method could gain good compression ratio in maintaining a peak signal noise ratio value of above 70 for all the experimented sequences. The SSIM value is equal to 1 and the value of CC is greater than 0.9 for all experiments. Subjective evaluation on the reconstructed images indicated that the proposed method could reproduce the diagnostic features of fMRI images clearly.

Hierarchical oriented predictions for resolution scalable lossless and near-lossless compression of CT and MRI biomedical images

We propose a new hierarchical approach to resolution scalable lossless and near-lossless (NLS) compression. It combines the adaptability of DPCM schemes with new hierarchical oriented predictors to provide resolution scalability with better compression performances than the usual hierarchical interpolation predictor or the wavelet transform. Because the proposed hierarchical oriented prediction (HOP) is not really efficient on smooth images, we also introduce new predictors, which are dynamically optimized using a least-square criterion. Lossless compression results, which are obtained on a large-scale medical image database, are more than 4% better on CTs and 9% better on MRIs than resolution scalable JPEG-2000 (J2K) and close to nonscalable CALIC. The HOP algorithm is also well suited for NLS compression, providing an interesting rate-distortion tradeoff compared with JPEG-LS and equivalent or a better PSNR than J2K for a high bit rate on noisy (native) medical images.