Merging Images Research Articles

Hybrid cardiovascular imaging. A clinical consensus statement of the european association of nuclear medicine (EANM) and the european association of cardiovascular imaging (EACVI) of the ESC.

Hybrid imaging consists of a combination of two or more imaging modalities, which equally contribute to image information. To date, hybrid cardiovascular imaging can be performed by either merging images acquired on different scanners, or with truly hybrid PET/CT and PET/MR scanners. The European Association of Nuclear Medicine (EANM), and the European Association of Cardiovascular Imaging (EACVI) of the European Society of Cardiology (ESC) aim to review clinical situations that may benefit from the use of hybrid cardiac imaging and provide advice on acquisition protocols providing the most relevant information to reach diagnosis in various clinical situations.

Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images.

Cone photoreceptor inner segments visualized in non-confocal split-detection adaptive optics scanning light ophthalmoscope (AOSLO) images appear as obliquely illuminated domes with bright and dark opposing regions. Previously, the pairing of these bright and dark regions for automated photoreceptor identification has necessitated complex algorithms. Here we demonstrate how the merging of split-detection images captured with a non-confocal quadrant light detection scheme allows automated cone identification using simple, open-source image processing tools, while also improving accuracy in both normal and pathologic retinas.

Remote sensing image fusion method based on depth feature extraction

Abstract Remote sensing image fusion involves combining data from various remote sensing sensors or merging multiple images acquired by the same sensor but in different spectral bands to create a superior quality composite image, high resolution, multi-band, and multi-temporal remote sensing image through certain algorithms and technologies. By utilizing fusion techniques, remote sensing images of varying bands, resolutions, and phases can be combined to enhance the accuracy of ground object information. This paper introduces a remote sensing image fusion method that leverages deep learning for depth feature extraction. Swin Transformer makes full use of the characteristics of the relationship between adjacent pixels, and adaptively extracts panchromatic image (PAN) and multispectral image (MS) features through two adaptive feature extraction channels. High-quality satellite data is employed to confirm the efficacy of the suggested approach, with the findings demonstrating strong performance in ERGAS, SCC, SSIM, and PSNR, and other evaluation indexes and has good spectral information fidelity and spatial information integration degree.

Identifying defects and varieties of Malting Barley Kernels

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach’s advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

Dose-Response Relationships Between Radiation Exposure, Bone Marrow Function as Measured by 18F-Fluorothymidine Positron Emission Tomography, and Lymphocyte Counts During Chemoradiation for Non-Small Cell Lung Cancer

Purpose18F-Fluorothymidine(FLT)-PET enables sensitive imaging of bone marrow (BM) proliferation. Sequential FLT-PET/CT scans before and during chemoradiation (CRT) for non-small cell lung cancer (NSCLC) were repurposed to investigate dose-response effects of radiation on BM proliferation. Methods and materialsTwenty-six NSCLC patients underwent platinum-based CRT to 60Gy in 30 fractions with FLT-PET/CT scans at baseline, week 2 (20Gy) and week 4 (40Gy). FLT uptake in BM was isolated using Medical Image Merge software. Week 2 and week 4 FLT-PET BM scans were fused with contemporaneous radiation isodose distributions. Relationships between radiation dose and FLT BM uptake (SUVmax and visual parameters) were analyzed using generalized-linear and restricted cubic-spline models. Percentage volumes of total BM without appreciable FLT uptake (“ablated”) on week 2 and week 4 FLT-PET scans were calculated by comparisons with baseline scans. ResultsThoracic FLT uptake was ablated in BM regions exposed to cumulative radiation doses ≥3Gy by week 2. In all cases BM FLT SUVmax declined rapidly as radiation dose increased. BM proliferation significantly decreased by more than 95% after ≥3-4Gy at 2 weeks and ≥4-5Gy at 4 weeks. The ablated BM volume increased from week 2 to week 4 as BM in the penumbra accumulated radiation dose. Median percentage of total BM ablated was 13.1% (range 5.6-20.3) at 2 weeks and 15.7% (range 9.2 - 24.1) at 4 weeks. Mean lymphocyte counts fell from baseline of 2.01×109/L to 0.77 week 2, and 0.60 week 4. Lymphocyte decline strongly correlated with percentage of total BM ablated by week 4 (y=-46 -1.64 x, R2adj = 0.34, p=0.001). ConclusionsBM ablation associated with low dose-radiation exposure during CRT correlated significantly with lower week 4 lymphocyte counts. BM is a potential organ-at-risk and reducing the BM volume exposed to ≥3Gy may help preserve lymphocytes essential for effective adjuvant immunotherapy.

Robot-assisted transosseous repair of triangular fibrocartilage complex: a cadaver study.

The feasibility and accuracy of robot-assisted bone tunnel construction in the transosseous repair of the triangular fibrocartilaginous complex (TFCC) were compared with those of freehand arthroscopic repair. A total of 20 cadaveric specimens were randomized into robotic-assisted and arthroscopy-guided groups. Three bone tunnels were constructed in the ulnar foveal region in each specimen. The discrepancy between the planned and actual tunnel exits was determined in the robot-assisted group by merging images. The success rate of tunnel construction, time consumption and number of drilling attempts were compared between groups. The median planned/actual exit discrepancy was 0.8 mm in the robot-assisted group, with 90% of tunnel exits successfully placed in the footprint region, compared to 63.3% in the arthroscopy-guided group. The robot-assisted group spent less time and required fewer drilling attempts to construct bone tunnels. These results indicated that the robot-assisted technique can accurately construct multiple bone tunnels in the foveal region and reduce the difficulty of TFCC transosseous repair.Level of evidence: III.

Cultural and Aesthetic Dynamics of Photography as a Form of Contemporary Art

Art-photography is a form of contemporary art that marked itself in the early 1960s. Its historical evolution has not been sufficiently studied. At the same time, it provides an opportunity to identify and understand the logic of the internal development of art-photography and its relationship with the external cultural context. The cultural and aesthetic dynamics of art-photography is associated with three historical stages: early, mature and late postmodernism (meta modernism) and the corresponding forms of contemporary art. The first stage (1960—1970) saw the realization by contemporary artists of the conceptual possibilities of photography. It was mainly the photographic documentation of process-action forms of contemporary art. The same period saw the birth and formation of the main artistic strategies and creative methodologies of art-photography: adherence to documentaries, the use of staging (directing), the introduction of seriality (typology), the combination of word and image. The second stage (1980s — 1990s) of art-photography functioning is characterized by further development and enrichment of established artistic tendencies and the emergence of new practices in which photographic material expands its subject matter and claims the role of a text that problematizes (thematizes) the represented reality. The development of art-photography at the third stage (2000s to the present) is determined by two main factors: the processes of digitalization and oscillation of contemporary culture and art, which leads to the transformation of the “mode of truth” of the photographic image and the strengthening of the importance of the aesthetics of the individual photographic frame. At the level of practice, this is expressed in the creation of photographic pictures where reality and fantasy images merge into a single whole and become indistinguishable. This effect was reinforced by printing photographs of enormous size and exhibiting them in museum spaces and galleries. Nowadays, art-photography, while remaining a form of contemporary art, seeks to go beyond it and acquire signs of aesthetic self-sufficiency.

A Multi-Input Convolutional Neural Network Model for Electric Motor Mechanical Fault Classification Using Multiple Image Transformation and Merging Methods

This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performance degradation or malfunctions, manifesting as abnormal signals in vibrations or currents. Our research focuses on enhancing the accuracy of fault classification in electric motor facilities, employing innovative image transformation methods—recurrence plots (RPs), the Gramian angular summation field (GASF), and the Gramian angular difference field (GADF)—in conjunction with a multi-input convolutional neural network (CNN) model. We conducted comprehensive experiments using datasets encompassing four types of machinery components: bearings, belts, shafts, and rotors. The results reveal that our multi-input CNN model exhibits exceptional performance in fault classification across all machinery types, significantly outperforming traditional single-input models. This study not only demonstrates the efficacy of advanced image transformation techniques in fault detection but also underscores the potential of multi-input CNN models in industrial fault diagnosis, paving the way for more reliable and efficient monitoring of electric motor machinery.

Neuromorphic Synergy for Video Binarization.

Bimodal objects, such as the checkerboard pattern used in camera calibration, markers for object tracking, and text on road signs, to name a few, are prevalent in our daily lives and serve as a visual form to embed information that can be easily recognized by vision systems. While binarization from intensity images is crucial for extracting the embedded information in the bimodal objects, few previous works consider the task of binarization of blurry images due to the relative motion between the vision sensor and the environment. The blurry images can result in a loss in the binarization quality and thus degrade the downstream applications where the vision system is in motion. Recently, neuromorphic cameras offer new capabilities for alleviating motion blur, but it is non-trivial to first deblur and then binarize the images in a real-time manner. In this work, we propose an event-based binary reconstruction method that leverages the prior knowledge of the bimodal target's properties to perform inference independently in both event space and image space and merge the results from both domains to generate a sharp binary image. We also develop an efficient integration method to propagate this binary image to high frame rate binary video. Finally, we develop a novel method to naturally fuse events and images for unsupervised threshold identification. The proposed method is evaluated in publicly available and our collected data sequence, and shows the proposed method can outperform the SOTA methods to generate high frame rate binary video in real-time on CPU-only devices.

Early response monitoring during [177Lu]Lu-PSMA I&T therapy with quantitated SPECT/CT predicts overall survival of mCRPC patients: subgroup analysis of a Swiss-wide prospective registry study

PurposeTo assess early tumor response with quantitated SPECT/CT and to correlate it with clinical outcome in metastatic castration–resistant prostate cancer (mCRPC) patients treated with 177Lutetium-PSMA I&T therapy.MethodsSingle-center, observational study, part of the prospective Swiss national cancer registry study investigating the safety and efficacy of [177Lu]Lu-PSMA I&T (EKNZ: 2021–01271) in mCRPC patients treated with at least two cycles of [177Lu]Lu-PSMA I&T 6-weekly. After the first and second cycle quantitated SPECT/CT (Symbia Intevo, Siemens) was acquired 48 h after injection (three fields of view from head to thigh, 5 s/frame) and reconstructed using xQuant® (48i, 1 s, 10-mm Gauss). Image analysis: The PSMA-positive total tumor volumes (TTV) were semi-automatically delineated using a SUV threshold of 3 with MIMencore® (version 7.1.3, Medical Image Merge Software Inc.). Changes in TTV, highest tumor SUVmax, and total tumor SUVmean between cycles 1 and 2 were calculated and grouped into a) stable or decrease and b) increase. Serum PSA levels were assessed at each therapy cycle and at follow-up until progression or death. Changes in TTV, PSA, SUVmax, and SUVmean were correlated with PSA-progression-free survival (PSA-PFS) and the overall survival (OS) using the Kaplan–Meier methodology (log-rank test).ResultsBetween 07/2020 and 04/2022, 111 patients were screened and 73 finally included in the data analysis. The median follow-up was 8.9 months (range 1.4–26.6 months). Stable or decreased TTV at cycle 2 was associated with longer OS (hazard ratio (HR) 0.28, 95% confidence interval (CI) 0.09–0.86, p < 0.01). Similar, stable, or decreased PSA was associated with longer OS (HR 0.21; CI 0.07–0.62, p < 0.01) and PSA-PFS (HR 0.34; 95% CI 0.16–0.72, p < 0.01). Combining TTV and PSA will result in an augmented prognostic value for OS (HR 0.09; CI 0.01–0.63; p < 0.01) and for PSA-PFS (HR 0.11; CI 0.02–0.68; p < 0.01). A reduction of SUVmax or SUVmean was not prognostically relevant, neither for OS (p 0.88 and 0.7) nor for PSA-PFS (p 0.73 and 0.62, respectively).ConclusionSix weeks after initiating [177Lu]Lu-PSMA I&T, TTV and serum PSA appear to be good prognosticators for OS. Combined together, TTV + PSA change demonstrates augmented prognostic value and can better predict PSA-PFS. Larger studies using TTV change prospectively as an early-response biomarker are warranted for implementing management change towards a more personalized clinical practice.

Dual-Layer Spectral CT as Innovative Imaging Guidance in Lung Biopsies: Could Color-Coded Z-Effective Images Allow More Diagnostic Samplings and Biomarkers Information?

The aim of the study was to try to obtain more information on diagnostic samplings and biomarkers using dual-layer spectral CT in lung biopsies. Lung biopsies were performed by merging images obtained with CBCT with those from spectral CT to use them as functional guidance, experimenting with double sampling to determine the difference between the area with a higher Z-effective number and that with a lower Z-effective number. Ten patients with large lung lesions on spectral CT were selected and underwent percutaneous transthoracic lung mass biopsy. Technical success was calculated. The percentage of neoplastic, inflammatory, fibrotic, necrotic cells, or non-neoplastic lung parenchyma was reported. The possibility of carrying out immunohistochemical or molecular biology investigations was analyzed. All lesions were results malignant in 10/10 samples in the Zmax areas; in the Zmin areas, malignant cells were found in 7/10 samples. Technical success was achieved in 100% of cases for Zmax sampling and in 70% for Zmin sampling (p-value: 0.2105). The biomolecular profile was detected in 9/10 (90%) cases in Zmax areas, while in 4/10 (40%) cases in Zmin areas (p-value: 0.0573). The advantage of Z-effective imaging would be to identify a region of the lesion that is highly vascularized and probably richer in neoplastic cells, thus decreasing the risk of obtaining a non-diagnostic biopsy sample.

Dense orchard landscape mapping based on image merging with skeleton prediction

To address the difficulty of individual vine-plant segmentation in large orchards, this study proposes a segmentation model for individual canopies that combines the characteristics of tree deciduousness and natural growth patterns. This enables the landscape mapping of orchards under intensive planting conditions. In this study, winter and summer unmanned aerial vehicle (UAV) remote-sensing images of kiwifruit orchards were used for canopy segmentation. It involved: 1) target location identification of tree trunks in winter images and alignment of winter and summer image information; 2) semantic segmentation of branches in winter images; 3) screening of individual skeletons and prediction of branch growth; 4) removal of non-canopy parts of summer images using digital image processing to obtain canopy leaf segmentation results; and 5) segmentation of the extent of the individual plant canopies based on the predicted results of the individual plants. The branch precision rate for the final kiwifruit canopy at the plant level was 99.25% and the recall rate was 95.96%. The average recall rate of the canopy precision segmentation was 82.79%, the accuracy rate of which was 73.31%. This model does not involve plant height information and can implement instance segmentation of deciduous trees of the same species, with similar characteristics, and dense planting. The proposed solution addresses the problem of large-scale standardized planting of the same species, especially those that do not have obvious height or color differences and cannot be segmented using a digital elevation model.

Gradual OCR: An Effective OCR Approach Based on Gradual Detection of Texts

In this paper, we present a novel approach to optical character recognition that incorporates various supplementary techniques, including the gradual detection of texts and gradual filtering of inaccurately recognized texts. To minimize false negatives, we attempt to detect all text by incrementally lowering the relevant thresholds. To mitigate false positives, we implement a novel filtering method that dynamically adjusts based on the confidence levels of recognized texts and their corresponding detection thresholds. Additionally, we use straightforward yet effective strategies to enhance the optical character recognition accuracy and speed, such as upscaling, link refinement, perspective transformation, the merging of cropped images, and simple autoregression. Given our focus on Korean chart data, we compile a mix of real-world and artificial Korean chart datasets for experimentation. Our experimental results show that our approach outperforms Tesseract by approximately 7 to 15 times and EasyOCR by 3 to 5 times in accuracy, as measured using a Jaccard similarity-based error rate on our datasets.

Bounded PCA based Multi Sensor Image Fusion Employing Curvelet Transform Coefficients

The fusion of thermal and visible images acts as an important device for target detection. The quality of the spectral content of the fused image improves with wavelet-based image fusion. However, compared to PCA-based fusion, most wavelet-based methods provide results with a lower spatial resolution. The outcome gets better when the two approaches are combined, but they may still be refined. Compared to wavelets, the curvelet transforms more accurately depict the edges in the image. Enhancing the edges is a smart way to improve spatial resolution and the edges are crucial for interpreting the images. The fusion technique that utilizes curvelets enables the provision of additional data in both spectral and spatial areas concurrently. In this paper, we employ an amalgamation of Curvelet Transform and a Bounded PCA (CTBPCA) method to fuse thermal and visible images. To evidence the enhanced efficiency of our proposed technique, multiple evaluation metrics and comparisons with existing image merging methods are employed. Our approach outperforms others in both qualitative and quantitative analysis, except for runtime performance. Future Enhancement-The study will be based on using the fused image for target recognition. Future work should also focus on this method’s continued improvement and optimization for real-time video processing.

Minimizing vegetation influence on soil salinity mapping with novel bare soil pixels from multi-temporal images

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation.

Forgotten treasures in the HST/FOC UV imaging polarimetric archives of active galactic nuclei

Context. Over its 13 yr of operation (1990–2002), the Faint Object Camera (FOC) on board the Hubble Space Telescope (HST) observed 26 individual active galactic nuclei (AGNs) in ultraviolet (UV) imaging polarimetry. However, not all of the observations have been reduced and analyzed or set within a standardized framework. Aims. We plan to reduce and analyze the AGN observations that have been neglected in the FOC archives using a consistent, novel, and open-access reduction pipeline of our own. We then extend the method to the full AGN sample, thus leading to potential discoveries in the near future. Methods. We developed a new pipeline in Python that will be able to reduce all the FOC observations in imaging polarimetry in a homogeneous way. Most of the previously published reduced observations are dispersed throughout the literature, with the range of different analyses and approaches making it difficult to fully interpret the FOC AGN sample. By standardizing the method, we have enabled a coherent comparison among the different observational sets. Results. In this first paper of a series exploring the full HST/FOC AGN sample, we present an exhaustively detailed account of how to properly reduce the observational data. Current progress in cross-correlation functions, convolution kernels, and a sophisticated merging and smoothing of the various polarization filter images, together with precise propagation of errors, has provided state-of-the-art UV polarimetric maps. We compare our new maps to the benchmark AGN case of NGC 1068 and successfully reproduce the main results previously published, while pushing the polarimetric exploration of this AGN futher, thanks to a finer resolution and a higher signal-to-noise ratio (S/N) than previously reported. We also present, for the first time, an optical polarimetric map of the radio-loud AGN IC 5063 and we examine the complex interactions between the AGN outflows and the surrounding interstellar medium (ISM). Conclusions. Thanks to our newly and standardized reduction pipeline, we were able to explore the full HST/FOC AGN sample, starting with observations that had not been previously published (e.g., IC 5063 here). This pipeline will allow us to make a complete atlas of UV polarimetric images of the 26 unique AGNs observed by the FOC, highlighting the importance and necessity of (imaging) polarimeters for the upcoming new generation of 30-m class telescopes.

Pre-Operative Radiotherapy in STS: Does Downsizing Matter?

Soft tissue sarcomas (STS) are a rare but insidious tumor that arises from mesenchymal tissue. Preoperative RT, followed by resection, is routinely used in extremity and truncal STS. The impact of RT on tumor volume and implications for prognosis is a great concern for patients, as changes in volume is often perceived as a reflection of treatment response. To date, data between tumor volume changes after RT and overall survival (OS) and distant metastasis-free survival (DMFS) in STS is sparse. In this study, we investigate changes in tumor volume after preoperative RT and its impact on OS and DMFS. Between 2000 and 2022, patients with stage I-III primary sarcoma of the extremity or trunk treated with pre-operative RT and wide resection were retrospectively reviewed from a prospective repository. Tumors were contoured using T2 sequences on MRIs within 2 weeks of initiation of RT and within 4 weeks post-RT. Tumor volume was determined using the medical image merge software. Univariate analysis (UVA) was performed using the log-rank test. Multivariate analysis (MVA) was performed using the Cox proportional hazards model. One hundred thirty-two patients had both pre- and post-RT MRIs available for contours. Median follow-up was 6 years. Median age at diagnosis was 56 years (range = 19-92). Most patients had stage III (73%) disease in the lower extremity (75%). The most common histology was undifferentiated sarcoma (30%). Median dose was 50 Gy in 25 fractions. Chemotherapy was administered in 44 (33%) patients. Median tumor volume prior to and after RT was 198 cc (range = 7.49 - 3899.87) and 225.21 cc (range = 0-9879.2), respectively. A decrease in tumor volume was seen in 90 patients (68%) from pre- to post-RT. In these patients, the mean percent change was -35%. Forty-two patients had an increase in tumor volume post-RT with a mean percent change of 56%. Overall local control was 98%. The 2- and 5-year OS were 89% and 76%, respectively. The 2- and 5-year DMFS was 76% and 70%, respectively. On UVA, increased age (p < 0.001), KPS ≤80 (p = 0.002), and smokers (p = 0.009) were associated with decreased OS. No variables were associated with DMFS. On MVA, age (p = 0.01) was associated with decreased OS and female gender (p = 0.03) and high-grade disease was associated with decreased DMFS (p = 0.04) Change in tumor volume did not impact OS (p = 0.15) or DMFS (p = 0.75). The 2- and 5-year DMFS for patients with a decrease in tumor volume was 79% and 71%, respectively. The 2 and 5-year DMFS for patients with an increase in tumor volume was 77% and 70%, respectively. The 2- and 5-year OS for patients with a decrease in tumor volume was 92% and 80%, respectively. The 2 and 5-year OS for patients with an increase in tumor volume was 83% and 69%, respectively. Change in tumor volume did not significantly impact OS and DMFS. Further studies with an increased sample size are warranted to corroborate these findings, however these results may assuage patient anxieties about the perceived tumor response to RT.

Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System.

Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with computed tomography (CT) scan or magnetic resonance imaging (MRI), can help doctors optimize the advantages of each imaging technique. As a result, perceptions serve as valid and valuable assessment criteria. The contrast sensitivity function (CSF), which describes the rates of visual contrast sensitivity through the changing of spatial frequencies, is used mathematically to characterize the human visual system (HVS). As a result, we suggest novel methods for fusing images that use discrete wavelets transform (DWT) based on HVS and CSF models. Images from MRI or CT scan were combined with MRE images, and the outcomes were assessed both subjectively and objectively. Visual inspection of merging images was done throughout the qualitative analysis. The CT-MRE fused images in all four datasets were shown to be superior at maintaining bones and spatial resolution, despite the MRI-MRE being better at exhibiting soft tissues and contrast resolution. It is clear from all four datasets that the liver soft tissue in MRI and CT images mixed successfully with the red-colored stiffness distribution seen in MRE images. The proposed approach outperformed DWT, which produced visual artifacts such as signal loss. Quantitative evaluation using mean, standard deviation, and entropy showed that the generated images from the proposed technique performed better than the source images and DWT. Additionally, peak signal-to-noise ratio, mean square error, correlation coefficient, and structural similarity index measure were employed to compare the two fusion approaches, namely, MRI-MRE and CT-MRE. The comparison did not show the superiority of one approach over the other. In conclusion, both subjective and objective evaluation approaches revealed that the combined images contained more information and characteristics. Hence, the proposed method might be a useful procedure to diagnose and localize the stiffness regions on the liver soft tissue by fusion of MRE with MRI or CT.

DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography.

Neural networks (NNs) have been widely applied in tomographic imaging through data-driven training and image processing. One of the main challenges in using NNs in real medical imaging is the requirement of massive amounts of training data - which are not always available in clinical practice. In this article, we demonstrate that, on the contrary, one can directly execute image reconstruction using NNs without training data. The key idea is to bring in the recently introduced deep image prior (DIP) and merge it with electrical impedance tomography (EIT) reconstruction. DIP provides a novel approach to the regularization of EIT reconstruction problems by compelling the recovered image to be synthesized from a given NN architecture. Then, by relying on the NN's built-in back-propagation and the finite element solver, the conductivity distribution is optimized. Quantitative results based on simulation and experimental data show that the proposed method is an effective unsupervised approach capable of outperforming state-of-the-art alternatives.

Transperineal Prostate Biopsy Targeted by Magnetic Resonance Imaging Cognitive Fusion

Prostate cancer is among the most frequently diagnosed cancers and a leading cause of cancer-related death in men. Currently, the most reliable and widely used imaging test for prostate cancer diagnosis is multiparametric pelvic magnetic resonance imaging (mpMRI). Modern biopsy techniques are based on the computerised merging of ultrasound and MRI images to provide better vision during the biopsy procedure (Fusion Biopsy). However, the method is expensive due to high equipment cost. Cognitive fusion of ultrasound and MRI images has recently emerged as a cheaper and easier alternative to computerised fusion. The aim of this prospective study is to perform an in-patient comparison of the systematic prostate biopsy procedure (SB) vs. cognitive fusion (CF) guided prostate biopsy method in terms of safety, ease of use, cancer detection rate and clinically significant cancer detection. We enrolled 103 patients with suspected prostate cancer that were biopsy naive, with PSA > 4 ng/dL and PIRADS score of 3, 4 or 5. All patients received a transperineal standard 12-18 cores systematic biopsy (SB) and a four-cores targeted cognitive fusion (CF) biopsy. Following the prostate biopsy, 68% of the patients were diagnosed with prostate cancer (70/103 patients). SB diagnosis rate was 62% while CF biopsy was slightly better with a 66% rate. There was a significant 20% increase in clinically significant prostate cancer detection rate for the CF compared to SB (p < 0.05) and a significant prostate cancer risk upgrade from the low to the intermediate risk category (13%, p = 0.041). Transperineal cognitive fusion targeted prostate biopsy is a straightforward biopsy method that is easy to perform and is a safe alternative to standard systematic biopsy with improved significant cancer detection accuracy. A combined targeted and systematic approach should be used for the best diagnostic results.