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Mahmutovic Persson I, Bozovic G, Westergren-Thorsson G, Rolandsson Enes S. Spatial lung imaging in clinical and translational settings. Breathe (Sheff) 2024; 20:230224. [PMID: 39360023 PMCID: PMC11444490 DOI: 10.1183/20734735.0224-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/05/2024] [Indexed: 10/04/2024] Open
Abstract
For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movement, transferred cardiac pulsation, vast density range and gravitation sensitivity. However, there is extensive ongoing research in this fast-evolving field. Recent improvements in spatial imaging have allowed us to study the three-dimensional structure of the lung, providing both spatial architecture and transcriptomic information at single-cell resolution. This fast progression, however, comes with several challenges, including significant image file storage and network capacity issues, increased costs, data processing and analysis, the role of artificial intelligence and machine learning, and mechanisms to combine several modalities. In this review, we provide an overview of advances and current issues in the field of spatial lung imaging.
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Affiliation(s)
- Irma Mahmutovic Persson
- Lund University BioImaging Centre (LBIC), Faculty of Medicine, Lund University, Lund, Sweden
- Respiratory Immunopharmacology, Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
| | - Gracijela Bozovic
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden
- Department of Medical Imaging and Clinical Physiology, Skåne University Hospital, Lund, Sweden
| | - Gunilla Westergren-Thorsson
- Lund University BioImaging Centre (LBIC), Faculty of Medicine, Lund University, Lund, Sweden
- Lung Biology, Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
| | - Sara Rolandsson Enes
- Lung Biology, Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
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Zhang JH, Neumann T, Schaeffter T, Kolbitsch C, Kerkering KM. Respiratory motion-corrected T1 mapping of the abdomen. MAGMA (NEW YORK, N.Y.) 2024; 37:637-649. [PMID: 39133420 PMCID: PMC11417068 DOI: 10.1007/s10334-024-01196-1] [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: 04/19/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/13/2024]
Abstract
OBJECTIVE The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency. MATERIALS AND METHODS Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times. RESULTS For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8 s. DISCUSSION The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.
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Affiliation(s)
- Jana Huiyue Zhang
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.
- Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany.
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
| | - Tom Neumann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Vaussenat F, Bhattacharya A, Payette J, Benavides-Guerrero JA, Perrotton A, Gerlein LF, Cloutier SG. Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study. JMIR BIOMEDICAL ENGINEERING 2023; 8:e47146. [PMID: 38875670 PMCID: PMC11041423 DOI: 10.2196/47146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/22/2023] [Accepted: 09/07/2023] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea. OBJECTIVE The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard. METHODS We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods. RESULTS The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89. CONCLUSIONS Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.
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Affiliation(s)
- Fabrice Vaussenat
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Abhiroop Bhattacharya
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Julie Payette
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | | | - Alexandre Perrotton
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Luis Felipe Gerlein
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Sylvain G Cloutier
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
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Liu Y, Yi Z, Zhao Y, Chen F, Feng Y, Guo H, Leong ATL, Wu EX. Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion. Magn Reson Med 2020; 85:897-911. [PMID: 32966651 DOI: 10.1002/mrm.28480] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 07/26/2020] [Accepted: 07/27/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To provide joint calibrationless parallel imaging reconstruction of highly accelerated multislice 2D MR k-space data. METHODS Adjacent image slices in multislice MR data have similar coil sensitivity maps, spatial support, and image content. Such similarities can be utilized to improve image quality by reconstructing multiple slices jointly with low-rank tensor completion. Specifically, the multichannel k-space data from multiple slices are constructed into a block-wise Hankel tensor and iteratively updated by promoting tensor low-rankness through higher-order SVD. This multislice block-wise Hankel tensor completion was implemented for 2D spiral and Cartesian k-space undersampling where sampling patterns vary between adjacent slices. The approach was evaluated with human brain MR data and compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. RESULTS The proposed multislice block-wise Hankel tensor completion approach robustly reconstructed highly undersampled multislice 2D spiral and Cartesian data. It produced substantially lower level of artifacts compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. Quantitative evaluation using error maps and root mean square error demonstrated its significantly improved performance in terms of residual artifacts and root mean square error. CONCLUSION Our proposed multislice block-wise Hankel tensor completion method exploits the similar coil sensitivity and image content within multislice MR data through a tensor completion framework. It offers a new and effective approach to acquire and reconstruct highly undersampled multislice MR data in a calibrationless manner.
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Affiliation(s)
- Yilong Liu
- Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Zheyuan Yi
- Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, People's Republic of China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, People's Republic of China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, People's Republic of China
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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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Ferrante E, Paragios N. Slice-to-volume medical image registration: A survey. Med Image Anal 2017; 39:101-123. [DOI: 10.1016/j.media.2017.04.010] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 04/08/2017] [Accepted: 04/27/2017] [Indexed: 11/25/2022]
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Ludwig UA, Klausmann F, Baumann S, Honal M, Hövener JB, König D, Deibert P, Büchert M. Whole-body MRI-based fat quantification: a comparison to air displacement plethysmography. J Magn Reson Imaging 2014; 40:1437-44. [PMID: 24449401 DOI: 10.1002/jmri.24509] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Accepted: 10/14/2013] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To demonstrate the feasibility of an algorithm for MRI whole-body quantification of internal and subcutaneous fat and quantitative comparison of total adipose tissue to air displacement plethysmography (ADP). MATERIALS AND METHODS For comparison with ADP, whole-body MR data of 11 volunteers were obtained using a continuously moving table Dixon sequence. Resulting fat images were corrected for B1 related intensity inhomogeneities before fat segmentation. RESULTS The performed MR measurements of the whole body provided a direct comparison to ADP measurements. The segmentation of subcutaneous and internal fat in the abdomen worked reliably with an accuracy of 98%. Depending on the underlying model for fat quantification, the resultant MR fat masses represent an upper and a lower limit for the true fat masses. In comparison to ADP, the results were in good agreement with ρ ≥ 0.97, P < 0.0001. CONCLUSION Whole-body fat quantities derived noninvasively by using a continuously moving table Dixon acquisition were directly compared with ADP. The accuracy of the method and the high reproducibility of results indicate its potential for clinical applications.
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Affiliation(s)
- Ute A Ludwig
- Department of Radiology - Medical Physics, University Medical Center Freiburg, Freiburg, Germany
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Baumann T, Kannengiesser SAR, Honal M. Temporally constrained respiratory gating improves continuously moving table MRI during free breathing. J Magn Reson Imaging 2012; 38:198-205. [PMID: 23239532 DOI: 10.1002/jmri.23964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Accepted: 10/23/2012] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To evaluate a novel breathing motion correction algorithm for continuously moving table magnetic resonance imaging (CMT-MRI) that optimizes motion consistency in a fixed time span. MATERIALS AND METHODS In 22 patients CMT-MRI was performed during free breathing. During a preparatory phase (constant) or continuously during the scan (adaptive) gating thresholds were computed from breathing states that should allow for motion consistent k-space sampling. After data from a first k-space traversal was acquired irrespective of breathing motion, subsequently k-space lines with discordant breathing states were reacquired below the gating threshold. Time constraints of CMT-MRI were respected, because a fixed time was allocated for reacquisition. Image quality and lesion depiction were evaluated on images reconstructed from the first traversal and motion-corrected images. RESULTS Compared to constant thresholds, gating with adaptive thresholds led to a higher number of reacquired k-space lines (60.1%/41.7%) and a larger fraction of motion consistent final k-space lines (96.6%/78.8%). Adaptive gating induced a significant increase in image quality for all regions affected by breathing motion. Only one of 22 lesions was not depicted on the adaptively corrected images, whereas 15 were readily appreciable. CONCLUSION Temporally constrained respiratory gating with adaptive thresholds allows for fully sampled, motion-corrected CMT-MRI acquisitions during free breathing.
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Affiliation(s)
- Tobias Baumann
- Department of Diagnostic Radiology, University Hospital Freiburg, Germany.
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