Transfer Imaging Research Articles

Advanced Magnetic Resonance Imaging for Early Diagnosis and Monitoring of Movement Disorders.

Advanced magnetic resonance imaging (MRI) techniques are transforming the study of movement disorders by providing valuable insights into disease mechanisms. This narrative review presents a comprehensive overview of their applications in this field, offering an updated perspective on their potential for early diagnosis, disease monitoring, and therapeutic evaluation. Emerging MRI modalities such as neuromelanin-sensitive imaging, diffusion-weighted imaging, magnetization transfer imaging, and relaxometry provide sensitive biomarkers that can detect early microstructural degeneration, iron deposition, and connectivity disruptions in key regions like the substantia nigra. These techniques enable earlier and more accurate differentiation of movement disorders, including Parkinson's disease, progressive supranuclear palsy, multiple system atrophy, corticobasal degeneration, Lewy body and frontotemporal dementia, Huntington's disease, and dystonia. Furthermore, MRI provides objective metrics for tracking disease progression and assessing therapeutic efficacy, making it an indispensable tool in clinical trials. Despite these advances, the absence of standardized protocols limits their integration into routine clinical practice. Addressing this gap and incorporating these techniques more systematically could bring the field closer to leveraging advanced MRI for personalized treatment strategies, ultimately improving outcomes for individuals with movement disorders.

Ultra-high-resolution brain MRI at 0.55T: bSTAR and its application to magnetization transfer ratio imaging.

This study aims to evaluate the feasibility of structural sub-millimeter isotropic brain MRI at 0.55 T using a 3D half-radial dual-echo balanced steady-state free precession sequence, termed bSTAR and to assess its potential for high-resolution magnetization transfer imaging. Phantom and in-vivo imaging of three healthy volunteers was performed on a low-field 0.55 T MR-system with isotropic bSTAR resolution settings of 0.87 × 0.87 × 0.87 mm3 and 0.69 × 0.69 × 0.69 mm3. Furthermore, off-resonance mapping was performed using 3D double-echo spoiled gradient imaging. For magnetization transfer (MT) MRI, the RF pulse duration of the 0.87 mm bSTAR scan was modified. Data were reconstructed using a GPU-accelerated compressed sensing algorithm. Magnetization transfer ratio (MTR) maps were calculated from two bSTAR scans with and without RF pulse prolongation. The MTR scan took 5 minutes and the reproducibility was assessed through repeated scans. Off-resonance mapping revealed that bSSFP brain imaging with TR < 5ms is essentially free of off-resonance-related artifacts even near the nasal cavities. Phantom and in-vivo scans demonstrated the feasibility of sub-millimeter isotropic bSTAR imaging. MTR maps obtained with high isotropic resolution bSTAR showed contrast between white and gray matter in agreement with expectations from high-field studies. The MTR measurements were highly reproducible with an average inter-scan MTR peak value of 43.3 ± 0.3 percent units. This study demonstrated the potential of sub-millimeter and artifact-free morphologic brain imaging at 0.55 T using bSTAR leveraging the advantages of low-field MRI, such as reduced susceptibility artifacts and improved radio-frequency field homogeneity. Furthermore, MT-sensitized bSTAR brain MRI enabled whole-brain MTR assessment within clinically feasible times and with high reproducibility.

Illustration image style transfer method design based on improved cyclic consistent adversarial network.

To improve the expressiveness and realism of illustration images, the experiment innovatively combines the attention mechanism with the cycle consistency adversarial network and proposes an efficient style transfer method for illustration images. The model comprehensively utilizes the image restoration and style transfer capabilities of the attention mechanism and the cycle consistency adversarial network, and introduces an improved attention module, which can adaptively highlight the key visual elements in the illustration, thereby maintaining artistic integrity during the style transfer process. Through a series of quantitative and qualitative experiments, high-quality style transfer is achieved, especially while retaining the original features of the illustration. The results show that when running on the Monet2photo dataset, when the system iterates to 72 times, the loss function value of the research method approaches the target value of 0.00. On the Horse2zebra dataset, as the sample size increases, the research method has the smallest FID value, and the value approaches 40.00 infinitely. With the change of peak signal-to-noise ratio, the accuracy of the research algorithm has been greater than 95.00%. Practical application found that the color of the image obtained by the research method is more gorgeous and the line features are more obvious. The above results all show that the research method has achieved more satisfactory results in the task of style transfer of illustration images, especially in terms of the accuracy of style transfer and the retention of image details.

CEST imaging combined with 1H-MRS reveal the neuroprotective effects of riluzole by improving neurotransmitter imbalances in Alzheimer’s disease mice

BackgroundThe imbalance of glutamate (Glu) and gamma-aminobutyric acid (GABA) neurotransmitter system plays a crucial role in the pathogenesis of Alzheimer’s disease (AD). Riluzole is a Glu modulator originally approved for amyotrophic lateral sclerosis that has shown potential neuroprotective effects in various neurodegenerative disorders. However, whether riluzole can improve Glu and GABA homeostasis in AD brain and its related mechanism of action remain unknown. This study utilized chemical exchange saturation transfer (CEST) imaging combined with proton magnetic resonance spectroscopy (1H-MRS) to monitor the dynamic changes of Glu and GABA in riluzole-treated AD mice, aiming to evaluate the efficacy and mechanism of riluzole in AD treatment.MethodsGluCEST, GABACEST and 1H-MRS were used to longitudinally monitor Glu and GABA levels in 3xTg AD mice treated with riluzole (12.5 mg/kg/day) or vehicle for 20 weeks. Magnetic resonance measurements were performed at baseline, 6, 12, and 20 weeks post-treatment. Cognitive performance was assessed using the Morris Water Maze (MWM) at baseline, 10, and 20 weeks. At the study endpoint, immunohistochemistry, Nissl staining, and Western blot were used to evaluate the brain pathology, neuronal survival, and protein expression.ResultsGluCEST, GABACEST and 1H-MRS consistently revealed higher levels of Glu and GABA in the brain of riluzole-treated AD mice compared to untreated controls, which were associated with improvements in spatial learning and memory. The cognitive improvements significantly correlated with the increased GluCEST signals and Glu levels. Immunohistochemistry and Nissl staining demonstrated that riluzole treatment reduced amyloid-beta (Aβ) deposition, tau hyperphosphorylation, GFAP-positive astrocyte activation, and prevented neuronal loss. Moreover, riluzole upregulated the expression of excitatory amino acid transporter 2 (EAAT2), glutamic acid decarboxylase 65/67 (GAD65/67), and glutamine synthetase (GS), suggesting enhanced neurotransmitter metabolism.ConclusionsCEST imaging combined with 1H-MRS demonstrated the effectiveness of riluzole in modulating Glu- and GABA-related changes and improving cognitive function in 3xTg AD mice, potentially through regulating key proteins involved in neurotransmitter metabolism. These findings suggest riluzole as a therapeutic agent for Alzheimer’s disease and highlight the utility of multimodal MR imaging in monitoring treatment response and exploring disease mechanisms.

Quantitative magnetization transfer and g-ratio imaging of white matter myelin in early psychotic spectrum disorders.

Myelin abnormalities in white matter have been implicated in the pathophysiology of psychotic spectrum disorders (PSD), which are characterized by brain dysconnectivity as a core feature. Among evidence from in vivo MRI studies, diffusion imaging findings have largely supported disrupted white matter integrity in PSD; however, they are not specific to myelin changes. Using a multimodal imaging approach, the current study aimed to further delineate myelin and microstructural changes in the white matter of a young PSD cohort. We utilized quantitative magnetization transfer (qMT) imagingcombined with advanced diffusion imaging to estimate specific myelin-related biophysical properties in 51 young adult PSD patients compared with 38 age-matched healthy controls. The macromolecular proton fraction (MPF) obtained from qMT was used as a specific marker of myelin content. Additionally, MPF was employed along with diffusion metrics of axonal density (vic) and extra-cellular volume fraction to derive the g-ratio, a measure of relative myelin sheath thickness defined as the ratio of inner to outer axonal diameter. Compared to controls, we observed a widespread MPF reduction and localized g-ratio increase in patients, primarily those with a diagnosis of schizophrenia or depressive schizoaffective disorder. No between-group differences were noted in vic, suggesting similar axonal densities across groups. Correlation analysis revealed that lower MPF was significantly related to poorer working memory performance in PSD, while the HC group showed a positive association for working memory with both g-ratio and vic. The pattern of changes observed in our multimodal imaging markers suggests that PSD, depending on symptomatology, is characterized by specific alterations in white matter integrity and myelin-axonal geometry of major white matter tracts, which may impact working memory function. These findings provide a more detailed view of myelin-related white matter changes in early stages of PSD.

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time.

Microstructural Damage and Repair in the Spinal Cord of Patients With Early Multiple Sclerosis and Association With Disability at 5 Years.

The dynamics of microstructural spinal cord (SC) damage and repair in people with multiple sclerosis (pwMS) and their clinical relevance have yet to be explored. We set out to describe patient-specific profiles of microstructural SC damage and change during the first year after MS diagnosis and to investigate their associations with disability and SC atrophy at 5 years. We performed a longitudinal monocentric cohort study among patients with relapsing-remitting MS: first relapse <1 year, no relapse <1 month, and high initial severity on MRI (>9 T2 lesions on brain MRI and/or initial myelitis). pwMS and age-matched healthy controls (HCs) underwent cervical SC magnetization transfer (MT) imaging at baseline and at 1 year for pwMS. Based on HC data, SC MT ratio z-score maps were computed for each person with MS. An index of microstructural damage was calculated as the proportion of voxels classified as normal at baseline and identified as damaged after 1 year. Similarly, an index of repair was also calculated (voxels classified as damaged at baseline and as normal after 1 year). Linear models including these indices and disability or SC cross-sectional area (CSA) change between baseline and 5 years were implemented. Thirty-seven patients and 19 HCs were included. We observed considerable variability in the extent of microstructural SC damage at baseline (0%-58% of SC voxels). We also observed considerable variability in damage and repair indices over 1 year (0%-31% and 0%-20%), with 18 patients showing predominance of damage and 18 predominance of repair. The index of microstructural damage was associated positively with the Expanded Disability Status Scale score (r = 0.504, p = 0.002) and negatively with CSA change (r = -0.416, p = 0.02) at 5 years, independent of baseline SC lesion volume. People with early relapsing-remitting MS exhibited heterogeneous profiles of microstructural SC damage and repair. Progression of microstructural damage was associated with disability progression and SC atrophy 5 years later. These results indicate a potential for microstructural repair in the SC to prevent disability progression in pwMS.

Live-cell FRET assay on the stoichiometry and affinity of the YAP complexes in MCF-7cells.

Yes-associated protein (YAP), a focal point of current biological research, is involved in regulating various life processes. In this report, live-cell fluorescence resonance energy transfer (FRET) imaging was employed to unravel the YAP complexes in MCF-7cells. Fluorescence imaging of living cells co-expressing CFP (cyan fluorescent protein)-YAP and YFP (yellow fluorescent protein)-LATS1 (large tumor suppressor 1) plasmids revealed that YAP promoted LATS1 oligomerization around mitochondria. Moreover, FRET two-hybrid assay showed that YAP directly interacted with LATS1 to form dimer. Similarly, we found that YAP directly interacted with large tumor suppressor 2 (LATS2) to form a heterotrimer with 1:2 in cytoplasm and around mitochondria. In addition, YAP directly interacted with angiomotin (AMOT) to form a heterodimer in cytoplasm. However, YAP did not interact with O-linked N-acetylglucosamine transferase (OGT). Furthermore, FRET assay also indicated that YAP exhibited a higher affinity with AMOT, followed by LATS1, and least with LATS2. In summary, YAP directly interacts with LATS1 and AMOT to form a heterodimer, with LATS2 to form a heterotrimer with 1:2, and shows a preference for binding to AMOT, followed by LATS1, and lastly LATS2, providing new insights into the Hippo-YAP signaling pathway.

Altered topology in cortical morphometric similarity network in recurrent major depressive disorder

BackgroundRecurrent major depressive disorder (RDD) is increasingly understood to be associated with a 'disconnection' within the brain areas. But, the true understanding of cortical connectivities remains challenging. Morphometric similarity network (MSN) with multi-modal magnetic resonance imaging (MRI) could provide more information about cortical micro-architecture changes in individuals with RDD. MethodsHere, we integrated multi-modal features from T1-weighted imaging, diffusion tensor imaging (DTI), and inhomogeneous magnetization transfer imaging (ihMT) to construct MSN. We used graph theory to calculate topological changes in MSN and explore their relationship with the severity and recurrence. The topological properties of 42 RDD patients were compared with 56 age, sex, and education-matched healthy controls. ResultsRDD subjects showed significantly decreased global efficiency, increased characteristic path length, reduced nodal efficiencies in the parietal lobe, subcortical area, and temporal lobe, increased betweenness centrality in the left supplementary motor area (SMA), decreased intra-modular connections in the parietal module and decreased inter-modular connections between the parietal and prefrontal modules. Notably, the global efficiency, characteristic path length, local efficiency of the right superior parietal gyrus, and inter-modular connections between the parietal and prefrontal modules were significantly associated with the number of depressive episodes. The betweenness centrality in SMA and the intra-modular connections in the parietal module showed a positive relationship with 17-item Hamilton Rating Scale for Depression (HAMD) scores. ConclusionsThe altered topology of MSN may serve as potential underlying pathological mechanisms of RDD. The impaired information integration of the network, particularly the disconnection within the fronto-parietal network, may be associated with the recurrence of depression. The SMA and the fronto-parietal network may be related to the severity of depression.

SAR Image Colorization with a Deep Learning Model for All-Inclusion Understanding (H)

The procedure for enhancing halftone photographs with color to create eye-catching pigmented pictures is known as image colorization. This involves the application of colors in line with the intended purpose of the image. However, traditional methods of colorization available today often include the need for end user’s intervention in the form providing color points and doodle drawings, as well as use of color images for reference in which case colors are transferred to the target image or using multiple color images to predict what the colored result would look like; however, the majority of colorizes developed tend to produce colorized images that are not realistic in most instances due to unskilled users who attempt to apply the devices, wrong color transfer or limited color image library. In this paper, to overcome these weaknesses, a new method that consists of two modules is proposed to colorize certain regions of an image by fusing semantic segmentation and seamless region filling. In the first module, input image foreground and background regions masks and categories are extracted each extracted region's reference image is taken from a per-sorted color image database using a Mask R-CNN model. Using colorization approaches based on the VGG and U-Net models, respectively, the second module entails coloring the image's backdrop and other areas. Afterward, the final, fully colored image is created by combining the photographs using the poission editing process. The tests show that our method enables not only the proper reference image selection based on the semantic information of various image components, but also incorporation of the colored results to create a believable colored image. Proposing to use different CNNbased models in a combination where each is used according to its own strength, In contrast to other methods now in use, our scheme gives a deeper creative visual impression while avoiding the disadvantage of single-step techniques' failure frequency. Keywords: Mask R-CNN, Input Photograph, Image Sections, Coloured Photographs, Conceptual Data, Visuals, Black and White Images, Semantic Pix elation, Reference Picture, Base Image, Images Stockpile, Outlying Areas, Image Library, Segregation Methods, Doodle, Conventional Neural Network Based Designs, U-Net Architecture, One-shot Approach, Strategy Present in This Manuscript, Zonal Regions, Principal Zones, Large Image, Color space, Competitive Half toning Algorithm, Zonal Context, Focus Area, Artistic Incorporation, Partially Automatic Process, Cutting Out Section of the Picture, Instance Level Segmentation

Altered Nigral Amide Proton Transfer Imaging Signal Concordant With Motor Asymmetry in Parkinson's Disease: A Multipool CEST MRI Study.

Asymmetry is a natural characteristic of Parkinson's disease (PD), which can be used to distinguish PD from atypical parkinsonism. Chemical exchange saturation transfer (CEST) has demonstrated value in reflecting the subtle changes related to neuron loss and abnormal protein accumulation in PD but has not been used to investigate asymmetry in PD. This study aimed to examine asymmetrical changes in the mesencephalic nucleus of PD patients with motor asymmetry using four-pool CEST analysis and to explore the relationship between imaging asymmetry and motor asymmetry. Forty-six PD cases with motor asymmetry (PD_MA) and 23 normal controls (NC) were included. The patients were divided into three subgroups based on their conditions: PD with mild motor asymmetry (PD_MMA), PD with severe motor asymmetry (PD_SMA), and hemiparkinsonism (PD_Hemi). Differences among the more affected and less affected sides of cases in various PD subgroups and the NC group were analyzed. Motor laterality and imaging laterality were determined to evaluate the concordance. Motor asymmetry and imaging asymmetry indexes were calculated to evaluate the correlation. Compared with NC, amide proton transfer (APT) was significantly decreased in the more affected sides of substantia nigra (SN) and red nucleus in the PD_MA, PD_SMA, and PD_Hemi groups. In the PD-SMA group, the APT signal in the SN was significantly reduced in the more affected side compared with the less affected side. The imaging asymmetry index in APT for the SN was positively associated with the motor asymmetry index in the PD_SMA group (β = 0.431, p = 0.014). The imaging laterality in APT for the SN had a significant consistency with motor laterality in the PD_SMA group (κ = 0.566, p = 0.001). These findings suggest inherent asymmetry of APT signal in the SN in PD patients with severe asymmetry, with nigral APT potentially serving as a noninvasive biomarker of lateralization in PD.

M.N. Muravyev and the Icons of St. Isaac’s Cathedral in Vilna: Imperial Transfer of Sacral Images?

Introduction. In the Orthodox Nicholas Church in Vilna since the 1860s, there were icons brought from St. Isaac’s Cathedral in St. Petersburg. The reasons, mechanisms, and socio-political context of the transportation of the icons from the capital of the Russian Empire to the Northwestern Krai after the uprising of 1863 were not clear. The purpose of this article is to analyze the complex of archival and published sources and reconstruct the history of sending the icons of St. Isaac’s Cathedral to Vilna, to identify the factors that accompanied this process and to determine the circle of its participants. Methods and materials. The article is based on the archival sources from the funds of the Russian State Historical Archive (f. 472 “Chancery of the Ministry of the Imperial Court,” f. 502 “Cabinet of the Architect Montferrand,” f. 789 “Imperial Academy of Arts,” f. 1311 “Commission of the construction of St. Isaac’s Cathedral”). The study of archival sources and synchronous sources of personal origin and historiography made it possible to reconstruct the motives, the course, and stages of the transportation of icons from St. Isaac’s Cathedral to Vilna churches. Analysis. The solicitation of the Vilna Governor-General M.N. Muravyev about sending to Vilna the picturesque icons replaced by mosaics in St. Isaac’s Cathedral was caused both by his desire for economy and the speedy filling of Vilna churches by Orthodox icons and, apparently, by the very correspondence of the icons (depicting saints whose names coincided with the names of members of the Romanov dynasty) to his own political course aimed at imperial integration and the symbolic conquest of the space of the Northwestern Krai. Results. M.N. Muravyev’s successor, the Governor-General K.P. Kaufman, who perhaps knew about the symbolic unity of Orthodoxy and the monarchy, reflected in the icons of St. Isaac’s Cathedral, made a similar request to the Ministry of the Imperial Court to send the additional icons. However, Alexander II was a supporter of a softer political course in relation to the Northwestern Krai and did not consider the icons of St. Isaac’s Cathedral as an instrument of symbolic and confessional politics, and the practice of sending the icons from Petersburg to Vilna was finished. Funding. The research was supported by the Russian Science Foundation, project № 21-48-04402 “Saints and heroes from Christianization to Nationalism: Symbol, Image, Memory (Nord-West Russia, Baltic and Nordic countries)”.

Amide proton transfer weighted MRI measurements yield consistent and repeatable results in patients with gliomas: a prospective test-retest study.

Chemical exchange saturation transfer (CEST) imaging has emerged as a promising imaging biomarker, but its reliability for clinical practice remains uncertain. This study aimed to investigate the robustness of CEST parameters in healthy volunteers and patients with brain tumours. A total of n = 52 healthy volunteers and n = 52 patients with histologically confirmed glioma underwent two consecutive 3-T MRI scans separated by a 1-min break. The CEST measurements were reconstructed using two models: with and without fluid suppression and included the evaluation of both amide (amidePTw) and amine (aminePTw) offsets. Mean intensity values in healthy volunteers were compared from volumetric segmentations (VOI) of grey matter, white matter, and the whole brain. Mean intensity values in brain tumour patients were assessed from VOI of the contrast-enhancing, non-enhancing and whole tumour, as well as from the normal-appearing white matter. Test-retest reliability was assessed using ICC and Bland-Altman plots. The amidePTw/aminePTw signal intensity distribution was significantly affected by fluid suppression (p < 0.001 for each VOI). Test-retest reliability in healthy volunteers showed fair to excellent agreement (ICC = 0.53-0.74), with the highest signal intensity values observed by amidePTw (ICC = 0.73-0.74). In patients, an excellent agreement of both amidePTw and aminePTw measurements was observed across different tumour regions (ICC = 0.76-0.89), with the highest ICC for contrast-enhancing tumour measurements. Bland-Altman analysis indicated negligible systematic bias and no proportional bias in measurement errors. Measurements from amide/aminePTw imaging obtained from an adequately powered test-retest study yield consistent and reproducible results in glioma patients, as a prerequisite for robust imaging biomarker discovery in neuro-oncology. Question The clinical reliability of chemical exchange saturation transfer imaging remains uncertain, necessitating further investigation to establish its robustness as a biomarker in neuro-oncology. Findings This study demonstrates that amide/amine proton transfer imaging provides repeatable, high-agreement measurements in glioma patients, particularly in contrast-enhancing tumour regions. Clinical relevance This test-retest study demonstrates that chemical exchange saturation transfer imaging using two models and assessing amide and amine offsets yield consistent and repeatable results in glioma patients, as a prerequisite for robust imaging biomarker discovery for neuro-oncology studies and clinical practice.

Innovative imaging techniques for early glioma detection and characterization: a systematic review and meta-analysis

Background: Gliomas, primary intra-axial brain tumors originating from neuroglial cells, pose diagnostic challenges despite advancements in imaging techniques. This systematic review and meta-analysis aimed to evaluate recent innovations in imaging modalities for glioma detection and characterization. Methodology: A comprehensive search of PubMed and Cochrane Library identified studies from 2015 to December 2023. Inclusion criteria encompassed studies on imaging techniques for gliomas, published in peer-reviewed journals. Quality was assessed using the Newcastle-Ottawa Scale. Results: Fifteen studies on glioma grades and imaging techniques were reviewed. Diffusion Tensor Imaging (DTI) was practical for glioma characterization, with Apparent Diffusion (AD) maps accurately detecting malignant transformation and differentiating tumor grades. 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) enhanced glioma identification, particularly when combined with MRI, improving specificity for high-grade tumors. Advanced MRI techniques, such as MR Perfusion Imaging, and Dynamic 18F-FET PET were useful for distinguishing glioma grades and evaluating tumor biology. Amide Proton Transfer Imaging, in conjunction with FDG-PET, also enhanced diagnostic precision. The meta-analysis showed a combined effect size of 0.8622 (95% CI [0.6401; 1.0843]) for ADC in gliomas, indicating a high diagnostic value. Conclusion: Recent advancements in DTI and PET significantly improve glioma detection and characterization, highlighting the need for integrated imaging for accuracy.

Object Detection in Historical Images: Transfer Learning and Pseudo Labelling

The automatic analysis of images in the historical sciences often requires the identification of objects. Object identification is a well-researched problem for modern photographs; however, for historical material, annotations are often necessary. We present a solution for finding objects without manual work. The method consists of a style transfer of images from the COCO dataset into the domain using CycleGAN and training with items obtained through pseudo-labelling on the original and the additional transferred COCO images. Different strategies to assemble the dataset are compared. The best method obtains an F1 score of 0.58 for 15 object types without any labelling.

A ViT-based Method of Stitching Defect Detection for Packaging Bags by Integrating Image Correction and Transfer Learning Solutions

A ViT-based Method of Stitching Defect Detection for Packaging Bags by Integrating Image Correction and Transfer Learning Solutions

An Organ-aware Diagnosis Framework for Radiology Report Generation.

Radiology report generation (RRG) is crucial to save the valuable time of radiologists in drafting the report, therefore increasing their work efficiency. Compared to typical methods that directly transfer image captioning technologies to RRG, our approach incorporates organ-wise priors into the report generation. Specifically, in this paper, we propose Organ-aware Diagnosis (OaD) to generate diagnostic reports containing descriptions of each physiological organ. During training, we first develop a task distillation (TD) module to extract organ-level descriptions from reports. We then introduce an organ-aware report generation module that, for one thing, provides a specific description for each organ, and for another, simulates clinical situations to provide short descriptions for normal cases. Furthermore, we design an auto-balance mask loss to ensure balanced training for normal/abnormal descriptions and various organs simultaneously. Being intuitively reasonable and practically simple, our OaD outperforms SOTA alternatives by large margins on commonly used IU-Xray and MIMIC-CXR datasets, as evidenced by a 3.4% BLEU-1 improvement on MIMIC-CXR and 2.0% BLEU-2 improvement on IU-Xray.

Observation of E-cadherin adherens junction dynamics with metal-induced energy transfer imaging and spectroscopy

Epithelial cadherin (E-cad) mediated cell-cell junctions play a crucial role in the establishment and maintenance of tissues and organs. In this study, we employed metal-induced energy transfer imaging and spectroscopy to investigate variations in intermembrane distance during adhesion between two model membranes adorned with E-cad. By correlating the measured intermembrane distances with the distinct E-cad junction states, we probed the dynamic behavior and diversity of E-cad junctions across different binding pathways. Our observations led to the identification of a transient intermediate state referred to as the X-dimeric state and enabled a detailed analysis of its kinetics. We discovered that the formation of the X-dimer leads to significant membrane displacement, subsequently impacting the formation of other X-dimers. These direct experimental insights into the subtle dynamics of E-cad-modified membranes and the resultant changes in intermembrane distance provide perspectives on the assembly of E-cad junctions between cells. This knowledge enhances our comprehension of tissue and organ development and may serve as a foundation for the development of innovative therapeutic strategies for diseases linked to cell-cell adhesion abnormalities.

The value of amide proton transfer imaging in predicting parametrial invasion and lymph-vascular space invasion of cervical cancer

ObjectiveTo explore the value of amide proton transfer (APT) imaging in assessing parametrial invasion (PMI) and lymph-vascular space invasion (LVSI) of cervical cancer. Materials and methodsWe retrospectively analyzed the clinical and imaging data of cervical cancer patients diagnosed pathologically at our hospital from January 2021 to June 2024. All patients underwent routine magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and APT imaging before treatment. Apparent diffusion coefficient (ADC) and APT values were measured. Based on the pathological results, patients were categorized into LVSI (+) and LVSI (−) groups, and PMI (+) and PMI (−) groups. Independent sample t-tests were used to compare the ADC and APT values between these groups. Receiver operating characteristic (ROC) curves were used to assess the sensitivity, specificity, and area under the curve (AUC) of ADC, APT, and ADC + APT in predicting PMI and LVSI. The Delong test was employed to compare the diagnostic performance among these measures. ResultsA total of 83 patients were included, with 56 in the LVSI (−) group, 27 in the LVSI (+) group, 35 in the PMI (−) group, and 16 in the PMI (+) group. The ADC values for the LVSI (+) and PMI (+) groups were significantly lower than those for the LVSI (−) and PMI (−) groups (P < 0.01). The APT values for the LVSI (+) and PMI (+) groups were significantly higher than those for the LVSI (−) and PMI (−) groups (P < 0.01). The AUC values for ADC, APT, and the combination of ADC + APT in predicting LVSI were 0.839, 0.788, and 0.880, respectively, and in predicting PMI were 0.770, 0.764, and 0.796, respectively. There were no statistically significant differences in the diagnostic performance of ADC, APT, and ADC + APT in predicting PMI. However, the diagnostic performance of ADC + APT in predicting LVSI was significantly better than that of ADC and APT alone (P < 0.01). ConclusionAPT imaging can predict LVSI and PMI status in cervical cancer before surgery. When combined with ADC, its diagnostic accuracy for predicting LVSI is higher than that of APT or ADC alone. This suggests a novel approach for assessing LVSI in cervical cancer.

Use of Image Recognition and Machine Learning for the Automatic and Objective Evaluation of Standstill Marks on Rolling Bearings

One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into six classes. An internal project has now investigated the extent to which this evaluation can be automated and objectified. Images of standstill marks were classified using convolutional neural networks that were implemented with the deep learning library Pytorch. With basic convolutional neural networks, an accuracy of 70.19% for the classification of all six classes and 83.65% for the classification of pairwise classes was achieved. Classification accuracies were improved by image augmentation and transfer learning with pre-trained convolutional neural networks. Overall, an accuracy of 83.65% for the classification of all six standstill mark classes and 91.35% for the classification of pairwise classes was achieved. Since 16 individual marks are generated per test run in a typical quasi standstill test (QSST) of the CCT and the deviation in the prediction of the classification is a maximum of one school grade, the accuracy achieved is already sufficient to carry out a reliable and objective evaluation of the markings.