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Anand S, Lustig M. Beat Pilot Tone (BPT): Simultaneous MRI and RF motion sensing at arbitrary frequencies. Magn Reson Med 2024. [PMID: 38872443 DOI: 10.1002/mrm.30150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 04/13/2024] [Accepted: 04/23/2024] [Indexed: 06/15/2024]
Abstract
PURPOSE To introduce a simple system exploitation with the potential to turn MRI scanners into general-purpose radiofrequency (RF) motion monitoring systems. METHODS Inspired by Pilot Tone (PT), this work proposes Beat Pilot Tone (BPT), in which two or more RF tones at arbitrary frequencies are transmitted continuously during the scan. These tones create motion-modulated standing wave patterns that are sensed by the receiver coil array, incidentally mixed by intermodulation in the receiver chain, and digitized simultaneously with the MRI data. BPT can operate at almost any frequency as long as the intermodulation products lie within the bandwidth of the receivers. BPT's mechanism is explained in electromagnetic simulations and validated experimentally. RESULTS Phantom and volunteer experiments over a range of transmit frequencies suggest that BPT may offer frequency-dependent sensitivity to motion. Using a semi-flexible anterior receiver array, BPT appears to sense cardiac-induced body vibrations at microwave frequencies (≥ $$ \ge $$ 1.2 GHz). At lower frequencies, it exhibits a similar cardiac signal shape to PT, likely due to blood volume changes. Other volunteer experiments with respiratory, bulk, and head motion show that BPT can achieve greater sensitivity to motion than PT and greater separability between motion types. Basic multiple-input multiple-output (4 × 22 $$ 4\times 22 $$ MIMO) operation with simultaneous PT and BPT in head motion is demonstrated using two transmit antennas and a 22-channel head-neck coil. CONCLUSION BPT may offer a rich source of motion information that is frequency-dependent, simultaneous, and complementary to PT and the MRI exam.
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Affiliation(s)
- Suma Anand
- Electrical Engineering and Computer Sciences, University of California, Berkeley, California
| | - Michael Lustig
- Electrical Engineering and Computer Sciences, University of California, Berkeley, California
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Wu Y, Wang Z, Chu Y, Peng R, Peng H, Yang H, Guo K, Zhang J. Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review. Biomimetics (Basel) 2024; 9:170. [PMID: 38534855 DOI: 10.3390/biomimetics9030170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/29/2024] [Accepted: 03/09/2024] [Indexed: 03/28/2024] Open
Abstract
Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.
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Affiliation(s)
- Yuwen Wu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Zhisen Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Yuyi Chu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Renyuan Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Haoran Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Hongbo Yang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Kai Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Juzhong Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
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Xiao H, Han X, Zhi S, Wong YL, Liu C, Li W, Liu W, Wang W, Zhang Y, Wu H, Lee HFV, Cheung LYA, Chang HC, Liao YP, Deng J, Li T, Cai J. Ultra-fast multi-parametric 4D-MRI image reconstruction for real-time applications using a downsampling-invariant deformable registration (D2R) model. Radiother Oncol 2023; 189:109948. [PMID: 37832790 DOI: 10.1016/j.radonc.2023.109948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 09/12/2023] [Accepted: 10/09/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND AND PURPOSE Motion estimation from severely downsampled 4D-MRI is essential for real-time imaging and tumor tracking. This simulation study developed a novel deep learning model for simultaneous MR image reconstruction and motion estimation, named the Downsampling-Invariant Deformable Registration (D2R) model. MATERIALS AND METHODS Forty-three patients undergoing radiotherapy for liver tumors were recruited for model training and internal validation. Five prospective patients from another center were recruited for external validation. Patients received 4D-MRI scans and 3D MRI scans. The 4D-MRI was retrospectively down-sampled to simulate real-time acquisition. Motion estimation was performed using the proposed D2R model. The accuracy and robustness of the proposed D2R model and baseline methods, including Demons, Elastix, the parametric total variation (pTV) algorithm, and VoxelMorph, were compared. High-quality (HQ) 4D-MR images were also constructed using the D2R model for real-time imaging feasibility verification. The image quality and motion accuracy of the constructed HQ 4D-MRI were evaluated. RESULTS The D2R model showed significantly superior and robust registration performance than all the baseline methods at downsampling factors up to 500. HQ T1-weighted and T2-weighted 4D-MR images were also successfully constructed with significantly improved image quality, sub-voxel level motion error, and real-time efficiency. External validation demonstrated the robustness and generalizability of the technique. CONCLUSION In this study, we developed a novel D2R model for deformation estimation of downsampled 4D-MR images. HQ 4D-MR images were successfully constructed using the D2R model. This model may expand the clinical implementation of 4D-MRI for real-time motion management during liver cancer treatment.
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Affiliation(s)
- Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077; Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China.
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Shaohua Zhi
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Yat-Lam Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Weiwei Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Weihu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Ho-Fun Victor Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China 999077
| | - Lai-Yin Andy Cheung
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China 999077
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China 999077
| | - Yen-Peng Liao
- Department of Radiation Oncology's Division of Medical Physics & Engineering, University of Texas Southwestern Medical Center, Texas 75390, USA
| | - Jie Deng
- Department of Radiation Oncology's Division of Medical Physics & Engineering, University of Texas Southwestern Medical Center, Texas 75390, USA
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077.
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Huttinga NRF, Bruijnen T, van den Berg CAT, Sbrizzi A. Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy. Med Image Anal 2023; 88:102843. [PMID: 37245435 DOI: 10.1016/j.media.2023.102843] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the radiotherapy plan should be adapted in real-time according to the estimated motion-fields. All of this should be performed with a total latency of maximally 200 ms, including data acquisition and reconstruction. A measure of confidence in such estimated motion-fields is highly desirable, for instance to ensure the patient's safety in case of unexpected and undesirable motion. In this work, we propose a framework based on Gaussian Processes to infer 3D motion-fields and uncertainty maps in real-time from only three readouts of MR-data. We demonstrated an inference frame rate up to 69 Hz including data acquisition and reconstruction, thereby exploiting the limited amount of required MR-data. Additionally, we designed a rejection criterion based on the motion-field uncertainty maps to demonstrate the framework's potential for quality assurance. The framework was validated in silico and in vivo on healthy volunteer data (n=5) acquired using an MR-linac, thereby taking into account different breathing patterns and controlled bulk motion. Results indicate end-point-errors with a 75th percentile below 1 mm in silico, and a correct detection of erroneous motion estimates with the rejection criterion. Altogether, the results show the potential of the framework for application in real-time MR-guided radiotherapy with an MR-linac.
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Affiliation(s)
- Niek R F Huttinga
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, The Netherlands; Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, The Netherlands.
| | - Tom Bruijnen
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, The Netherlands; Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, The Netherlands; Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, The Netherlands; Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, The Netherlands
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Ludwig J, Kerkering KM, Speier P, Schaeffter T, Kolbitsch C. Pilot tone-based prospective correction of respiratory motion for free-breathing myocardial T1 mapping. MAGMA (NEW YORK, N.Y.) 2023; 36:135-150. [PMID: 35921020 PMCID: PMC9992053 DOI: 10.1007/s10334-022-01032-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/22/2022] [Accepted: 07/10/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To provide respiratory motion correction for free-breathing myocardial T1 mapping using a pilot tone (PT) and a continuous golden-angle radial acquisition. MATERIALS AND METHODS During a 45 s prescan the PT is acquired together with a dynamic sagittal image covering multiple respiratory cycles. From these images, the respiratory heart motion in head-feet and anterior-posterior direction is estimated and two linear models are derived between the PT and heart motion. In the following scan through-plane motion is corrected prospectively with slice tracking based on the PT. In-plane motion is corrected for retrospectively. Our method was evaluated on a motion phantom and 11 healthy subjects. RESULTS Non-motion corrected measurements using a moving phantom showed T1 errors of 14 ± 4% (p < 0.05) compared to a reference measurement. The proposed motion correction approach reduced this error to 3 ± 4% (p < 0.05). In vivo the respiratory motion led to an overestimation of T1 values by 26 ± 31% compared to breathhold T1 maps, which was successfully corrected to an average difference of 3 ± 2% (p < 0.05) between our free-breathing approach and breathhold data. DISCUSSION Our proposed PT-based motion correction approach allows for T1 mapping during free-breathing with the same accuracy as a corresponding breathhold T1 mapping scan.
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Affiliation(s)
- Juliane Ludwig
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Abbestr. 2-12, 10587, Berlin, Germany.
| | - Kirsten Miriam Kerkering
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Abbestr. 2-12, 10587, Berlin, Germany
| | | | - Tobias Schaeffter
- Department of Biomedical Engineering, Technische Universität Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Abbestr. 2-12, 10587, Berlin, Germany
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Huttinga NRF, Bruijnen T, Van Den Berg CAT, Sbrizzi A. Real-Time Non-Rigid 3D Respiratory Motion Estimation for MR-Guided Radiotherapy Using MR-MOTUS. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:332-346. [PMID: 34520351 DOI: 10.1109/tmi.2021.3112818] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The MR-Linac is a combination of an MR-scanner and radiotherapy linear accelerator (Linac) which holds the promise to increase the precision of radiotherapy treatments with MR-guided radiotherapy by monitoring motion during radiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal MR-guidance for respiratory motion during radiotherapy requires MR-based 3D motion estimation with a latency of 200-500 ms. Currently this is still challenging since typical methods rely on MR-images, and are therefore limited by the 3D MR-imaging latency. In this work, we present a method to perform non-rigid 3D respiratory motion estimation with 170 ms latency, including both acquisition and reconstruction. The proposed method called real-time low-rank MR-MOTUS reconstructs motion-fields directly from k -space data, and leverages an explicit low-rank decomposition of motion-fields to split the large scale 3D+t motion-field reconstruction problem posed in our previous work into two parts: (I) a medium-scale offline preparation phase and (II) a small-scale online inference phase which exploits the results of the offline phase for real-time computations. The method was validated on free-breathing data of five volunteers, acquired with a 1.5T Elekta Unity MR-Linac. Results show that the reconstructed 3D motion-field are anatomically plausible, highly correlated with a self-navigation motion surrogate ( R=0.975 ±0.0110 ), and can be reconstructed with a total latency of 170 ms that is sufficient for real-time MR-guided abdominal radiotherapy.
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Ludwig J, Speier P, Seifert F, Schaeffter T, Kolbitsch C. Pilot tone-based motion correction for prospective respiratory compensated cardiac cine MRI. Magn Reson Med 2020; 85:2403-2416. [PMID: 33226699 DOI: 10.1002/mrm.28580] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/24/2020] [Accepted: 10/12/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE To evaluate prospective motion correction using the pilot tone (PT) as a quantitative respiratory motion signal with high temporal resolution for cardiac cine images during free breathing. METHODS Before cine data acquisition, a short prescan was performed, calibrating the PT to the respiratory-induced heart motion using respiratory-resolved real-time images. The calibrated PT was then applied for nearly real-time prospective motion correction of cine MRI through slice tracking (ie, updating the slice position before every readout). Additionally, in-plane motion correction was performed retrospectively also based on the calibrated PT data. The proposed method was evaluated in a moving phantom and 10 healthy volunteers. RESULTS The PT showed very good correlation to the phantom motion. In volunteer studies using a long-term scan over 7.96 ± 1.40 min, the mean absolute error between registered and predicted motion from the PT was 1.44 ± 0.46 mm in head-feet and 0.46 ± 0.07 mm in anterior-posterior direction. Irregular breathing could also be corrected well with the PT. The PT motion correction leads to a significant improvement of contrast-to-noise ratio by 68% (P ≤ .01) between blood pool and myocardium and sharpness of endocardium by 24% (P = .04) in comparison to uncorrected data. The image score, which refers to the cine image quality, has improved with the utilization of the proposed PT motion correction. CONCLUSION The proposed approach provides respiratory motion-corrected cine images of the heart with improved image quality and a high scan efficiency using the PT. The PT is independent of the MR acquisition, making this a very flexible motion-correction approach.
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Affiliation(s)
- Juliane Ludwig
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | | | - Frank Seifert
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Technische Universität Berlin, Biomedical Engineering, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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Navest RJM, Mandija S, Zijlema SE, Stemkens B, Andreychenko A, Lagendijk JJW, van den Berg CAT. The noise navigator for MRI-guided radiotherapy: an independent method to detect physiological motion. Phys Med Biol 2020; 65:12NT01. [PMID: 32330921 DOI: 10.1088/1361-6560/ab8cd8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motion is problematic during radiotherapy as it could lead to potential underdosage of the tumor, and/or overdosage in organs-at-risk. A solution is adaptive radiotherapy guided by magnetic resonance imaging (MRI). MRI allows for imaging of target volumes and organs-at-risk before and during treatment delivery with superb soft tissue contrast in any desired orientation, enabling motion management by means of (real-time) adaptive radiotherapy. The noise navigator, which is independent of the MR signal, could serve as a secondary motion detection method in synergy with MR imaging. The feasibility of respiratory motion detection by means of the noise navigator was demonstrated previously. Furthermore, from electromagnetic simulations we know that the noise navigator is sensitive to tissue displacement and thus could in principle be used for the detection of various types of motion. In this study we demonstrate the detection of various types of motion for three anatomical use cases of MRI-guided radiotherapy, i.e. torso (bulk movement and variable breathing), head-and-neck (swallowing) and cardiac. Furthermore, it is shown that the noise navigator can detect bulk movement, variable breathing and swallowing on a hybrid 1.5 T MRI-linac system. Cardiac activity detection through the noise navigator seems feasible in an MRI-guided radiotherapy setting, but needs further optimization. The noise navigator is a versatile and fast (millisecond temporal resolution) motion detection method independent of MR signal that could serve as an independent verification method to detect the occurrence of motion in synergy with real-time MRI-guided radiotherapy.
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Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands. Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, Universiy Medical Center Utrecht, Utrecht, Netherlands
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Navest RJM, Mandija S, Bruijnen T, Stemkens B, Tijssen RHN, Andreychenko A, Lagendijk JJW, van den Berg CAT. The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy. Phys Med Biol 2020; 65:01NT02. [PMID: 31775130 DOI: 10.1088/1361-6560/ab5c62] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Respiratory-correlated 4D-MRI can characterize respiratory-induced motion of tumors and organs-at-risk for radiotherapy treatment planning and is a necessity for image guidance of moving tumors treated on an MRI-linac. Essential for 4D-MRI generation is a robust respiratory surrogate signal. We investigated the feasibility of the noise navigator as respiratory surrogate signal for 4D-MRI generation. The noise navigator is based on the respiratory-induced modulation of the thermal noise variance measured by the receive coils during MR acquisition and thus is inherently present and synchronized with MRI data acquisition. Additionally, the noise navigator can be combined with any rectilinear readout strategy (e.g. radial and cartesian) and is independent of MR image contrast and imaging orientation. For radiotherapy applications, the noise navigator provides a robust respiratory signal for patients scanned with an elevated coil setup. This is particularly attractive for widely used cartesian sequences where currently a non-interfering self-navigation means is lacking for MRI-based simulation and MRI-guided radiotherapy. The feasibility of 4D-MRI generation with the noise navigator as respiratory surrogate signal was demonstrated for both cartesian and radial readout strategies in radiotherapy setup on four healthy volunteers and two radiotherapy patients on a dedicated 1.5 T MRI scanner and two radiotherapy patients on a 1.5 T MRI-linac system. Moreover, the respiratory-correlated 4D-MR images showed liver motion comparable to a reference 2D cine MRI series for the volunteers. For 2D cartesian cine MRI acquisitions, both the noise navigator and respiratory bellows were benchmarked against an image navigator. Respiratory phase detection based on the noise navigator agreed 1.4 times better with the image navigator than the respiratory bellows did. For a 3D Stack-of-Stars acquisitions, the noise navigator was compared to radial self-navigation and a 1.7 times higher respiratory phase detection agreement was observed than for the respiratory bellows compared to radial self-navigation.
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Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands. Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands. Author to whom any correspondence should be addressed
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Huttinga NRF, van den Berg CAT, Luijten PR, Sbrizzi A. MR-MOTUS: model-based non-rigid motion estimation for MR-guided radiotherapy using a reference image and minimal k-space data. Phys Med Biol 2020; 65:015004. [PMID: 31698354 DOI: 10.1088/1361-6560/ab554a] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Time-resolved motion estimation from MRI data has received an increasing amount of interest due to the advent of the MR-Linac. The combination of an MRI scanner and a linear accelerator enables radiation plan adaptation based on internal organ motion estimated from MRI data. However, time-resolved estimation of this motion from MRI data still remains a challenge. In light of this application, we propose MR-MOTUS, a framework to estimate non-rigid 3D motion from minimal k-space data. MR-MOTUS consists of two main components: (1) a signal model that explicitly relates the k-space signal of a deforming object to non-rigid motion-fields and a reference image, and (2) model-based reconstructions of the non-rigid motion-fields directly from k-space data. Using an a priori available reference image and the fact that internal body motion exhibits a high level of spatial correlation, we represent the motion-fields in a low-dimensional space and reconstruct them from minimal k-space data that can be acquired very rapidly. The signal model is validated through numerical experiments with a digital 3D phantom and motion-fields are reconstructed from retrospectively undersampled in vivo head and abdomen data using various undersampling strategies. A comparison is made with state-of-the-art image registration performed on images reconstructed from the same undersampled data. Results show that MR-MOTUS reconstructs in vivo 3D rigid head motion from 474-fold retrospectively downsampled k-space data, and in vivo non-rigid 3D respiratory motion from 63-fold retrospectively undersampled k-space data. Preliminary results on prospectively undersampled data acquired with a 2D golden angle acquisition during free-breathing demonstrate the practical feasibility of the method.
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Jaeschke SHF, Robson MD, Hess AT. Scattering matrix imaging pulse design for real-time respiration and cardiac motion monitoring. Magn Reson Med 2019; 82:2169-2177. [PMID: 31317579 PMCID: PMC6771869 DOI: 10.1002/mrm.27884] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 05/01/2019] [Accepted: 06/07/2019] [Indexed: 12/26/2022]
Abstract
Purpose The scattering matrix (S‐matrix) of a parallel transmit (pTx) coil is sensitive to physiological motion but requires additional monitoring RF pulses to be measured. In this work, we present and evaluate pTx RF pulse designs that simultaneously excite for imaging and measure the S‐matrix to generate real‐time motion signals without prolonging the image sequence. Theory and Methods Three pTx waveforms for measuring the S‐matrix were identified and superimposed onto the imaging excitation RF pulses: (1) time division multiplexing, (2) frequency division multiplexing, and (3) code division multiplexing. These 3 methods were evaluated in healthy volunteers for scattering sensitivity and image artefacts. The S‐matrix and real‐time motion signals were calculated on the image calculation environment of the MR scanner. Prospective cardiac triggers were identified in early systole as a high rate of change of the cardiac motion signal. Monitoring accuracy was compared against electrocardiogram or the imaged diaphragm position. Results All 3 monitoring approaches measure the S‐matrix during image excitation with quality correlated to input power. No image artefacts were observed for frequency multiplexing, and low energy artefacts were observed in the other methods. The accuracy of the achieved prospective cardiac gating was 15 ± 16 ms for breath hold and 24 ± 17 ms during free breathing. The diaphragm position prediction accuracy was 1.3 ± 0.9 mm. In all volunteers, good quality cine images were acquired for breath hold scans and dual gated CINEs were demonstrated. Conclusion The S‐matrix can be measured during image excitation to generate real‐time cardiac and respiratory motion signals for prospective gating. No artefacts are introduced when frequency division multiplexing is used.
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Affiliation(s)
- Sven H F Jaeschke
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Matthew D Robson
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.,Perspectum Diagnostics, Oxford, United Kingdom
| | - Aaron T Hess
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.,BHF Centre of Research Excellence, University of Oxford, Oxford, United Kingdom
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Navest RJM, Mandija S, Andreychenko A, Raaijmakers AJE, Lagendijk JJW, van den Berg CAT. Understanding the physical relations governing the noise navigator. Magn Reson Med 2019; 82:2236-2247. [PMID: 31317566 PMCID: PMC6771522 DOI: 10.1002/mrm.27906] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 05/30/2019] [Accepted: 06/24/2019] [Indexed: 11/28/2022]
Abstract
Purpose The noise navigator is a passive way to detect physiological motion occurring in a patient through thermal noise modulations measured by standard clinical radiofrequency receive coils. The aim is to gain a deeper understanding of the potential and applications of physiologically induced thermal noise modulations. Methods Numerical electromagnetic simulations and MR measurements were performed to investigate the relative contribution of tissue displacement versus modulation of the dielectric lung properties over the respiratory cycle, the impact of coil diameter and position with respect to the body. Furthermore, the spatial motion sensitivity of specific noise covariance matrix elements of a receive array was investigated. Results The influence of dielectric lung property variations on the noise variance is negligible compared to tissue displacement. Coil size affected the thermal noise variance modulation, but the location of the coil with respect to the body had a larger impact. The modulation depth of a 15 cm diameter stationary coil approximately 3 cm away from the chest (i.e. radiotherapy setup) was 39.7% compared to 4.2% for a coil of the same size on the chest, moving along with respiratory motion. A combination of particular noise covariance matrix elements creates a specific spatial sensitivity for motion. Conclusions The insight gained on the physical relations governing the noise navigator will allow for optimized use and development of new applications. An optimized combination of elements from the noise covariance matrix offer new ways of performing, e.g. motion tracking.
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Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - S Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - A Andreychenko
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,ITMO University, St. Petersburg, Russian Federation.,Department of Healthcare, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies of the Moscow, Moscow, Russian Federation
| | - A J E Raaijmakers
- Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands.,Deptartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - J J W Lagendijk
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - C A T van den Berg
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
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13
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Jackson LH, Price AN, Hutter J, Ho A, Roberts TA, Slator PJ, Clough JR, Deprez M, McCabe L, Malik SJ, Chappell L, Rutherford MA, Hajnal JV. Respiration resolved imaging with continuous stable state 2D acquisition using linear frequency SWEEP. Magn Reson Med 2019; 82:1631-1645. [PMID: 31183892 PMCID: PMC6682494 DOI: 10.1002/mrm.27834] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 04/04/2019] [Accepted: 05/09/2019] [Indexed: 01/31/2023]
Abstract
Purpose To investigate the potential of continuous radiofrequency (RF) shifting (SWEEP) as a technique for creating densely sampled data while maintaining a stable signal state for dynamic imaging. Methods We present a method where a continuous stable state of magnetization is swept smoothly across the anatomy of interest, creating an efficient approach to dense multiple 2D slice imaging. This is achieved by introducing a linear frequency offset to successive RF pulses shifting the excited slice by a fraction of the slice thickness with each successive repeat times (TR). Simulations and in vivo imaging were performed to assess how this affects the measured signal. Free breathing, respiration resolved 4D volumes in fetal/placental imaging is explored as potential application of this method. Results The SWEEP method maintained a stable signal state over a full acquisition reducing artifacts from unstable magnetization. Simulations demonstrated that the effects of SWEEP on slice profiles was of the same order as that produced by physiological motion observed with conventional methods. Respiration resolved 4D data acquired with this method shows reduced respiration artifacts and resilience to non‐rigid and non‐cyclic motion. Conclusions The SWEEP method is presented as a technique for improved acquisition efficiency of densely sampled short‐TR 2D sequences. Using conventional slice excitation the number of RF pulses required to enter a true steady state is excessively high when using short‐TR 2D acquisitions, SWEEP circumvents this limitation by creating a stable signal state that is preserved between slices.
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Affiliation(s)
- L H Jackson
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - A N Price
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - J Hutter
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - A Ho
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom.,Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - T A Roberts
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - P J Slator
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - J R Clough
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - M Deprez
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - L McCabe
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - S J Malik
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - L Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - M A Rutherford
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
| | - J V Hajnal
- Biomedical Engineering, School of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom
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14
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Stemkens B, Paulson ES, Tijssen RHN. Nuts and bolts of 4D-MRI for radiotherapy. ACTA ACUST UNITED AC 2018; 63:21TR01. [DOI: 10.1088/1361-6560/aae56d] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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15
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Jaeschke SH, Robson MD, Hess AT. Cardiac gating using scattering of an 8-channel parallel transmit coil at 7T. Magn Reson Med 2018; 80:633-640. [PMID: 29230860 PMCID: PMC5947608 DOI: 10.1002/mrm.27038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Revised: 10/27/2017] [Accepted: 11/17/2017] [Indexed: 12/24/2022]
Abstract
PURPOSE To establish a cardiac signal from scattering matrix or scattering coefficient measurements made on a 7T 8-channel parallel transmit (pTx) system, and to evaluate its use for cardiac gating. METHODS Measurements of the scattering matrix and scattering coefficients were acquired using a monitoring pulse sequence and during a standard cine acquisition, respectively. Postprocessing used an independent component analysis and gating feature identification. The effect of the phase of the excitation radiofrequency (RF) field ( B1+ shim) on the cardiac signal was simulated for multiple B1+ shim configurations, and cine images were reconstructed from both the scattering coefficients and electrocardiogram (ECG). RESULTS The cardiac motion signal was successfully identified in all subjects with a mean signal-to-noise ratio of 33.1 and 5.7 using the scattering matrix and scattering coefficient measurements, respectively. The dominant gating feature in the cardiac signal was a peak aligned with end-systole that occurred on average at 311 and 391 ms after the ECG trigger, with a mean standard deviation of 13.4 and 18.1 ms relative to ECG when using the scattering matrix and scattering coefficients measurements, respectively. The scattering coefficients showed a dependence on B1+ shim with some shim configurations not showing any cardiac signal. Cine images were successfully reconstructed using the scattering coefficients with minimal differences compared to those using ECG. CONCLUSION We have shown that the scattering of a pTx RF coil can be used to estimate a cardiac signal, and that scattering matrix and coefficients can be used to cardiac gate MRI acquisitions with the scattering matrix providing a superior cardiac signal. Magn Reson Med 80:633-640, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Sven H.F. Jaeschke
- University of Oxford Centre for Clinical Magnetic Resonance Research, John Radcliffe HospitalOxfordUnited Kingdom
| | - Matthew D. Robson
- University of Oxford Centre for Clinical Magnetic Resonance Research, John Radcliffe HospitalOxfordUnited Kingdom
| | - Aaron T. Hess
- University of Oxford Centre for Clinical Magnetic Resonance Research, John Radcliffe HospitalOxfordUnited Kingdom
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16
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Navest RJM, Andreychenko A, Lagendijk JJW, van den Berg CAT. Prospective Respiration Detection in Magnetic Resonance Imaging by a Non-Interfering Noise Navigator. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1751-1760. [PMID: 29994440 DOI: 10.1109/tmi.2018.2808699] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Passive monitoring of the thermal noise variances of the channels of a receive array was shown to reveal respiratory motion of the underlying anatomy, a so called "noise navigator". There is, however, an inevitable trade off between the accuracy and temporal resolution of the noise navigator due to its passive nature. A temporal filter has to be added to the noise navigator to accurately reveal respiration and retain temporal resolution. For real-time applications of the noise navigator, e.g., prospective motion correction or motion tracking, the added filter must be prospective. Thus a prospective Kalman filter was designed to predict respiration from the noise navigator without a temporal delay. The performance of the noise navigator enhanced by this prospective Kalman filter was explored and the robustness of the proposed method was assessed on healthy volunteers. The respiratory signal could be measured by the noise navigator independent of magnetic resonance acquisition. The calculated respiratory signal was qualitatively compared with the respiratory bellows. In addition, a strong linear relationship was found between the prospective noise navigator and a quantitative 2-D image navigator for measurements, including free and tasked breathing.
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17
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Stemkens B, Benkert T, Chandarana H, Bittman ME, Van den Berg CA, Lagendijk JJ, Sodickson DK, Tijssen RH, Block KT. Adaptive bulk motion exclusion for improved robustness of abdominal magnetic resonance imaging. NMR IN BIOMEDICINE 2017; 30:e3830. [PMID: 28885742 PMCID: PMC5643254 DOI: 10.1002/nbm.3830] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 08/03/2017] [Accepted: 08/14/2017] [Indexed: 05/09/2023]
Abstract
Non-Cartesian magnetic resonance imaging (MRI) sequences have shown great promise for abdominal examination during free breathing, but break down in the presence of bulk patient motion (i.e. voluntary or involuntary patient movement resulting in translation, rotation or elastic deformations of the body). This work describes a data-consistency-driven image stabilization technique that detects and excludes bulk movements during data acquisition. Bulk motion is identified from changes in the signal intensity distribution across different elements of a multi-channel receive coil array. A short free induction decay signal is acquired after excitation and used as a measure to determine alterations in the load distribution. The technique has been implemented on a clinical MR scanner and evaluated in the abdomen. Six volunteers were scanned and two radiologists scored the reconstructions. To show the applicability to other body areas, additional neck and knee images were acquired. Data corrupted by bulk motion were successfully detected and excluded from image reconstruction. An overall increase in image sharpness and reduction of streaking and shine-through artifacts were seen in the volunteer study, as well as in the neck and knee scans. The proposed technique enables automatic real-time detection and exclusion of bulk motion during MR examinations without user interaction. It may help to improve the reliability of pediatric MRI examinations without the use of sedation.
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Affiliation(s)
- Bjorn Stemkens
- Department of RadiotherapyUniversity Medical Center Utrechtthe Netherlands
| | - Thomas Benkert
- Center for Advanced Imaging Innovation and Research (CAIR), Department of RadiologyNew York University School of MedicineNew YorkNYUSA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkNYUSA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAIR), Department of RadiologyNew York University School of MedicineNew YorkNYUSA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkNYUSA
| | - Mark E. Bittman
- Center for Advanced Imaging Innovation and Research (CAIR), Department of RadiologyNew York University School of MedicineNew YorkNYUSA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkNYUSA
| | | | - Jan J.W. Lagendijk
- Department of RadiotherapyUniversity Medical Center Utrechtthe Netherlands
| | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), Department of RadiologyNew York University School of MedicineNew YorkNYUSA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkNYUSA
| | - Rob H.N. Tijssen
- Department of RadiotherapyUniversity Medical Center Utrechtthe Netherlands
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAIR), Department of RadiologyNew York University School of MedicineNew YorkNYUSA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkNYUSA
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18
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Hess AT, Tunnicliffe EM, Rodgers CT, Robson MD. Diaphragm position can be accurately estimated from the scattering of a parallel transmit RF coil at 7 T. Magn Reson Med 2017; 79:2164-2169. [PMID: 28771792 PMCID: PMC5836958 DOI: 10.1002/mrm.26866] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 07/12/2017] [Accepted: 07/19/2017] [Indexed: 11/10/2022]
Abstract
Purpose To evaluate the use of radiofrequency scattering of a parallel transmit coil to track diaphragm motion. Methods Measurements made during radiofrequency excitation on an 8‐channel parallel transmit coil by the directional couplers of the radiofrequency safety monitor were combined and converted into diaphragm position. A 30‐s subject‐specific calibration with an MRI navigator was used to determine a diaphragm estimate from each directional‐coupler measure. Seven healthy volunteers were scanned at 7 T, in which images of the diaphragm were continuously acquired and directional couplers were monitored during excitation radiofrequency pulses. The ability to detect coughing was evaluated in one subject. The method was implemented on the scanner and evaluated for diaphragm gating of a free‐breathing cardiac cine. Results Six of the seven scans were successful. In these subjects, the root mean square difference between MRI and scattering estimation of the superior–inferior diaphragm position was 1.4 ± 0.5 mm. On the scanner, the position was calculated less than 2 ms after every radiofrequency pulse. A prospectively gated (echocardiogram and respiration) high‐resolution free‐breathing cine showed no respiratory artifact and sharp blood‐myocardium definition. Conclusions Transmit coil scattering is sensitive to diaphragm motion and provides rapid, quantitative, and accurate monitoring of respiration. Magn Reson Med 79:2164–2169, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Aaron T Hess
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
| | - Elizabeth M Tunnicliffe
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
| | - Christopher T Rodgers
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
| | - Matthew D Robson
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford, United Kingdom
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19
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Bydder M, Rapacchi S, Girard O, Guye M, Ranjeva JP. Trimmed autocalibrating k-space estimation based on structured matrix completion. Magn Reson Imaging 2017; 43:88-94. [PMID: 28716683 DOI: 10.1016/j.mri.2017.07.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 07/07/2017] [Accepted: 07/13/2017] [Indexed: 11/28/2022]
Abstract
PURPOSE Parallel imaging allows the reconstruction of undersampled data from multiple coils. This provides a means to reject and regenerate corrupt data (e.g. from motion artefact). The purpose of this work is to approach this problem using the SAKE parallel imaging method. THEORY AND METHODS Parallel imaging methods typically require calibration by fully sampling the center of k-space. This is a challenge in the presence of corrupted data, since the calibration data may be corrupted which leads to an errors-in-variables problem that cannot be solved by least squares or even iteratively reweighted least squares. The SAKE method, based on matrix completion and structured low rank approximation, was modified to detect and trim these errors from the data. RESULTS Simulated and actual corrupted datasets were reconstructed with SAKE, the proposed approach and a more standard reconstruction method (based on solving a linear equation) with a data rejection criterion. The proposed approach was found to reduce artefacts considerably in comparison to the other two methods. CONCLUSION SAKE with data trimming improves on previous methods for reconstructing images from grossly corrupted data.
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Affiliation(s)
- Mark Bydder
- Aix-Marseille Université, Centre de Résonance Magnétique Biologique et Médicale, UMR CNRS 7339, Marseille, France.
| | - Stanislas Rapacchi
- Aix-Marseille Université, Centre de Résonance Magnétique Biologique et Médicale, UMR CNRS 7339, Marseille, France
| | - Olivier Girard
- Aix-Marseille Université, Centre de Résonance Magnétique Biologique et Médicale, UMR CNRS 7339, Marseille, France
| | - Maxime Guye
- Aix-Marseille Université, Centre de Résonance Magnétique Biologique et Médicale, UMR CNRS 7339, Marseille, France; Assistance Publique - Hôpitaux de Marseille, CEMREM, Pôle d'Imagerie Médicale, CHU Timone, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix-Marseille Université, Centre de Résonance Magnétique Biologique et Médicale, UMR CNRS 7339, Marseille, France
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20
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Andreychenko A, Denis de Senneville B, Navest RJM, Tijssen RHN, Lagendijk JJW, van den Berg CAT. Respiratory motion model based on the noise covariance matrix of a receive array. Magn Reson Med 2017; 79:1730-1735. [PMID: 28593709 DOI: 10.1002/mrm.26775] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 04/20/2017] [Accepted: 05/15/2017] [Indexed: 11/12/2022]
Abstract
PURPOSE Tracking of the internal anatomy by means of a motion model that uses the MR-derived motion fields and noise covariance matrix (NCM) dynamic as a surrogate signal. METHODS A 2D respiratory motion model was developed based on the MR-derived motion fields and the NCM of a receive array used in MRI. Temporal dynamics of the NCM were used as a motion surrogate for a linear correspondence motion model. The model performance was tested on five healthy volunteers with a liver as the target. The motion fields were calculated from the cineMR frames with an optical flow registration tool. RESULTS The model estimated the liver motion with an average residual error of 2.3 mm (13% of the motion amplitude). The model formation takes 3 min and the model latency was 0.5 s in the current implementation. The limiting factor for the latency is the current update time of the NCM (0.48 s), which in principle can be reduced to 0.004 s with an alternative way to determine the NCM. CONCLUSIONS The 2D respiratory motion of the liver can be effectively estimated with the linear motion model that uses the temporal behavior of the NCM as motion surrogate. Magn Reson Med 79:1730-1735, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- A Andreychenko
- Center for Image Sciences, University Medical Center Utrecht, the Netherlands
| | - B Denis de Senneville
- Center for Image Sciences, University Medical Center Utrecht, the Netherlands.,IMB, UMR 5251 CNRS/University of Bordeaux, Bordeaux, France
| | - R J M Navest
- Center for Image Sciences, University Medical Center Utrecht, the Netherlands
| | - R H N Tijssen
- Center for Image Sciences, University Medical Center Utrecht, the Netherlands
| | - J J W Lagendijk
- Center for Image Sciences, University Medical Center Utrecht, the Netherlands
| | - C A T van den Berg
- Center for Image Sciences, University Medical Center Utrecht, the Netherlands
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