Article
Computer Science, Interdisciplinary Applications
Rafael C. Schick, Thomas Koehler, Wolfgang Noichl, Fabio De Marco, Konstantin Willer, Theresa Urban, Manuela Frank, Thomas Pralow, Ingo Maack, Sven Prevrhal, Bernd Lundt, Alexander Fingerle, Daniela Pfeiffer, Julia Herzen, Franz Pfeiffer
Summary: Dark-field radiography is a promising imaging technique for early diagnosis of lung diseases, but prolonged image acquisition time can lead to motion artifacts. A motion artifact reduction algorithm was developed and successfully evaluated in simulated chest phantom and in vivo human data, significantly improving the image quality of dark-field chest X-ray radiographs by reducing motion artifacts.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Optics
Mengnan Liu, Han Yu, Xiaoqi Xi, Siyu Tan, Linlin Zhu, Zhicun Zhang, Jian Chen, Bin Yan
Summary: Laboratory nanocomputed tomography (nano-CT) with a spatial resolution of up to 100 nm is widely used, but drift artifacts from long-time scanning reduce its spatial resolution. Existing correction methods using sparse projections are affected by the high noise and contrast differences in nano-CT. A proposed method combining gray and frequency domain features improves drift estimation accuracy by 5x and 16x compared to mainstream methods. The proposed method effectively enhances the imaging quality of nano-CT.
Article
Radiology, Nuclear Medicine & Medical Imaging
Leilei Zhou, Hao Liu, Yi-Xuan Zou, Guozhi Zhang, Bin Su, Liyan Lu, Yu-Chen Chen, Xindao Yin, Hong-Bing Jiang
Summary: This study evaluated the clinical performance of an artificial intelligence-based motion correction reconstruction algorithm for cerebral CT. The results showed that the algorithm significantly improved image quality and lesion detectability.
EUROPEAN RADIOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Luca Brombal, Lucia Mariel Arana Pena, Fulvia Arfelli, Renata Longo, Francesco Brun, Adriano Contillo, Francesca Di Lillo, Giuliana Tromba, Vittorio Di Trapani, Sandro Donato, Ralf Hendrik Menk, Luigi Rigon
Summary: The SYRMA-3D collaboration is establishing a breast computed tomography (bCT) clinical program at the Elettra synchrotron radiation facility in Trieste, Italy. This study evaluates and compensates for motion artifacts in synchrotron radiation bCT through an optical tracking approach. Results show that optical tracking procedure can effectively correct motion artifacts and improve the quality of reconstructed CT images.
Article
Materials Science, Characterization & Testing
Markus Wedekind, Susana Castillo, Marcus Magnor
Summary: This article introduces a method for correcting ring artifacts in computed tomography (CT) reconstruction. The method compensates for errors in the gain and offset values of each pixel and reduces blur by inferring information from neighboring pixels. Experimental results show that this method effectively mitigates the shortcomings of purely offset-based approaches and approaches using all projections, and can be efficiently implemented.
JOURNAL OF NONDESTRUCTIVE EVALUATION
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Bo Li, Ningzhi Li, Ze Wang, Radu Balan, Thomas Ernst
Summary: An improved method for image reconstruction of simultaneous multislice EPI scans of the brain with motion correction was proposed and evaluated. The method includes updating receiver phase and resampling coil sensitivities to correct motion-induced artifacts and improve temporal SNR.
MAGNETIC RESONANCE IN MEDICINE
(2023)
Article
Engineering, Biomedical
Yahya Moshaei-Nezhad, Ronald Tetzlaff, Matthias Kirsch
Summary: This paper presents a new method for correcting respiration and heartbeat motion artifacts in neurosurgery thermographic images. The method consists of two main steps: separating the local wavelets of the phase using a complex steerable pyramid, and carrying out temporal filtering followed by image reconstruction. The optimized and adjusted-combined local and global (OA-CLG) method is applied to detect and compensate for the motion artifacts. The proposed method preserves the image structures, properties, spatial resolution, and temperature values, as demonstrated by the evaluation on 10 clinical thermographic datasets.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Interdisciplinary Applications
Zheng Sun, Jiejie Du, Yue Yao, Qi Meng, Huifeng Sun
Summary: A deep learning-based method is proposed to directly correct motion artifacts in non-gated IVPA pullback sequences. The frames are classified into dynamic and static frames, and a neural network called MAC-Net is designed to correct motion in dynamic frames. The trained network can directly correct motion while preserving the original structures without discarding any frames.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Review
Engineering, Biomedical
F. Lamare, A. Bousse, K. Thielemans, C. Liu, T. Merlin, H. Fayad, D. Visvikis
Summary: This article provides a comprehensive overview of the development in the field of PET respiratory motion correction, covering different multimodality imaging devices and approaches including synchronization, estimation, and motion correction.
PHYSICS IN MEDICINE AND BIOLOGY
(2022)
Article
Engineering, Biomedical
B. Denis de Senneville, P. Coupe, M. Ries, L. Facq, C. T. W. Moonen
Summary: This study evaluates deep learning for on-line correction of motion related errors in abdominal MR-thermometry using a convolutional neural network designed during a preparative learning stage.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Chemistry, Physical
Allison R. Pessoa, Jefferson A. O. Galindo, Luiz F. dos Santos, Rogeria R. Goncalves, Stefan A. Maier, Leonardo de S. Menezes, Anderson M. Amaral
Summary: Lanthanide-doped single dielectric nanoparticles are used for nanoscale temperature sensing with high resolution. However, the low number of emitters in individual nanocrystals requires higher excitation power densities, which can cause overlapping emissions and affect temperature measurements. This study demonstrates a method to separate and correct these overlapping bands, resulting in improved temperature readout accuracy and corrected thermal artifacts.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)
Article
Computer Science, Artificial Intelligence
Cuihong Fan, Weina Fu, Shuai Liu
Summary: This paper studies a non-rigid 3D reconstruction and high-precision correction method for motion pose, using a non-rigid imaging model and a hybrid neural network to adjust the pose, achieving high-precision reconstruction of non-rigid 3D motion pose.
CONNECTION SCIENCE
(2022)
Article
Acoustics
Yuanyuan Wang, Xingyue Wei, Zonghui Pan, Lijie Huang, Qiong He, Jianwen Luo
Summary: Increasing PRF can reduce motion artifacts in strain estimation, while increasing NSA can cause larger motion artifacts, with a recommended NSA of 3 to balance the influences of motion artifacts and improvement in strain estimation. MSA has little influence on motion artifacts but can improve lateral estimation performance, with a recommendation of MSA of 15.
Article
Chemistry, Analytical
Guoce Hu, Jun Wang, Huaxia Deng, Mengchao Ma, Xiang Zhong
Summary: Phase-shift profilometry (PSP) shows promise for high-precision 3D shape measurements. However, the movement of objects introduces artifacts that significantly affect the accuracy of measurement. In this study, we propose a method to reduce motion artifacts using feature information in the image and simulate it using the six-step term shift method. Experimental results demonstrate the effectiveness of the method in reducing motion-induced phase shifts.
Article
Radiology, Nuclear Medicine & Medical Imaging
Ruiqi Geng, Yuxin Zhang, Jitka Starekova, David R. Rutkowski, Lloyd Estkowski, Alejandro Roldan-Alzate, Diego Hernando
Summary: The study found that cardiovascular motion can lead to ADC bias in pancreas DWI, which can be addressed by using motion-robust gradient waveforms. In healthy volunteers, motion-robust DWI methods were able to avoid heterogeneous DW signals and variable ADC across the pancreas, improving the accuracy of pancreas DWI.
MAGNETIC RESONANCE IN MEDICINE
(2021)
Article
Gastroenterology & Hepatology
Francois Avila, Benedicte Caron, Gabriela Hossu, Khalid Ambarki, Stephan Kannengiesser, Freddy Odille, Jacques Felblinger, Silvio Danese, Myriam Choukour, Valerie Laurent, Laurent Peyrin-Biroulet
Summary: The study assessed the accuracy of magnetic resonance elastography in detecting intestinal fibrosis and predicting clinical course in patients with Crohn's disease. The results showed that magnetic resonance elastography is a reliable tool for detecting intestinal fibrosis and predicting a worse disease outcome.
DIGESTIVE DISEASES AND SCIENCES
(2022)
Article
Surgery
Julie F. Leclerc, Francois Avila, Gabriela Hossu, Jacques Felblinger, Ahmet Ayav, Valerie Laurent
Summary: This study provides an overview of variations of the hepatic artery from the origin to the segmental branching using abdominal computed tomography. The results show multiple anatomical patterns and emphasize the importance of dedicated thin-section imaging for preoperative planning in liver surgery.
AMERICAN JOURNAL OF SURGERY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Karyna Isaieva, Marc Fauvel, Nicolas Weber, Pierre-Andre Vuissoz, Jacques Felblinger, Julien Oster, Freddy Odille
Summary: This study proposes a hardware and software system for acquiring data from external devices during MRI imaging, for online or offline use. The system has shown good performance and versatility for a wide range of MRI applications.
MAGNETIC RESONANCE IN MEDICINE
(2022)
Article
Medicine, General & Internal
Anne-Lise Le Bars, Kevin Moulin, Daniel B. Ennis, Jacques Felblinger, Bailiang Chen, Freddy Odille
Summary: This study proposes a super-resolution technique for imaging myocardial fiber architecture using cardiac magnetic resonance. By combining low-resolution image stacks, high-resolution images with improved image quality and accuracy in evaluating myocardial fiber structure are obtained. The technique shows promising results in both experimental and clinical tests.
Article
Engineering, Biomedical
Mously Dior Diaw, Stephane Papelier, Alexandre Durand-Salmon, Jacques Felblinger, Julien Oster
Summary: This study investigates the robustness of convolutional neural networks (CNNs) for QT measurement. By training 3 CNN-based deep learning models and testing them on four external databases, the results show that the deep learning models are more accurate than the state-of-the-art wavelet-based algorithm for QT measurement, especially in drug clinical trials.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Editorial Material
Radiology, Nuclear Medicine & Medical Imaging
Pierre-Andre Vuissoz
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Chemistry, Analytical
Maroua Mehri, Guillaume Calmon, Freddy Odille, Julien Oster
Summary: Deep learning architectures can accurately detect R-peaks in 3D vectorcardiograms without the need for pre-processing or post-processing steps. Experimental results on four different public databases showed that this approach significantly reduces false detections and does not increase the number of missed peaks, making it valuable for devices that require high precision.
Article
Radiology, Nuclear Medicine & Medical Imaging
Camille Meullenet, Karyna Isaieva, Freddy Odille, Claire Dessale, Jacques Felblinger, Philippe Henrot
Summary: This study evaluated the image quality of T2-weighted supine breast MRI and compared it with standard prone MRI. The use of the generalized reconstruction by inversion of coupled systems method improved the image quality of supine breast MRI, but further evaluation with more patients and target lesions is needed.
CURRENT PROBLEMS IN DIAGNOSTIC RADIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Karyna Isaieva, Camille Meullenet, Pierre-Andre Vuissoz, Marc Fauvel, Lena Nohava, Elmar Laistler, Mohamed Aziz Zeroual, Philippe Henrot, Jacques Felblinger, Freddy Odille
Summary: Traditional breast MRI is performed with a specialized coil in the prone position, but supine breast MRI with motion correction is a feasible alternative. This study demonstrates an online, motion-corrected reconstruction method that significantly reduces motion artifacts and improves diagnostic quality for supine breast imaging.
MAGNETIC RESONANCE IN MEDICINE
(2023)
Article
Chemistry, Analytical
Maroua Mehri, Guillaume Calmon, Freddy Odille, Julien Oster, Alain Lalande
Summary: Deep learning models have been increasingly used for analyzing medical data, such as electrocardiograms. However, existing ECG datasets often lack specific distortions that can enhance deep learning algorithms. This study proposes using a generative adversarial network (GAN) to synthesize realistic 3D magnetohydrodynamic (MHD) distortion templates and augment available ECG recordings. The synthesized MHD-distorted ECGs significantly improve the accuracy of a DL-based R-peak detector. This approach provides a simple and effective alternative to collecting new patient data.
Article
Imaging Science & Photographic Technology
Karyna Isaieva, Freddy Odille, Yves Laprie, Guillaume Drouot, Jacques Felblinger, Pierre-Andre Vuissoz
Summary: MRI is commonly used for speech imaging, but its slow speed complicates imaging of fast movements. This study tested the applicability of super-resolution algorithms for dynamic vocal tract MRI, and the results showed that it can reconstruct high-quality dynamic 3D volumes of the vocal tract.
JOURNAL OF IMAGING
(2023)
Article
Computer Science, Interdisciplinary Applications
Vinicius Ribeiro, Karyna Isaieva, Justine Leclere, Jacques Felblinger, Pierre-Andre Vuissoz, Yves Laprie
Summary: This research presents a method for individually segmenting nine non-rigid vocal tract articulators in real-time MRI movies. The software is openly available as an installable package and is designed to develop speech applications and clinical and non-clinical research in fields that require vocal tract geometry, such as speech, singing, and human beatboxing.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Imaging Science & Photographic Technology
Ioannis K. Douros, Yu Xie, Chrysanthi Dourou, Karyna Isaieva, Pierre-Andre Vuissoz, Jacques Felblinger, Yves Laprie
Summary: This research aims to create a 3D dynamic atlas of the vocal tract to capture the dynamics of articulators in all dimensions and build a speaker model independent of individual characteristics. The proposed method involves temporal alignment of real-time MR images and adaptive kernel regression for atlas construction. The evaluation demonstrates that the generated maps can accurately capture the dynamic behavior of articulators and reduce subject variability.
JOURNAL OF IMAGING
(2022)