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Keaveney S, Hopkinson G, Markus JE, Priest AN, Scurr E, Hughes J, Robertson S, Doran SJ, Collins DJ, Messiou C, Koh DM, Winfield JM. A scan-specific quality control acquisition for clinical whole-body (WB) MRI protocols. Phys Med Biol 2024; 69:125027. [PMID: 38648786 DOI: 10.1088/1361-6560/ad4195] [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: 11/22/2023] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
Objective.Image quality in whole-body MRI (WB-MRI) may be degraded by faulty radiofrequency (RF) coil elements or mispositioning of the coil arrays. Phantom-based quality control (QC) is used to identify broken RF coil elements but the frequency of these acquisitions is limited by scanner and staff availability. This work aimed to develop a scan-specific QC acquisition and processing pipeline to detect broken RF coil elements, which is sufficiently rapid to be added to the clinical WB-MRI protocol. The purpose of this is to improve the quality of WB-MRI by reducing the number of patient examinations conducted with suboptimal equipment.Approach.A rapid acquisition (14 s additional acquisition time per imaging station) was developed that identifies broken RF coil elements by acquiring images from each individual coil element and using the integral body coil. This acquisition was added to one centre's clinical WB-MRI protocol for one year (892 examinations) to evaluate the effect of this scan-specific QC. To demonstrate applicability in multi-centre imaging trials, the technique was also implemented on scanners from three manufacturers.Main results. Over the course of the study RF coil elements were flagged as potentially broken on five occasions, with the faults confirmed in four of those cases. The method had a precision of 80% and a recall of 100% for detecting faulty RF coil elements. The coil array positioning measurements were consistent across scanners and have been used to define the expected variation in signal.Significance. The technique demonstrated here can identify faulty RF coil elements and positioning errors and is a practical addition to the clinical WB-MRI protocol. This approach was fully implemented on systems from two manufacturers and partially implemented on a third. It has potential to reduce the number of clinical examinations conducted with suboptimal hardware and improve image quality across multi-centre studies.
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Affiliation(s)
- Sam Keaveney
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | | | - Julia E Markus
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Andrew N Priest
- Department of Imaging, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Erica Scurr
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Julie Hughes
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Scott Robertson
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Simon J Doran
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - David J Collins
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Christina Messiou
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Dow-Mu Koh
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Jessica M Winfield
- MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
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Zamzam AEA, Aboukhadrah RS, Khali MM, Khodair SAZ. Diagnostic value of three-dimensional cube fluid attenuated inversion recovery imaging and its axial MIP reconstruction in multiple sclerosis. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00795-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Magnetic resonance imaging is regarded as one of the most important markers for multiple sclerosis. It can detect lesions in order to establish dissemination in time and space, which would aid in the diagnosis. Two-dimensional FLAIR is a standard sequence in MS routine imaging because it suppresses cerebrospinal fluid signal, increasing contrast between lesions and CSF and improving white matter lesion detection. Newer 3D FLAIR sequences are expected to offer even more benefits, such as improved MS lesions detection and higher resolution due to thinner slice thickness. We aimed to compare the role of 3D Cube FLAIR imaging (versus standard 2D FLAIR) in the assessment of white matter lesions in MS patients, as well as to test the convenience of using maximum intensity projection (MIP) on 3D FLAIR images for faster and easier evaluation.
Results
This study included 160 MS patients. A 1.5 T routine brain MRI scan was performed, which included a 2D FLAIR sequence, followed by a 3D-FLAIR sequence. All images were analyzed after 3D-FLAIR images were reformatted into axial MIP images. Lesions were counted in each sequence and classified into supra-tentorial (periventricular, deep white matter, and juxta-cortical), and infra-tentorial lesions, with the relative comparison of lesions numbers on 3D-FLAIR and MIP versus 2D-FLAIR expressed as a percentage increase or decrease. 3D FLAIR can significantly improve MS lesion detection in all areas of the brain when compared with 2D FLAIR results. At 2 mm reformatting, there is no difference in MS lesion detection between sagittal 3D FLAIR and axial MIP reconstruction, implying that the MIP algorithm can be used to simplify lesion detection by reducing the number of images while maintaining the same level of reliability.
Conclusion
3D FLAIR sequences should be added to conventional 2D FLAIR sequences in the MRI protocol when MS is suspected.
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Inkinen SI, Mäkelä T, Kaasalainen T, Peltonen J, Kangasniemi M, Kortesniemi M. Automatic head computed tomography image noise quantification with deep learning. Phys Med 2022; 99:102-112. [PMID: 35671678 DOI: 10.1016/j.ejmp.2022.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/02/2022] [Accepted: 05/25/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE Computed tomography (CT) image noise is usually determined by standard deviation (SD) of pixel values from uniform image regions. This study investigates how deep learning (DL) could be applied in head CT image noise estimation. METHODS Two approaches were investigated for noise image estimation of a single acquisition image: direct noise image estimation using supervised DnCNN convolutional neural network (CNN) architecture, and subtraction of a denoised image estimated with denoising UNet-CNN experimented with supervised and unsupervised noise2noise training approaches. Noise was assessed with local SD maps using 3D- and 2D-CNN architectures. Anthropomorphic phantom CT image dataset (N = 9 scans, 3 repetitions) was used for DL-model comparisons. Mean square error (MSE) and mean absolute percentage errors (MAPE) of SD values were determined using the SD values of subtraction images as ground truth. Open-source clinical head CT low-dose dataset (Ntrain = 37, Ntest = 10 subjects) were used to demonstrate DL applicability in noise estimation from manually labeled uniform regions and in automated noise and contrast assessment. RESULTS The direct SD estimation using 3D-CNN was the most accurate assessment method when comparing in phantom dataset (MAPE = 15.5%, MSE = 6.3HU). Unsupervised noise2noise approach provided only slightly inferior results (MAPE = 20.2%, MSE = 13.7HU). 2DCNN and unsupervised UNet models provided the smallest MSE on clinical labeled uniform regions. CONCLUSIONS DL-based clinical image assessment is feasible and provides acceptable accuracy as compared to true image noise. Noise2noise approach may be feasible in clinical use where no ground truth data is available. Noise estimation combined with tissue segmentation may enable more comprehensive image quality characterization.
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Affiliation(s)
- Satu I Inkinen
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
| | - Teemu Mäkelä
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland
| | - Touko Kaasalainen
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Juha Peltonen
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Marko Kangasniemi
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Mika Kortesniemi
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
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Nousiainen K, Mäkelä T, Peltonen JI. Characterizing geometric distortions of 3D sequences in clinical head MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE 2022; 35:983-995. [PMID: 35657535 PMCID: PMC9596562 DOI: 10.1007/s10334-022-01020-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/20/2022] [Accepted: 05/12/2022] [Indexed: 11/25/2022]
Abstract
Objective Phantoms are often used to estimate the geometric accuracy in magnetic resonance imaging (MRI). However, the distortions may differ between anatomical and phantom images. This study aimed to investigate the applicability of a phantom-based and a test-subject-based method in evaluating geometric distortion present in clinical head-imaging sequences. Materials and methods We imaged a 3D-printed phantom and test subjects with two MRI scanners using two clinical head-imaging 3D sequences with varying patient-table positions and receiver bandwidths. The geometric distortions were evaluated through nonrigid registrations: the displaced acquisitions were compared against the ideal isocenter positioning, and the varied bandwidth volumes against the volume with the highest bandwidth. The phantom acquisitions were also registered to a computed tomography scan. Results Geometric distortion magnitudes increased with larger table displacements and were in good agreement between the phantom and test-subject acquisitions. The effect of increased distortions with decreasing receiver bandwidth was more prominent for test-subject acquisitions. Conclusion Presented results emphasize the sensitivity of the geometric accuracy to positioning and imaging parameters. Phantom limitations may become an issue with some sequence types, encouraging the use of anatomical images for evaluating the geometric accuracy.
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Affiliation(s)
- Katri Nousiainen
- HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
- Department of Physics, University of Helsinki, Helsinki, Finland.
| | - Teemu Mäkelä
- HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Physics, University of Helsinki, Helsinki, Finland
| | - Juha I Peltonen
- HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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