The last quarter century has witnessed major advancements that have brought biomedical imaging to a paramount status in life sciences. As a prominent example, the National Institute of Biomedical Imaging and Bioengineering (NIBIB) was established in 2000 as the newest institute in the National Institutes of Health (NIH), USA. Generally speaking, the scope of biomedical imaging covers data acquisition, image reconstruction, and image analysis, involving theories, methods, systems, and applications. While tomographic and post-processing techniques become increasingly sophisticated, traditional and emerging modalities play more and more critical roles in anatomical, functional, cellular, and molecular imaging.

Over the last quarter-century, the field of biomedical imaging has undergone significant advancements, establishing its paramount importance in the life sciences. A notable example is the establishment of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) in 2000, which became the newest institute within the National Institutes of Health (NIH) in the USA. Generally speaking, the scope of biomedical imaging includes data acquisition, image reconstruction, and image analysis, involving theories, methods, systems, and applications. As tomographic and post-processing techniques become increasingly sophisticated, both traditional and emerging modalities play increasingly critical roles in anatomical, functional, cellular, and molecular imaging.

在过去的四分之一个世纪中，生物医学成像领域经历了重大的进展，使其在生命科学中占据了重要地位。一个显著的例子是，生物医学成像与生物工程国家研究所（NIBIB）于2000年成立，成为美国国家卫生研究院（NIH）中最新的研究所。一般而言，生物医学成像的范围涵盖数据采集、图像重建和图像分析，涉及理论、方法、系统和应用。随着层析成像和后处理技术的日益复杂，传统和新兴成像方式在解剖、功能、细胞和分子成像中扮演着越来越重要的角色。

International Journal of Biomedical Imaging

MRI is an important tool for accurate detection and targeted biopsy of prostate lesions. However, the imaging appearances of some prostate cancers are similar to those of the surrounding normal tissue on MRI, which are referred to as MRI-invisible prostate cancers (MIPCas). The detection of MIPCas remains challenging and requires extensive systematic biopsy for identification. In this study, we developed a weakly supervised UNet (WSUNet) to detect MIPCas.

Yao Zheng

Jingliang Zhang

Dong Huang

Xiaoshuo Hao

Weijun Qin

Yang Liu

Detecting MRI-Invisible Prostate Cancers Using a Weakly Supervised Deep Learning Model

使用弱监督深度学习模型检测MRI隐匿性前列腺癌

Fast, accurate, and affordable bacterial identification methods are paramount for the timely treatment of infections, especially in resource‐limited settings (RLS). However, today, only 1.3% of the sub‐Saharan African diagnostic laboratories are performing clinical bacteriology. To improve this, diagnostic tools for RLS should prioritize simplicity, affordability, and ease of maintenance, as opposed to the costly equipment utilized for bacterial identification in high‐income countries, such as matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS). In this work, we present a new high‐throughput approach based on a simple wide‐field (864 mm 2 ) lensless imaging system allowing for the acquisition of a large portion of a Petri dish coupled with a supervised deep learning algorithm for identification at the bacterial colony scale. This wide‐field imaging system is particularly well suited to RLS since it includes neither moving mechanical parts nor optics. We validated this approach through the acquisition and the subsequent analysis of a dataset comprising 252 clinical isolates from five species, encompassing some of the most prevalent pathogens. The resulting optical morphotypes exhibited intra‐ and interspecies variability, a scenario considerably more akin to real‐world clinical practice than the one achievable by solely concentrating on reference strains. Despite this variability, high identification performance was achieved with a correct species identification rate of 91.7%. These results open up some new prospects for identification in RLS. We released both the acquired dataset and the trained identification algorithm in publicly available repositories.

Clément Douarre

Dylan David

Marco Fangazio

Emmanuel Picard

Emmanuel Hadji

Olivier Vandenberg

Barbara Barbé

Liselotte Hardy

Pierre R. Marcoux

Simple Imaging System for Label‐Free Identification of Bacterial Pathogens in Resource‐Limited Settings

资源有限环境下无标记细菌病原体简易成像识别系统

Introduction: Pulmonary transit time (PTT) is the time it takes blood to pass from the right ventricle to the left ventricle via the pulmonary circulation, making it a potentially useful marker for heart failure. We assessed the association of PTT with diastolic dysfunction (DD) and mitral valve regurgitation (MVR). Methods: We evaluated routine stress perfusion cardiovascular magnetic resonance (CMR) scans in 83 patients including assessment of PTT with simultaneously available echocardiographic assessment. Relevant DD and MVR were defined as exceeding Grade I (impaired relaxation and mild regurgitation). PTT was determined from CMR rest perfusion scans. Normalized PTT (nPTT), adjusted for heart rate, was calculated using Bazett’s formula. Results: Higher PTT and nPTT values were associated with higher grade DD and MVR. The diagnostic accuracy for the prediction of DD as quantified by the area under the ROC curve (AUC) was 0.73 (CI 0.61–0.85; p = 0.001) for PTT and 0.81 (CI 0.71–0.89; p &lt; 0.001) for nPTT. For MVR, the diagnostic performance amounted to an AUC of 0.80 (CI 0.68–0.92; p &lt; 0.001) for PTT and 0.78 (CI 0.65–0.90; p &lt; 0.001) for nPTT. PTT values &lt; 8 s rule out the presence of DD and MVR with a probability of 70% (negative predictive value 78%). Conclusion: CMR‐derived PTT is a readily obtainable hemodynamic parameter. It is elevated in patients with DD and moderate to severe MVR. Low PTT values make the presence of DD and MVR—as assessed by echocardiography—unlikely.

Martin Segeroth

David Jean Winkel

Beat A. Kaufmann

Ivo Strebel

Shan Yang

Joshy Cyriac

Jakob Wasserthal

Michael Bach

Pedro Lopez-Ayala

Alexander Sauter

Christian Mueller

Jens Bremerich

Michael Zellweger

Philip Haaf

Noninvasive Assessment of Cardiopulmonary Hemodynamics Using Cardiovascular Magnetic Resonance Pulmonary Transit Time

无创评估心肺血流动力学使用心血管磁共振肺通过时间

The study of biomedical imaging and biological surfaces is a rapidly growing interdisciplinary field that has attracted considerable interest from mathematical, engineering, and medicine communities. Many research problems in this field are application oriented and thus the results have practical values but are challenging due to physical and biological constraints and the large scale nature of massive biomedical and biomolecular data. To efficiently solve these problems, advanced mathematical models and fast and efficient computational algorithms are indispensable tools. This special issue was called to address mathematical difficulties and challenges in image and surface analysis. We would like to share with the readers the recent advances in topics such as topological-gradients-based edge/contour detection, partial differential-equation-transform-based feature separation, blind multiple-source reconstruction in bioluminescence tomography, partial-differential-equation-based cerebral cortex reconstruction, nonlinear elasto-mammography for characterization of breast tissue properties, and protein surface characterization.

We would like to thank the authors for their excellent contributions and patience that make this special issue possible. The time, effort, and valuable work of all anonymous reviewers on these papers are also very greatly acknowledged. This special issue constitutes ten papers.

The paper entitled “Contour detection and completion for inpainting and segmentation based on topological gradient and fast marching algorithms” by D. Auroux et al. introduces a contour functional that is based on topological gradient and couples it with a fast marching algorithm to determine the minimal path for the purpose of generating connected contours. This offers a hybrid scheme for edge detection and contour completion. Two specific applications are considered for image processing. For image segmentation, the topological gradient is shown to be more efficient than the standard gradient approaches. For image inpainting, the hybrid scheme particularly improves the quality of the inpainted images.

The paper entitled “Protein surface characterization using an invariant descriptor” by Z. Abu Deeb et al. develops a new invariant descriptor for the characterization of protein surfaces. It is suitable for various analysis tasks, such as protein functional classification and search and retrieval of protein surfaces over a large database. Its novelty is the combination of the power of residue-distance cooccurrence-based local and global surface descriptors. The proposed method not only reduces the computational complexity of matching 3D structures, but also facilitates direct comparison between protein structures of different sizes. The comparison with other methods on three protein families indicates that this method is effective.

The paper entitled “Extending local canonical correlation analysis to handle general linear contrasts for fMRI data” by M. Jin et al. designs a novel test statistics to enable canonical correlation analysis (CCA) and to handle general linear contrasts in more complicated fMRI paradigms. This approach avoids the reparameterization of the design matrix and the reestimation of the CCA solutions for each particular contrast of interest. This test statistics is more powerful than the traditional t-test in general linear models on the inference of evoked brain regional activations from noisy fMRI data, especially for weakly evoked and localized brain activations. The method improves detection power with acceptable computation time and has potential to meet the needs in recent fMRI where data is enormous, signal is weak, and the spatial correlation is strong.

The paper entitled “A novel FEM-based numerical solver for interactive catheter simulation in virtual catheterization” by S. Li et al. concerns with interactive simulation of the deformable catheters and guide-wires in virtual vascular interventional surgeries. The motion of catheters or guide-wires and their interactions with patients' vascular system are mathematically formulated in terms of a total potential energy, consisting of bending elastic energy, vessel wall deformation energy, and the work by the external forces. The minimization of the potential energy functional is numerically realized via a finite element simulation. Experimental studies indicate that the proposed method can realistically model and simulate deformable catheters and guide-wires in an interactive manner.

The paper entitled “Cortical surface reconstruction from high-resolution MR brain images” by S. Osechinskiy et al. presents a new PDE-based approach that readily scales with imaging resolution for reconstructing the cerebral cortex from MR images. The scalability virtue of the approach makes it promising in brain imaging research where high-resolution MRI becomes more popular. This scalability is achieved by using an implicit deformable surface model in a fast marching framework guided by a novel, computationally efficient model using potential field mapping. The method requires much lower computational resources and allows much faster computations than conventional methods.

The paper entitled “Serial FEM/XFEM-based update of preoperative brain images using intraoperative MRI” by L. Vigneron et al. aims to overcome the limitation of current neuronavigation systems that cannot adapt to changing intraoperative conditions over time. The authors develop a complete 3D framework for serial preoperative images updated in the presence of brain shift followed by successive resections. The key ingredient of the system is a nonrigid registration technique using a biomechanical model driven by the deformations of key surfaces tracked in successive intraoperative images. Numerical results demonstrate that the present approach significantly improves the alignment of nonrigidly registered images.

The paper entitled “Selective extraction of entangled textures via adaptive PDE transform” by Y. Wang et al. presents a new adaptive algorithm for selective extraction of entangled textures. Texture characterization and analysis are complicated for images with spatial entanglement, orientation mixing, and high-frequency overlapping. Based on a recently developed PDE transform method for functional mode decomposition, the statistical variance of the local variation is adaptively incorporated in the PDE transform framework for separating textures of very similar features. Successful texture separation is attained for several benchmark images.

The paper entitled “Nonlinear elasto-mammography for characterization of breast tissue properties” by Z. G. Wang et al. extends their previous studies by incorporating the projection of displace information obtained from the conventional X-ray mammography into a nonlinear elastography framework. In particular, projection-type displacement measurements are considered before and after breast compression, and a revised adjoint gradient method is derived for calculating the gradient of the objective function in the nonlinear elasto-mammography framework. Simulations based on a three-dimensional breast phantom involving normal and cancerous tissues are conducted to validate the feasibility and robustness of the proposed approach.

The paper entitled “Fracture detection in traumatic pelvic CT images” by J. Wu et al. presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans. It uses adaptive windowing, boundary tracing, and wavelet transform, while incorporating anatomical information. Fracture detection is performed based on the results of prior pelvic bone segmentation via their registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately. Once verified with more data, the proposed method has the potential to be an important component of a larger modular system to extract features from CT images for a computer-assisted decision making system.

The paper entitled “A finite element mesh aggregating approach to multiple-source reconstruction in bioluminescence tomography” by J. Yu et al. develops a finite element mesh aggregating algorithm for blind multiple-source reconstruction in bioluminescence tomography. Without knowing the number of the sources in advance, an iterative procedure is utilized to detect multiple sources by exploiting the spatial structure of the nodes in finite element meshes and the characteristics of the energy decay. The detecting algorithm is formulated in a flexible reconstruction framework, where a variety of regularizers and inversion algorithms can be chosen by the user. Simulations using a tissue-like phantom demonstrate an improved performance of the new algorithm in terms of the automatic estimation of both locations and densities of multiple sources that differ greatly in power.


Weihong Guo

Lalita Udpa

Yang Wang

Guowei Wei

Shan Zhao

Weihong Guo

Lalita Udpa

Yang Wang

Guowei Wei

Shan Zhao

Mathematical Methods for Images and Surfaces 2011

2011年图像与曲面的数学方法

Background: Three‐dimensional gradient‐echo (3D‐GRE) sequences provide isotropic or nearly isotropic 3D images, leading to better visualization of smaller structures, compared to two‐dimensional (2D) sequences. The aim of this study was to prospectively compare 2D and 3D‐GRE sequences in terms of key imaging metrics, including signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), glenohumeral joint space, image quality, artifacts, and acquisition time in shoulder joint images, using 1.5‐T MRI scanner. Methods: Thirty‐five normal volunteers with no history of shoulder disorders prospectively underwent a shoulder MRI examination with conventional 2D sequences, including T 1 ‐ and T 2 ‐weighted fast spin echo (T 1 /T 2 w FSE) as well as proton density‐weighted FSE with fat saturation (PD‐FS) followed by 3D‐GRE sequences including VIBE, TRUEFISP, DESS, and MEDIC techniques. Two independent reviewers assessed all images of the shoulder joints. Pearson correlation coefficient and intra‐RR were used for reliability test. Results: Among 3D‐GRE sequences, TRUEFISP showed significantly the best CNR between cartilage‐bone (31.37 ± 2.57, p &lt; 0.05) and cartilage‐muscle (13.51 ± 1.14, p &lt; 0.05). TRUEFISP also showed the highest SNR for cartilage (41.65 ± 2.19, p &lt; 0.01) and muscle (26.71 ± 0.79, p &lt; 0.05). Furthermore, 3D‐GRE sequences showed significantly higher image quality, compared to 2D sequences ( p &lt; 0.001). Moreover, the acquisition time of the 3D‐GRE sequences was considerably shorter than the total acquisition time of PD‐FS sequences in three orientations ( p &lt; 0.01). Conclusions: 3D‐GRE sequences provide superior image quality and efficiency for evaluating articular joints, particularly in shoulder imaging. The TRUEFISP technique offers the best contrast and signal quality, making it a valuable tool in clinical practice.

Shapoor Shirani

Najmeh-Sadat Mousavi

Milad Ali Talib

Mohammad Ali Bagheri

Elahe Jazayeri Gharebagh

Qasim Abdulsahib Jaafar Hameed

Sadegh Dehghani

Comparison of 3D Gradient‐Echo Versus 2D Sequences for Assessing Shoulder Joint Image Quality in MRI

3D梯度回波与2D序列在评估肩关节MRI图像质量中的比较

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis.

Kenan Morani

Esra Kaya Ayana

Dimitrios Kollias

Devrim Unay

COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier

基于切片处理技术和改进Xception分类器的CT图像COVID-19检测

We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer. A raw magnetic resonance imaging dataset is used for network training and testing; the data contain various motion artifacts with ground truth images of the same subjects. The results were compared to six state-of-the-art MRI image motion correction methods using two types of motions. When motions were brief (within 5 s), the method reduced the average normalized root mean square error (NRMSE) from 45.25% to 17.51%, increased the mean structural similarity index measure (SSIM) from 79.43% to 91.72%, and increased the peak signal-to-noise ratio (PSNR) from 18.24 to 26.57 dB. Similarly, when motions were extended from 5 to 10 s, our approach decreased the average NRMSE from 60.30% to 21.04%, improved the mean SSIM from 33.86% to 90.33%, and increased the PSNR from 15.64 to 24.99 dB. The anatomical structures of the corrected images and the motion-free brain data were similar.

Md. Biddut Hossain

Rupali Kiran Shinde

Shariar Md Imtiaz

F. M. Fahmid Hossain

Seok-Hee Jeon

Ki-Chul Kwon

Nam Kim

Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction

Swin Transformer与Unet架构用于磁共振图像重建中的运动伪影校正

Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the “ImageNet” collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model’s generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model’s outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"><a:mi>F</a:mi><a:mn>1</a:mn></a:math> -score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes.

N. I. Md. Ashafuddula

Rafiqul Islam

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

ContourTL-Net：基于轮廓的迁移学习算法用于早期脑肿瘤检测

A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an

Wei-Wen Chen

Ling Kuo

Yi-Xun Lin

Wen-Chung Yu

Chien-Chao Tseng

Yenn-Jiang Lin

Ching-Chun Huang

Shih-Lin Chang

Jacky Chung-Hao Wu

Chun-Ku Chen

Ching-Yao Weng

Siwa Chan

Wei-Wen Lin

Yu-Cheng Hsieh

Ming-Chih Lin

Yun-Ching Fu

Tsung Chen

Shih-Ann Chen

Henry Horng-Shing Lu

A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance

一种利用心脏磁共振电影成像技术从肥厚型心肌病中分类法布里心肌病的深度学习方法

Purpose. The Gram-positive Staphylococcus aureus bacterium is one of the leading causes of infection in humans. The lack of specific noninvasive techniques for diagnosis of staphylococcal infection together with the severity of its associated complications support the need for new specific and selective diagnostic tools. This work presents the successful synthesis of an immunotracer that targets the α-toxin released by S. aureus. Methods. [89Zr]Zr-DFO-ToxAb was synthesized based on radiolabeling an anti-α-toxin antibody with zirconium-89. The physicochemical characterization of the immunotracer was performed by high-performance liquid chromatography (HPLC), radio-thin layer chromatography (radio-TLC), and electrophoretic analysis. Its diagnostic ability was evaluated in vivo by positron emission tomography/computed tomography (PET/CT) imaging in an animal model of local infection-inflammation (active S. aureus vs. heat-killed S. aureus) and infective osteoarthritis. Results. Chemical characterization of the tracer established the high radiochemical yield and purity of the tracer while maintaining antibody integrity. In vivo PET/CT image confirmed the ability of the tracer to detect active foci of S. aureus. Those results were supported by ex vivo biodistribution studies, autoradiography, and histology, which confirmed the ability of [89Zr]Zr-DFO-ToxAb to detect staphylococcal infectious foci, avoiding false-positives derived from inflammatory processes. Conclusions. We have developed an immuno-PET tracer capable of detecting S. aureus infections based on a radiolabeled antibody specific for the staphylococcal alpha toxins. The in vivo assessment of [89Zr]Zr-DFO-ToxAb confirmed its ability to selectively detect staphylococcal infectious foci, allowing us to discern between infectious and inflammatory processes.

María Isabel González

Mario González-Arjona

Lorena Cussó

Miguel Ángel Morcillo

John Jairo Aguilera-Correa

Jaime Esteban

Martha Kestler

Daniel Calle

Carlos Cerón

Marta Cortes-Canteli

Patricia Muñoz

Emilio Bouza

Manuel Desco

Beatriz Salinas

In Vivo Detection of Staphylococcus aureus Infections Using Radiolabeled Antibodies Specific for Bacterial Toxins

使用放射性标记的针对细菌毒素的抗体进行金黄色葡萄球菌感染的在体检测

Abbreviation	Int. J. Biomed. Imaging
Journal Impact	3.19
Quartiles(Global)	ENGINEERING, BIOMEDICAL(Q2)

ISSN	1687-4188, 1687-4196
h-index	50

Publication Information	Publisher: Hindawi Publishing Corporation，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2006，Proportion of original research papers: 90.91%，Self Citation Rate:0.00%， Gold OA Rate: 100.00%
Journal Fees	$950Details
Average review cycle	网友分享经验：20 Weeks

International Journal of Biomedical Imaging

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