A Pilot Study to Estimate the Impact of High Matrix Image Reconstruction on Chest Computed Tomography

Objectives: The objectives of the study were to estimate the impact of high matrix image reconstruction on chest computed tomography (CT) compared to standard image reconstruction. Material and Methods: This retrospective study included patients with interstitial or parenchymal lung disease, airway disease, and pulmonary nodules who underwent chest CT. Chest CT images were reconstructed using high matrix (1024 × 1024) or standard matrix (512 × 512), with all other parameters matched. Two radiologists, blinded to reconstruction technique, independently examined each lung, viewing image sets side by side and rating the conspicuity of imaging findings using a 5-point relative conspicuity scale. The presence of pulmonary nodules and confidence in classification of internal attenuation was also graded. Overall image quality and subjective noise/artifacts were assessed. Results: Thirty-four patients with 68 lungs were evaluated. Relative conspicuity scores were significantly higher using high matrix image reconstruction for all imaging findings indicative of idiopathic lung fibrosis (peripheral airway visualization, interlobular septal thickening, intralobular reticular opacity, and end-stage fibrotic change; P ≤ 0.001) along with emphysema, mosaic attenuation, and fourth order bronchi for both readers (P ≤ 0.001). High matrix reconstruction did not improve confidence in the presence or classification of internal nodule attenuation for either reader. Overall image quality was increased but not subjective noise/artifacts with high matrix image reconstruction for both readers (P < 0.001). Conclusion: High matrix image reconstruction significantly improves the conspicuity of imaging findings reflecting interstitial lung disease and may be useful for diagnosis or treatment response assessment.

A practical framework RNMF for the potential mechanism of cancer progression with the analysis of genes cumulative contribution abundance

Mutational signatures can reveal the mechanism of tumorigenesis. We developed the RNMF software for mutational signatures analysis, including a key model of cumulative contribution abundance (CCA) which was designed to highlight the association between genes and mutational signatures. Applied it to 1073 esophageal squamous cell carcinoma (ESCC) and found that APOBEC signatures (SBS2* and SBS13*) mediated the occurrence of PIK3CA E545k mutation. Furthermore, we found that age signature is strongly linked to the TP53 R342* mutation. In addition, the CCA matrix image data of genes in the signatures New, SBS3* and SBS17b* were helpful for the preliminary evaluation of shortened survival outcome. In a word, RNMF can successfully achieve the correlation analysis of genes and mutational signatures, proving a strong theoretical basis for the study of tumor occurrence and development mechanism and clinical adjuvant medicine.

Archetype and matrix image (potential forms of an image)

The article is devoted to the analysis of potential forms of the image in culture and the development of the Jungian concept of an archetype in Wunenburger, Bachelard, Durand and modern cultural studies. The notion of archetype in Carl Jung’s concept is related to the distinction between the archetype in itself, noumenon and archetype image conceived as a phenomenal manifestation of archetypal forms in the space-time, historical and social reality. This distinction has a Kantian lineage, which Jung was clearly conscious of. He provides a reference to the conception of Kant, calling it “a school of philosophical criticism” several times in his writings. In the studies of Jung’s concept, his approach to transcendentalism (Z. Rosińska) is at times present, and a certain type of its specific, evolutionary interpretation is used. The archetype, being a “thing in itself ”, determines the appearance of phenomenal forms in the space-time, historical and social world, while remaining outside the direct entanglement and referring to the evolutionally active sphere of the unconscious as an anthropological datum. The archetypal image expresses the permanent approximation of manifestation of the semantic core of the archetype itself. The notion of an archetype has evolved in contemporary understandings and conceptions; it was conceived as a psychological expression of the evolutionary pattern of behavior, as an affective-representative node and ante rem of an idea, as a hermeneutic pattern of meaning or as a kind of matrix image. The archetype can be understood in connection with anthropological structures or with a cultural image; one way of comprehension does not exclude the other.

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.