keyword
https://read.qxmd.com/read/38536580/impact-of-harmonization-on-the-reproducibility-of-mri-radiomic-features-when-using-different-scanners-acquisition-parameters-and-image-pre-processing-techniques-a-phantom-study
#1
JOURNAL ARTICLE
Ghasem Hajianfar, Seyyed Ali Hosseini, Sara Bagherieh, Mehrdad Oveisi, Isaac Shiri, Habib Zaidi
This study investigated the impact of ComBat harmonization on the reproducibility of radiomic features extracted from magnetic resonance images (MRI) acquired on different scanners, using various data acquisition parameters and multiple image pre-processing techniques using a dedicated MRI phantom. Four scanners were used to acquire an MRI of a nonanatomic phantom as part of the TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several inversion durations were employed, including 50, 100, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 ms...
March 27, 2024: Medical & Biological Engineering & Computing
https://read.qxmd.com/read/38535138/an-improved-path-finding-method-for-the-tracking-of-centerlines-of-tortuous-internal-carotid-arteries-in-mr-angiography
#2
JOURNAL ARTICLE
Se-On Kim, Yoon-Chul Kim
Centerline tracking is useful in performing segmental analysis of vessel tortuosity in angiography data. However, a highly tortuous) artery can produce multiple centerlines due to over-segmentation of the artery, resulting in inaccurate path-finding results when using the shortest path-finding algorithm. In this study, the internal carotid arteries (ICAs) from three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF MRA) data were used to demonstrate the effectiveness of a new path-finding method...
February 28, 2024: Journal of Imaging
https://read.qxmd.com/read/38535052/automatic-active-lesion-tracking-in-multiple-sclerosis-using-unsupervised-machine-learning
#3
JOURNAL ARTICLE
Jason Uwaeze, Ponnada A Narayana, Arash Kamali, Vladimir Braverman, Michael A Jacobs, Alireza Akhbardeh
BACKGROUND: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs...
March 16, 2024: Diagnostics
https://read.qxmd.com/read/38534552/dense-multi-scale-graph-convolutional-network-for-knee-joint-cartilage-segmentation
#4
JOURNAL ARTICLE
Christos Chadoulos, Dimitrios Tsaopoulos, Andreas Symeonidis, Serafeim Moustakidis, John Theocharis
In this paper, we propose a dense multi-scale adaptive graph convolutional network ( DMA-GCN ) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image...
March 14, 2024: Bioengineering
https://read.qxmd.com/read/38530733/single-image-based-deep-learning-for-segmentation-of-early-esophageal-cancer-lesions
#5
JOURNAL ARTICLE
Haipeng Li, Dingrui Liu, Yu Zeng, Shuaicheng Liu, Tao Gan, Nini Rao, Jinlin Yang, Bing Zeng
Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework...
March 26, 2024: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/38530384/posterior-cerebral-artery-pca-accessory-pca-hyperplastic-anterior-choroidal-artery-anastomosis-detected-on-magnetic-resonance-angiography
#6
JOURNAL ARTICLE
Akira Uchino, Kazuo Tokushige
PURPOSE: To describe a case of posterior cerebral artery (PCA)-accessory PCA (hyperplastic anterior choroidal artery) anastomosis detected on magnetic resonance angiography. METHODS: A 76-year-old man with a history of cerebral infarction underwent cranial magnetic resonance (MR) imaging and MR angiography of the intracranial region for the evaluation of brain and vascular lesions. The MR machine was a 3-Tesla scanner. MR angiography was performed using a standard three-dimensional time-of-flight technique...
March 26, 2024: Surgical and Radiologic Anatomy: SRA
https://read.qxmd.com/read/38528288/segmentation-based-fusion-of-ct-and-mr-images
#7
JOURNAL ARTICLE
Pragya Gupta, Nishant Jain
In this paper, a segmentation-based image fusion method is proposed for the fusion of MR and CT images to obtain a high contrast fused image that contains complementary information from both input images. The proposed method uses the fuzzy C-mean method to extract information about the skull from the CT image. This skull information is used to extract soft tissue information from the MR image. Both the skull information and the soft tissue information are then fused using the fusion rule. The efficiency of the proposed method over other state-of-the-art fusion methods is analyzed and compared using qualitative and quantitative analysis methods...
March 25, 2024: J Imaging Inform Med
https://read.qxmd.com/read/38527405/self-supervised-learning-for-medical-image-data-with-anatomy-oriented-imaging-planes
#8
JOURNAL ARTICLE
Tianwei Zhang, Dong Wei, Mengmeng Zhu, Shi Gu, Yefeng Zheng
Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e...
March 21, 2024: Medical Image Analysis
https://read.qxmd.com/read/38522915/characteristics-of-suspicious-breast-lesions-visible-only-on-mr-imaging-is-it-possible-to-classify-into-immediate-biopsy-and-careful-observation-groups
#9
JOURNAL ARTICLE
Ryozo Kai, Mitsuhiro Tozaki, Yuya Koike, Aya Nagata, Kanae Taruno, Yoshimitsu Ohgiya
PURPOSE: To investigate the characteristics of suspicious MRI-only visible lesions and to explore the validity of subcategorizing these lesions into the following two groups: lesions that would require immediate biopsy (4Bi) and lesions for which careful clinical follow-up could be recommended (4Fo). METHODS: A retrospective review of 108 MRI-only visible lesions in 106 patients who were diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 between June 2018 and June 2022 at our institution was performed by two radiologists...
March 22, 2024: Magnetic Resonance in Medical Sciences: MRMS
https://read.qxmd.com/read/38522623/fully-automated-contrast-selection-of-joint-bright-and-black-blood-late-gadolinium-enhancement-imaging-for-robust-myocardial-scar-assessment
#10
JOURNAL ARTICLE
Victor de Villedon de Naide, Jean-David Maes, Manuel Villegas-Martinez, Indra Ribal, Aurélien Maillot, Valéry Ozenne, Géraldine Montier, Thibaut Boullé, Soumaya Sridi, Pauline Gut, Thomas Küstner, Matthias Stuber, Hubert Cochet, Aurélien Bustin
PURPOSE: Joint bright- and black-blood MRI techniques provide improved scar localization and contrast. Black-blood contrast is obtained after the visual selection of an optimal inversion time (TI) which often results in uncertainties, inter- and intra-observer variability and increased workload. In this work, we propose an artificial intelligence-based algorithm to enable fully automated TI selection and simplify myocardial scar imaging. METHODS: The proposed algorithm first localizes the left ventricle using a U-Net architecture...
March 22, 2024: Magnetic Resonance Imaging
https://read.qxmd.com/read/38522252/linear-semantic-transformation-for-semi-supervised-medical-image-segmentation
#11
JOURNAL ARTICLE
Cheng Chen, Yunqing Chen, Xiaoheng Li, Huansheng Ning, Ruoxiu Xiao
Medical image segmentation is a focus research and foundation in developing intelligent medical systems. Recently, deep learning for medical image segmentation has become a standard process and succeeded significantly, promoting the development of reconstruction, and surgical planning of disease diagnosis. However, semantic learning is often inefficient owing to the lack of supervision of feature maps, resulting in that high-quality segmentation models always rely on numerous and accurate data annotations. Learning robust semantic representation in latent spaces remains a challenge...
March 21, 2024: Computers in Biology and Medicine
https://read.qxmd.com/read/38520646/deep-learning-based-automatic-pipeline-for-3d-needle-localization-on-intra-procedural-3d-mri
#12
JOURNAL ARTICLE
Wenqi Zhou, Xinzhou Li, Fatemeh Zabihollahy, David S Lu, Holden H Wu
PURPOSE: Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset...
March 23, 2024: International Journal of Computer Assisted Radiology and Surgery
https://read.qxmd.com/read/38519138/evaluation-of-ivim-in-the-spinal-cord-of-multiple-sclerosis-patients
#13
JOURNAL ARTICLE
Brian Johnson, Christine Heales
PURPOSE: To evaluate the ability of intravoxel incoherent motion (IVIM), a perfusion-weighted imaging technique, to differentiate microcirculation changes in the spinal cord of patients with multiple sclerosis (MS) compared with healthy individuals. METHODS: Fifteen healthy individuals and 15 individuals with MS underwent IVIM magnetic resonance (MR) imaging using a 3 T scanner with 2-D axial gradient recalled echo and 2-D axial diffusion-weighted imaging (DWI) sequences...
March 2024: Radiologic Technology
https://read.qxmd.com/read/38517610/deep-learning-based-3d-cerebrovascular-segmentation-workflow-on-bright-and-black-blood-sequences-magnetic-resonance-angiography
#14
JOURNAL ARTICLE
Langtao Zhou, Huiting Wu, Guanghua Luo, Hong Zhou
BACKGROUND: Cerebrovascular diseases have emerged as significant threats to human life and health. Effectively segmenting brain blood vessels has become a crucial scientific challenge. We aimed to develop a fully automated deep learning workflow that achieves accurate 3D segmentation of cerebral blood vessels by incorporating classic convolutional neural networks (CNNs) and transformer models. METHODS: We used a public cerebrovascular segmentation dataset (CSD) containing 45 volumes of 1...
March 22, 2024: Insights Into Imaging
https://read.qxmd.com/read/38516329/fully-automated-mr-based-virtual-biopsy-of-primary-cns-lymphomas
#15
JOURNAL ARTICLE
Vicky Parmar, Johannes Haubold, Luca Salhöfer, Mathias Meetschen, Karsten Wrede, Martin Glas, Maja Guberina, Tobias Blau, Denise Bos, Anisa Kureishi, René Hosch, Felix Nensa, Michael Forsting, Cornelius Deuschl, Lale Umutlu
BACKGROUND: Primary central nervous system lymphomas (PCNSL) pose a challenge as they may mimic gliomas on magnetic resonance imaging (MRI) imaging, compelling precise differentiation for appropriate treatment. This study focuses on developing an automated MRI-based workflow to distinguish between PCNSL and gliomas. METHODS: MRI examinations of 240 therapy-naive patients (141 males and 99 females, mean age: 55.16 years) with cerebral gliomas and PCNSLs (216 gliomas and 24 PCNSLs), each comprising a non-contrast T1-weighted, fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted sequence were included in the study...
2024: Neuro-oncology advances
https://read.qxmd.com/read/38512744/cross-modal-consistency-for-single-modal-mr-image-segmentation
#16
JOURNAL ARTICLE
Wenxuan Xu, Cangxin Li, Yun Bian, Qingquan Meng, Weifang Zhu, Fei Shi, Xinjian Chen, Chengwei Shao, Dehui Xiang
OBJECTIVE: Multi-modal magnetic resonance (MR) image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to obtain multiple modalities for a single patient in clinical applications. To address these issues, a cross-modal consistency framework is proposed for a single-modal MR image segmentation. METHODS: To enable single-modal MR image segmentation in the inference stage, a weighted cross-entropy loss and a pixel-level feature consistency loss are proposed to train the target network with the guidance of the teacher network and the auxiliary network...
March 21, 2024: IEEE Transactions on Bio-medical Engineering
https://read.qxmd.com/read/38506619/deep-learning-based-approach-for-brainstem-and-ventricular-mr-planimetry-application-in-patients-with-progressive-supranuclear-palsy
#17
JOURNAL ARTICLE
Salvatore Nigro, Marco Filardi, Benedetta Tafuri, Martina Nicolardi, Roberto De Blasi, Alessia Giugno, Valentina Gnoni, Giammarco Milella, Daniele Urso, Stefano Zoccolella, Giancarlo Logroscino
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP)...
March 20, 2024: Radiology. Artificial intelligence
https://read.qxmd.com/read/38504982/case-report-a-case-of-novel-homozygous-lrba-variant-induced-by-chromosomal-segmental-uniparental-disomy-genetic-and-clinical-insights
#18
Lihua Jiang, Sen Chen
OBJECTIVE: The study aims to report a rare case of a novel homozygous variant in the LRBA gene, originating from uniparental disomy of paternal origin. This case contributes new clinical data to the LRBA gene variant database. METHODS: The study details the case of a 2-year-old child diagnosed in May 2023 at our center with a homozygous LRBA gene variant. Detailed clinical data of the patient were collected, including whole-exome sequencing of peripheral blood mononuclear cells, with parental genetic verification...
2024: Frontiers in Immunology
https://read.qxmd.com/read/38504164/convolutional-neural-network-based-magnetic-resonance-image-differentiation-of-filum-terminale-ependymomas-from-schwannomas
#19
JOURNAL ARTICLE
Zhaowen Gu, Wenli Dai, Jiarui Chen, Qixuan Jiang, Weiwei Lin, Qiangwei Wang, Jingyin Chen, Chi Gu, Jia Li, Guangyu Ying, Yongjian Zhu
PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets...
March 19, 2024: BMC Cancer
https://read.qxmd.com/read/38504017/a-visual-language-foundation-model-for-computational-pathology
#20
JOURNAL ARTICLE
Ming Y Lu, Bowen Chen, Drew F K Williamson, Richard J Chen, Ivy Liang, Tong Ding, Guillaume Jaume, Igor Odintsov, Long Phi Le, Georg Gerber, Anil V Parwani, Andrew Zhang, Faisal Mahmood
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities...
March 19, 2024: Nature Medicine
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