1
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Li Y, Qi Y, Hu Z, Zhang K, Jia S, Zhang L, Xu W, Shen S, Wáng YXJ, Li Z, Liang D, Liu X, Zheng H, Cheng G, Zhang N. A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma. Quant Imaging Med Surg 2024; 14:2008-2020. [PMID: 38415166 PMCID: PMC10895104 DOI: 10.21037/qims-23-946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/14/2023] [Indexed: 02/29/2024]
Abstract
Background The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches. The adaption of segmentation architectures originally designed for road extraction for medical imaging represents an innovative step in this direction. By employing K-space data as the modal input, this method completely eliminates the information loss inherent in FFT, thereby potentially enhancing the precision and effectiveness of glioma diagnosis. Methods In the study, a novel architecture based on a deep-residual U-net was developed to accomplish the challenging task of automatically segmenting brain tumors from K-space data. Brain tumors from K-space data with different under-sampling rates were also segmented to verify the clinical application of our method. Results Compared to the benchmarks set in the 2018 Brain Tumor Segmentation (BraTS) Challenge, our proposed architecture had superior performance, achieving Dice scores of 0.8573, 0.8789, and 0.7765 for the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively. The corresponding Hausdorff distances were 2.5649, 1.6146, and 2.7187 for the WT, TC, and ET regions, respectively. Notably, compared to traditional image-based approaches, the architecture also exhibited an improvement of approximately 10% in segmentation accuracy on the K-space data at different under-sampling rates. Conclusions These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the segmentation task to be accomplished more efficiently.
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Affiliation(s)
- Yikang Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Imperial College London, London, UK
| | - Yulong Qi
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Zhanli Hu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ke Zhang
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wenjing Xu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shuai Shen
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yì Xiáng J Wáng
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Zongyang Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Guanxun Cheng
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Na Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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2
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Ravi D, Barkhof F, Alexander DC, Puglisi L, Parker GJM, Eshaghi A. An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training. Med Image Anal 2024; 91:103033. [PMID: 38000256 DOI: 10.1016/j.media.2023.103033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/04/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements devoid of significant artefacts. Despite a wide range of existing automatic methods having been developed to identify imperfections and artefacts in medical imaging, they mostly rely on data-hungry methods. In particular, the scarcity of artefact-containing scans available for training has been a major obstacle in the development and implementation of machine learning in clinical research. To tackle this problem, we propose a novel framework having four main components: (1) a set of artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, (2) a set of abstract and engineered features to represent images compactly, (3) a feature selection process that depends on the class of artefact to improve classification performance, and (4) a set of Support Vector Machine (SVM) classifiers trained to identify artefacts. Our novel contributions are threefold: first, we use the novel physics-based artefact generators to generate synthetic brain MRI scans with controlled artefacts as a data augmentation technique. This will avoid the labour-intensive collection and labelling process of scans with rare artefacts. Second, we propose a large pool of abstract and engineered image features developed to identify 9 different artefacts for structural MRI. Finally, we use an artefact-based feature selection block that, for each class of artefacts, finds the set of features that provide the best classification performance. We performed validation experiments on a large data set of scans with artificially-generated artefacts, and in a multiple sclerosis clinical trial where real artefacts were identified by experts, showing that the proposed pipeline outperforms traditional methods. In particular, our data augmentation increases performance by up to 12.5 percentage points on the accuracy, F1, F2, precision and recall. At the same time, the computation cost of our pipeline remains low - less than a second to process a single scan - with the potential for real-time deployment. Our artefact simulators obtained using adversarial learning enable the training of a quality control system for brain MRI that otherwise would have required a much larger number of scans in both supervised and unsupervised settings. We believe that systems for quality control will enable a wide range of high-throughput clinical applications based on the use of automatic image-processing pipelines.
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Affiliation(s)
- Daniele Ravi
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK; Queen Square Analytics, London, UK; School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK.
| | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, University College London, UK; Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Queen Square Analytics, London, UK; NMR Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institutes of Neurology, Faculty of Brain Sciences, University College London, London, UK; Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK; Queen Square Analytics, London, UK
| | | | - Geoffrey J M Parker
- Department of Medical Physics and Biomedical Engineering, University College London, UK; Queen Square Analytics, London, UK; NMR Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institutes of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Arman Eshaghi
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK; Queen Square Analytics, London, UK; NMR Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institutes of Neurology, Faculty of Brain Sciences, University College London, London, UK
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3
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Shiraishi K, Nakaura T, Uetani H, Nagayama Y, Kidoh M, Kobayashi N, Morita K, Yamahita Y, Tanaka Y, Baba H, Hirai T. Deep learning-based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time. Eur Radiol 2023; 33:7585-7594. [PMID: 37178197 DOI: 10.1007/s00330-023-09703-z] [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: 10/02/2022] [Revised: 03/06/2023] [Accepted: 03/20/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES To evaluate the image quality of the 3D hybrid profile order technique and deep-learning-based reconstruction (DLR) for 3D magnetic resonance cholangiopancreatography (MRCP) within a single breath-hold (BH) at 3 T magnetic resonance imaging (MRI). METHODS This retrospective study included 32 patients with biliary and pancreatic disorders. BH images were reconstructed with and without DLR. The signal-to-noise ratio (SNR), contrast, contrast-to-noise ratio (CNR) between the common bile duct (CBD) and periductal tissues, and full width at half maximum (FWHM) of CBD on 3D-MRCP were evaluated quantitatively. Two radiologists scored image noise, contrast, artifacts, blur, and overall image quality of the three image types using a 4-point scale. Quantitative and qualitative scores were compared using the Friedman test and post hoc Nemenyi test. RESULTS The SNR and CNR were not significantly different when under respiratory gating- and BH-MRCP without DLR. However, they were significantly higher under BH with DLR than under respiratory gating (SNR, p = 0.013; CNR, p = 0.027). The contrast and FWHM of MRCP under BH with and without DLR were lower than those under respiratory gating (contrast, p < 0.001; FWHM, p = 0.015). Qualitative scores for noise, blur, and overall image quality were higher under BH with DLR than those under respiratory gating (blur, p = 0.003; overall, p = 0.008). CONCLUSIONS The combination of the 3D hybrid profile order technique and DLR is useful for MRCP within a single BH and does not lead to the deterioration of image quality and space resolution at 3 T MRI. CLINICAL RELEVANCE STATEMENT Considering its advantages, this sequence might become the standard protocol for MRCP in clinical practice, at least at 3.0 T. KEY POINTS • The 3D hybrid profile order can achieve MRCP within a single breath-hold without a decrease in spatial resolution. • The DLR significantly improved the CNR and SNR of BH-MRCP. • The 3D hybrid profile order technique with DLR reduces the deterioration of image quality in MRCP within a single breath-hold.
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Affiliation(s)
- Kaori Shiraishi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan.
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Naoki Kobayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Kosuke Morita
- Department of Radiology, Kumamoto University Hospital, Kumamoto, Japan, Honjo 1-1-1, Kumamoto, Japan
| | - Yuichi Yamahita
- Canon Medical Systems Corporation, 70-1, Yanagi-Cho, Saiwai-Ku, Kawasaki-Shi, Kanagawa, 212-0015, Japan
| | - Yasuhito Tanaka
- Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
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Ahmed SY, Hassan FF. Optimizing imaging resolution in brain MRI: understanding the impact of technical factors. J Med Life 2023; 16:920-924. [PMID: 37675169 PMCID: PMC10478647 DOI: 10.25122/jml-2022-0212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 11/24/2022] [Indexed: 09/08/2023] Open
Abstract
Magnetic resonance imaging (MRI) exams are essential for diagnostic procedures, but their lengthy duration and associated costs limit their accessibility. Shorter scan times would reduce expenses and allow for more MRI exams, expanding the range of diagnostic procedures. This study investigated technical factors that could decrease scan time without compromising image quality, including field-of-view (FOV), phase field of view, phase oversampling, cross-talk, brain MRI imaging resolution, and scan time. Data were collected from September 2021 to June 2022. All patients underwent brain scans in the transverse plane following a standardized protocol using a 1.5-tesla Siemens Avanto MRI scanner. The protocol employed T2-weighted Turbo Spin Echo imaging. Twenty-four cases were included in this study. Initially, all participants underwent brain MRI scans using the original protocols with axial sections. The results indicated that altering the FOV phase and phase oversampling significantly affected the scan time, whereas other factors did not have a direct impact. The original protocol had a scan time of 3.47 minutes with a FOV of 230 mm, 90% FOV phase, and 0% phase oversampling. After implementing the modified protocol, the scan time was reduced to 2.18 minutes with a FOV of 217 mm and 93.98% phase oversampling of 13.96%. Statistical analysis confirmed the high significance of FOV phase and phase oversampling in reducing scan time. By optimizing these technical factors, MRI exams can be performed more efficiently, resulting in shorter scan times and potentially reducing costs. This would enhance patient comfort and enable a greater number of MRI exams, facilitating a more comprehensive range of diagnostic procedures.
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Affiliation(s)
- Shapol Yousif Ahmed
- Department of Basic Sciences, College of Medicine, Hawler Medical University, Erbil, Iraq
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5
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Miller WB. A scale-free universal relational information matrix (N-space) reconciles the information problem: N-space as the fabric of reality. Commun Integr Biol 2023; 16:2193006. [PMID: 37188326 PMCID: PMC10177686 DOI: 10.1080/19420889.2023.2193006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 05/17/2023] Open
Abstract
Cellular measurement is a crucial faculty in living systems, and exaptations are acknowledged as a significant source of evolutionary innovation. However, the possibility that the origin of biological order is predicated on an exaptation of the measurement of information from the abiotic realm has not been previously explored. To support this hypothesis, the existence of a universal holographic relational information space-time matrix is proposed as a scale-free unification of abiotic and biotic information systems. In this framework, information is a universal property representing the interactions between matter and energy that can be subject to observation. Since observers are also universally distributed, information can be deemed the fundamental fabric of the universe. The novel concept of compartmentalizing this universal N-space information matrix into separate N-space partitions as nodes of informational density defined by Markov blankets and boundaries is introduced, permitting their applicability to both abiotic and biotic systems. Based on these N-space partitions, abiotic systems can derive meaningful information from the conditional settlement of quantum entanglement asymmetries and coherences between separately bounded quantum informational reference frames sufficient to be construed as a form of measurement. These conditional relationships are the precursor of the reiterating nested architecture of the N-space-derived information fields that characterize life and account for biological order. Accordingly, biotic measurement and biological N-space partitioning are exaptations of preexisting information processes within abiotic systems. Abiotic and biotic states thereby reconcile as differing forms of measurement of fundamental universal information. The essential difference between abiotic and biotic states lies within the attributes of the specific observer/detectors, thereby clarifying several contentious aspects of self-referential consciousness.
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6
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Basit A, Inam O, Omer H. Accelerating GRAPPA reconstruction using SoC design for real-time cardiac MRI. Comput Biol Med 2023; 160:107008. [PMID: 37159960 DOI: 10.1016/j.compbiomed.2023.107008] [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: 12/04/2022] [Revised: 04/19/2023] [Accepted: 05/03/2023] [Indexed: 05/11/2023]
Abstract
Real-time cardiac MRI is a rapidly developing area of research that has the potential to improve the diagnosis and treatment of cardiovascular diseases. However, the acquisition of high-quality real-time cardiac MR (CMR) images is challenging as it requires a high frame rate and temporal resolution. To overcome this challenge, there have been recent efforts on several approaches including hardware-based improvements and image reconstruction techniques such as compressed sensing and parallel MRI. The use of parallel MRI techniques such as GRAPPA (Generalized Autocalibrating Partial Parallel Acquisition) is a promising approach for improving the temporal resolution of MRI and expanding its applications in clinical practice. However, the GRAPPA algorithm involves a significant amount of computation, particularly for high acceleration factors and large datasets. This can result in long reconstruction times, which can limit the ability to achieve real-time imaging or high frame rates. One solution to this challenge is to use specialized hardware i.e. field-programmable gate arrays (FPGAs). In this work, a novel 32-bit floating-point FPGA-based GRAPPA accelerator is proposed with an aim to reconstruct high-quality cardiac MR images at higher frame rates, making it well suited for real-time clinical applications. The proposed FPGA-based accelerator consists of custom-designed data processing units named as dedicated computational engines (DCEs) that allow for a continuous flow of data between the calibration and synthesis stages of GRAPPA reconstruction process. This greatly increases the throughput and reduces the latency of the overall proposed system. Moreover, a high-speed memory module (DDR4-SDRAM) is integrated with the proposed architecture to store the multi-coil MR data. An on-chip quad-core ARM Cortex-A53 processor is used to manage access control information required for data transfer between the DCEs and DDR4-SDRAM. The proposed accelerator is implemented on Xilinx Zynq UltraScale + MPSoC using high-level synthesis (HLS) and hardware descriptive language (HDL) with an aim to explore the trade-offs between the reconstruction time, resource utilization and design effort. Several experiments have been performed using in-vivo cardiac datasets i.e. 18-receiver coil and 30-receiver coil to evaluate the performance of the proposed accelerator. A comparison is performed with the contemporary CPU and GPU-based GRAPPA reconstruction methods in terms of reconstruction time, frames-per-second and reconstruction accuracy (RMSE and SNR). The results show that the proposed accelerator achieves speed-up factors up to 121× and 9× as compared to the contemporary CPU-based and GPU-based GRAPPA reconstruction methods, respectively. Moreover, it has been demonstrated that the proposed accelerator can achieve reconstruction rates of up to ∼27 frames-per-second while maintaining the visual quality of the reconstructed images.
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Affiliation(s)
- Abdul Basit
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan.
| | - Omair Inam
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Hammad Omer
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
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7
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Fervers P, Zaeske C, Rauen P, Iuga AI, Kottlors J, Persigehl T, Sonnabend K, Weiss K, Bratke G. Conventional and Deep-Learning-Based Image Reconstructions of Undersampled K-Space Data of the Lumbar Spine Using Compressed Sensing in MRI: A Comparative Study on 20 Subjects. Diagnostics (Basel) 2023; 13:diagnostics13030418. [PMID: 36766523 PMCID: PMC9914543 DOI: 10.3390/diagnostics13030418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/20/2023] [Accepted: 01/21/2023] [Indexed: 01/25/2023] Open
Abstract
Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive undersampling impairs image quality when using conventional reconstruction techniques. Deep-learning-based reconstruction methods might allow for stronger undersampling and thus faster MRI scans without loss of crucial image quality. We compared imaging approaches using parallel imaging (SENSE), a combination of parallel imaging and compressed sensing (COMPRESSED SENSE, CS), and a combination of CS and a deep-learning-based reconstruction (CS AI) on raw k-space data acquired at different undersampling factors. 3D T2-weighted images of the lumbar spine were obtained from 20 volunteers, including a 3D sequence (standard SENSE), as provided by the manufacturer, as well as accelerated 3D sequences (undersampling factors 4.5, 8, and 11) reconstructed with CS and CS AI. Subjective rating was performed using a 5-point Likert scale to evaluate anatomical structures and overall image impression. Objective rating was performed using apparent signal-to-noise and contrast-to-noise ratio (aSNR and aCNR) as well as root mean square error (RMSE) and structural-similarity index (SSIM). The CS AI 4.5 sequence was subjectively rated better than the standard in several categories and deep-learning-based reconstructions were subjectively rated better than conventional reconstructions in several categories for acceleration factors 8 and 11. In the objective rating, only aSNR of the bone showed a significant tendency towards better results of the deep-learning-based reconstructions. We conclude that CS in combination with deep-learning-based image reconstruction allows for stronger undersampling of k-space data without loss of image quality, and thus has potential for further scan time reduction.
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Affiliation(s)
- Philipp Fervers
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
- Correspondence:
| | - Charlotte Zaeske
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Philip Rauen
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Andra-Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | | | - Kilian Weiss
- Philips GmbH Market DACH, 22335 Hamburg, Germany
| | - Grischa Bratke
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
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8
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Ahmad A, Parker D, Dheer S, Samani ZR, Verma R. 3D-QCNet - A pipeline for automated artifact detection in diffusion MRI images. Comput Med Imaging Graph 2023; 103:102151. [PMID: 36502764 PMCID: PMC10494975 DOI: 10.1016/j.compmedimag.2022.102151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 10/27/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022]
Abstract
Artifacts are a common occurrence in Diffusion MRI (dMRI) scans. Identifying and removing them is essential to ensure the accuracy and viability of any post-processing carried out on these scans. This makes quality control (QC) a crucial first step prior to any analysis of dMRI data. Several QC methods for artifact detection exist, however they suffer from problems like requiring manual intervention and the inability to generalize across different artifacts and datasets. In this paper, we propose an automated deep learning (DL) pipeline that utilizes a 3D-Densenet architecture to train a model on diffusion volumes for automatic artifact detection. Our method is validated on 9000 volumes sourced from 7 large clinical datasets spanning different acquisition protocols (with different gradient directions, high and low b-values, single-shell and multi-shell acquisitions) from multiple scanners. Additionally, they represent diverse subject demographics including age, sex and the presence or absence of pathologies. Our QC method is found to accurately generalize across this heterogenous data by correctly detecting 92% artifacts on average across our test set. This consistent performance over diverse datasets underlines the generalizability of our method, which currently is a significant barrier hindering the widespread adoption of automated QC techniques. Thus, 3D-QCNet can be integrated into diffusion pipelines to effectively automate the arduous and time-intensive process of artifact detection.
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Affiliation(s)
- Adnan Ahmad
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew Parker
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Suhani Dheer
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Zahra Riahi Samani
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
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9
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Bardo DME, Rubert N. Radial sequences and compressed sensing in pediatric body magnetic resonance imaging. Pediatr Radiol 2022; 52:382-390. [PMID: 34009408 DOI: 10.1007/s00247-021-05097-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 03/11/2021] [Accepted: 04/28/2021] [Indexed: 12/28/2022]
Abstract
Magnetic resonance imaging (MRI) is often an ideal imaging modality for children of any age for any anatomy and for many pathologies. MRI sequences can be prescribed to produce high-resolution images of anatomical structures, characterize tissue composition, and detect physiological states and organ function. Shortening imaging sequences in any manner possible has been a topic of research and development in MRI since its emergence. Selection of imaging sequence parameters influences more than just the appearance and signal qualities of the imaged tissues; these details along with spatial encoding and data readout steps determine the time it takes to acquire an image. As each piece of image data is acquired and encoded with spatial and temporal information it is stored in k-space. As k-space is filled, either completely or partially, a diagnostic image or physiological data can be reconstructed. Shortening the length of time required for the readout step by efficiently filling k-space using compressed sensing and radial techniques is the subject of this manuscript.
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Affiliation(s)
- Dianna M E Bardo
- Phoenix Children's Hospital, 1919 E. Thomas Road, Phoenix, AZ, 85016, USA.
| | - Nicholas Rubert
- Phoenix Children's Hospital, 1919 E. Thomas Road, Phoenix, AZ, 85016, USA
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10
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Development, calibration, and testing of 3D amplified MRI (aMRI) for the quantification of intrinsic brain motion. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100022] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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11
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Tran AQ, Nguyen TA, Doan PT, Tran DN, Tran DT. Parallel magnetic resonance imaging acceleration with a hybrid sensing approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:2288-2302. [PMID: 33892546 DOI: 10.3934/mbe.2021116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In magnetic resonance imaging (MRI), the scan time for acquiring an image is relatively long, resulting in patient uncomfortable and error artifacts. Fortunately, the compressed sensing (CS) and parallel magnetic resonance imaging (pMRI) can reduce the scan time of the MRI without significantly compromising the quality of the images. It has been found that the combination of pMRI and CS can better improve the image reconstruction, which will accelerate the speed of MRI acquisition because the number of measurements is much smaller than that by pMRI. In this paper, we propose combining a combined CS method and pMRI for better accelerating the MRI acquisition. In the combined CS method, the under-sampled data of the K-space is performed by taking both regular sampling and traditional random under-sampling approaches. MRI image reconstruction is then performed by using nonlinear conjugate gradient optimization. The performance of the proposed method is simulated and evaluated using the reconstruction error measure, the universal image quality Q-index, and the peak signal-to-noise ratio (PSNR). The numerical simulations confirmed that, the average error, the Q index, and the PSNR ratio of the appointed scheme are remarkably improved up to 59, 63, and 39% respectively as compared to the traditional scheme. For the first time, instead of using highly computational approaches, a simple and efficient combination of CS and pMRI is proposed for the better MRI reconstruction. These findings are very meaningful for reducing the imaging time of MRI systems.
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Affiliation(s)
- Anh Quang Tran
- Department of Biomedical Engineering, Le Quy Don Technical University, Hanoi City, Vietnam
| | - Tien-Anh Nguyen
- Department of Physics, Le Quy Don Technical University, Hanoi City, Vietnam
| | - Phuc Thinh Doan
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
- Faculty of Mechanical, Electrical, Electronic and Automotive Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
| | - Duc-Nghia Tran
- Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi City, Vietnam
| | - Duc-Tan Tran
- Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, Vietnam
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12
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Samani ZR, Alappatt JA, Parker D, Ismail AAO, Verma R. QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images. Front Neurosci 2020; 13:1456. [PMID: 32038150 PMCID: PMC6987246 DOI: 10.3389/fnins.2019.01456] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 12/31/2019] [Indexed: 12/04/2022] Open
Abstract
Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering the growing number of consortium-like studies, underlining the need for automation of the process. In this paper, we have developed a deep-learning-based automated quality control (QC) tool, QC-Automator, for dMRI data, that can handle a variety of artifacts such as motion, multiband interleaving, ghosting, susceptibility, herringbone, and chemical shifts. QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ∼332,000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts. The method is fast and paves the way for efficient and effective artifact detection in large datasets. It is also demonstrated to be replicable on other datasets with different acquisition parameters.
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Affiliation(s)
- Zahra Riahi Samani
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
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Bouhrara M, Rejimon AC, Cortina LE, Khattar N, Spencer RG. Four-angle method for practical ultra-high-resolution magnetic resonance mapping of brain longitudinal relaxation time and apparent proton density. Magn Reson Imaging 2019; 66:57-68. [PMID: 31730882 DOI: 10.1016/j.mri.2019.11.013] [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: 08/23/2019] [Revised: 11/09/2019] [Accepted: 11/10/2019] [Indexed: 10/25/2022]
Abstract
Changes in longitudinal relaxation time (T1) and proton density (PD) are sensitive indicators of microstructural alterations associated with various central nervous system diseases as well as brain maturation and aging. In this work, we introduce a new approach for rapid and accurate high-resolution (HR) or ultra HR (UHR) mapping of T1 and apparent PD (APD) of the brain with correction of radiofrequency field, B1, inhomogeneities. The four-angle method (FAM) uses four spoiled-gradient recalled-echo (SPGR) images acquired at different flip angles (FA) and short repetition times (TRs). The first two SPGR images are acquired at low-spatial resolution and used to accurately map the active B1+ field with the recently introduced steady-state double angle method (SS-DAM). The estimated B1+ map is used in conjunction with the two other SPGR images, acquired at HR or UHR, to map T1 and APD. The method is evaluated with numerical, phantom, and in-vivo imaging measurements. Furthermore, we investigated imaging acceleration methods to further shorten the acquisition time. Our results indicate that FAM provides an accurate method for simultaneous HR or UHR mapping of T1 and APD in human brain in clinical high-field MRI. Derived parameter maps without B1+correction suffer from large inaccuracies, but this issue is well-corrected through use of the SS-DAM. Furthermore, the use of SPGR imaging with short TR and phased-array coil acquisition permits substantial imaging acceleration and enables robust HR or UHR T1 and APD mapping in a clinically acceptable time frame, with whole brain coverage obtained in less than 2 min or 5 min, respectively. The method exhibits high reproducibility and benefits from the use of the conventional SPGR sequence, available in all preclinical and clinical MRI machines, and very simple modeling to address a critical outstanding issue in neuroimaging.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
| | - Abinand C Rejimon
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Luis E Cortina
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nikkita Khattar
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
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Miller WB, Torday JS, Baluška F. The N-space Episenome unifies cellular information space-time within cognition-based evolution. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2019; 150:112-139. [PMID: 31415772 DOI: 10.1016/j.pbiomolbio.2019.08.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 07/26/2019] [Accepted: 08/09/2019] [Indexed: 02/08/2023]
Abstract
Self-referential cellular homeostasis is maintained by the measured assessment of both internal status and external conditions based within an integrated cellular information field. This cellular field attachment to biologic information space-time coordinates environmental inputs by connecting the cellular senome, as the sum of the sensory experiences of the cell, with its genome and epigenome. In multicellular organisms, individual cellular information fields aggregate into a collective information architectural matrix, termed a N-space Episenome, that enables mutualized organism-wide information management. It is hypothesized that biological organization represents a dual heritable system constituted by both its biological materiality and a conjoining N-space Episenome. It is further proposed that morphogenesis derives from reciprocations between these inter-related facets to yield coordinated multicellular growth and development. The N-space Episenome is conceived as a whole cell informational projection that is heritable, transferable via cell division and essential for the synchronous integration of the diverse self-referential cells that constitute holobionts.
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Affiliation(s)
| | - John S Torday
- Department of Pediatrics, Harbor-UCLA Medical Center, USA.
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15
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Krishnamurthy R, Wang DJJ, Cervantes B, McAllister A, Nelson E, Karampinos DC, Hu HH. Recent Advances in Pediatric Brain, Spine, and Neuromuscular Magnetic Resonance Imaging Techniques. Pediatr Neurol 2019; 96:7-23. [PMID: 31023603 DOI: 10.1016/j.pediatrneurol.2019.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 02/25/2019] [Accepted: 03/03/2019] [Indexed: 12/21/2022]
Abstract
Magnetic resonance imaging (MRI) is a powerful radiologic tool with the ability to generate a variety of proton-based signal contrast from tissues. Owing to this immense flexibility in signal generation, new MRI techniques are constantly being developed, tested, and optimized for clinical utility. In addition, the safe and nonionizing nature of MRI makes it a suitable modality for imaging in children. In this review article, we summarize a few of the most popular advances in MRI techniques in recent years. In particular, we highlight how these new developments have affected brain, spine, and neuromuscular imaging and focus on their applications in pediatric patients. In the first part of the review, we discuss new approaches such as multiphase and multidelay arterial spin labeling for quantitative perfusion and angiography of the brain, amide proton transfer MRI of the brain, MRI of brachial plexus and lumbar plexus nerves (i.e., neurography), and T2 mapping and fat characterization in neuromuscular diseases. In the second part of the review, we focus on describing new data acquisition strategies in accelerated MRI aimed collectively at reducing the scan time, including simultaneous multislice imaging, compressed sensing, synthetic MRI, and magnetic resonance fingerprinting. In discussing the aforementioned, the review also summarizes the advantages and disadvantages of each method and their current state of commercial availability from MRI vendors.
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Affiliation(s)
| | - Danny J J Wang
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Barbara Cervantes
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
| | | | - Eric Nelson
- Center for Biobehavioral Health, Nationwide Children's Hospital, Columbus, Ohio
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
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16
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Bouhrara M, Spencer RG. Steady-state double-angle method for rapid B 1 mapping. Magn Reson Med 2019; 82:189-201. [PMID: 30828871 DOI: 10.1002/mrm.27708] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 01/07/2019] [Accepted: 02/02/2019] [Indexed: 12/23/2022]
Abstract
PURPOSE To introduce an accurate, rapid, and practical method for active B1 field mapping based on the double-angle method (DAM) in the steady-state (SS) signal regime. METHODS We introduced and evaluated the performance of the SS-DAM approach to map the B1 field and compared the results to those calculated from the conventional DAM approach. Similar to DAM, SS-DAM uses the signal intensity ratio of 2 magnitude images acquired with different flip angles using the spoiled gradient recalled echo sequence. However, unlike DAM, in SS-DAM, these 2 spoiled gradient recalled echo images are acquired with very short TR, which allows substantially reduced acquisition time. Numerical, phantom, and in vivo brain imaging analyses, representing a wide range of T1 s and large B1 variation, were conducted. Methods for further accelerating acquisition were also investigated. RESULTS Our results demonstrate the potential of the SS-DAM approach to be applied widely in the clinical setting. B1 maps derived from SS-DAM were demonstrated to be quantitatively comparable to those derived from DAM but were derived much more rapidly. Large-volume B1 maps were obtained at a field strength of 3 tesla within clinically acceptable acquisition times. CONCLUSION SS-DAM permits accurate B1 mapping in the clinical setting, with whole-brain coverage in less than 1 min.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
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17
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Bilal M, Shah JA, Qureshi IM, Kadir K. Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation. Int J Biomed Imaging 2018; 2018:7803067. [PMID: 29610569 PMCID: PMC5828414 DOI: 10.1155/2018/7803067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 11/19/2017] [Accepted: 12/21/2017] [Indexed: 11/27/2022] Open
Abstract
Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. The L1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated and in vivo 2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison between k-t FOCUSS with MEMC and the proposed method.
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Affiliation(s)
- Muhammad Bilal
- Electrical Engineering Department, International Islamic University, Islamabad, Islamabad, Pakistan
| | - Jawad Ali Shah
- Electrical Engineering Section, UniKL BMI, Gombak, Selangor, Malaysia
| | - Ijaz M Qureshi
- Electrical Engineering Department, Air University Islamabad, Islamabad, Pakistan
| | - Kushsairy Kadir
- Electrical Engineering Section, UniKL BMI, Gombak, Selangor, Malaysia
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Nakamura S, Nakaura T, Kidoh M, Awai K, Namimoto T, Yoshinaka I, Harada K, Yamashita Y. Efficacy of the projection onto convex sets (POCS) algorithm at Gd-EOB-DTPA-enhanced hepatobiliary-phase hepatic MRI. SPRINGERPLUS 2016; 5:1311. [PMID: 27547685 PMCID: PMC4978650 DOI: 10.1186/s40064-016-2968-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 07/29/2016] [Indexed: 11/21/2022]
Abstract
PURPOSE To investigate the efficacy of the projection onto convex sets (POCS) algorithm at Gd-EOB-DTPA-enhanced hepatobiliary-phase MRI. METHODS In phantom study, we scanned a phantom and obtained images by conventional means (P1 images), by partial-Fourier image reconstruction (PF, P2 images) and by PF with the POCS algorithm (P3 images). Then we acquired and compared subtraction images (P2-P1 images and P3-P1 images). In clinical study, 55 consecutive patients underwent Gd-EOB-DTPA (EOB)-enhanced 3D hepatobiliary-phase MRI on a 1.5T scanner. Images were obtained using conventional method (C1 images), PF (C2 images), and PF with POCS (C3 images). The acquisition time was 17-, 14-, and 14 s for protocols C1, C2 and C3, respectively. Two radiologists assigned grades for hepatic vessel sharpness and we compared the visual grading among the 3 protocols. And one radiologist compared signal-to-noise-ratio (SNR) of the hepatic parenchyma. RESULTS In phantom study, there was no difference in signal intensity on a peripheral phantom column on P3-P1 images. In clinical study, there was no significant difference between C1 and C3 images (2.62 ± 0.49 vs. 2.58 ± 0.49, p = 0.70) in the score assigned for vessel sharpness nor in SNR (13.3 ± 2.67 vs. 13.1 ± 2.51, p = 0.18). CONCLUSION The POCS algorithm makes it possible to reduce the scan time of hepatobiliary phase (from 17 to 14 s) without reducing SNR and without increasing artifacts.
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Affiliation(s)
- Shinichi Nakamura
- Department of Diagnostic Radiology, Amakusa Regional Medical Center, Amakusa, Japan
- Department of Radiology, Kumamoto Rousai Hospital, 1670, Takehara-Machi, Yatushiro, Kumamoto 866-8533 Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Amakusa Regional Medical Center, Amakusa, Japan
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Amakusa Regional Medical Center, Amakusa, Japan
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Kazuo Awai
- Diagnostic Radiology, School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Tomohiro Namimoto
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Ichiro Yoshinaka
- Department of Surgery, Amakusa Regional Medical Center, Amakusa, Japan
| | - Kazunori Harada
- Department of Surgery, Amakusa Regional Medical Center, Amakusa, Japan
| | - Yasuyuki Yamashita
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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Ortiz R, Morales JM, Ruiz-Espana S, Bodi V, Monleon D, Moratal D. Magnetic resonance microimaging of a swine infarcted heart: Performing cardiac virtual histologies. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:1584-7. [PMID: 26736576 DOI: 10.1109/embc.2015.7318676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The aim of this study is to develop a computer-aided intuitive software tool based on MATLAB to reproduce the functions of a virtual histology over Magnetic Resonance (MR) microimages of small samples of swine's infarcted hearts. The basic characterization consists of selecting regions of interest (ROIs) of that MR microimage and extracting the most important information of these regions. The software tool will implement intuitive and sophisticated tools that allow the user to define ROIs on the different types of images provided by the MR scanner. The final purpose of this tool will be to analyze the acquired data in order to characterize some aspects of the later possible events after a myocardial infarction in swine's hearts and expand the study to human cases.
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Liu CH. Anatomical, functional and molecular biomarker applications of magnetic resonance neuroimaging. FUTURE NEUROLOGY 2015; 10:49-65. [DOI: 10.2217/fnl.14.60] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
ABSTRACT MRI and magnetic resonance spectroscopy (MRS) along with computed tomography and PET are the most common imaging modalities used in the clinics to detect structural abnormalities and pathological conditions in the brain. MRI generates superb image resolution/contrast without radiation exposure that is associated with computed tomography and PET; MRS and spectroscopic imaging technologies allow us to measure changes in brain biochemistry. Increasingly, neurobiologists and MRI scientists are collaborating to solve neuroscience problems across sub-cellular through anatomical levels. To achieve successful cross-disciplinary collaborations, neurobiologists must have sufficient knowledge of magnetic resonance principles and applications in order to effectively communicate with their MRI colleagues. This review provides an overview of magnetic resonance techniques and how they can be used to gain insight into the active brain at the anatomical, functional and molecular levels with the goal of encouraging neurobiologists to include MRI/MRS as a research tool in their endeavors.
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Evans BAJ, James TW, James K, Cox A, Farr L, Paisey SJ, Dempster DW, Stone MD, Griffiths PA, Hugtenburg RP, Brady SM, Wells T. Preclinical assessment of a new magnetic resonance-based technique for determining bone quality by characterization of trabecular microarchitecture. Calcif Tissue Int 2014; 95:506-20. [PMID: 25380571 DOI: 10.1007/s00223-014-9922-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 10/17/2014] [Indexed: 10/24/2022]
Abstract
The utility of HR-CT to study longitudinal changes in bone microarchitecture is limited by subject radiation exposure. Although MR is not subject to this limitation, it is limited both by patient movement that occurs during prolonged scanning at distal sites, and by the signal-to-noise ratio that is achievable for high-resolution images in a reasonable scan time at proximal sites. Recently, a novel MR-based technique, fine structure analysis (FSA) (Chase et al. Localised one-dimensional magnetic resonance spatial frequency spectroscopy. PCT/US2012/068284 2012, James and Chase Magnetic field gradient structure characteristic assessment using one-dimensional (1D) spatial frequency distribution analysis. 7932720 B2, 2011) has been developed which provides both high-resolution and fast scan times, but which generates at a designated set of spatial positions (voxels) a one-dimensional signal of spatial frequencies. Appendix 1 provides a brief introduction to FSA. This article describes an initial exploration of FSA for the rapid, non-invasive characterization of trabecular microarchitecture in a preclinical setting. For L4 vertebrae of sham and ovariectomized (OVX) rats, we compared FSA-generated metrics with those from CT datasets and from CT-derived histomorphometry parameters, trabecular number (Tb.N), bone volume density (BV/TV), trabecular thickness (Tb.Th) and trabecular separation (Tb.Sp). OVX caused a reduction of the higher frequency structures that correspond to a denser trabecular lattice, while increasing the preponderance of lower frequency structures, which correspond to a more open lattice. As one example measure, the centroid of the FSA spectrum (which we refer to as fSAcB) showed strong correlation in the same region with CT-derived histomorphometry values: Tb.Sp: r -0.63, p < 0.001; Tb.N: r 0.71, p < 0.001; BV/TV: r 0.64, p < 0.001, Tb.Th: r 0.44, p < 0.05. Furthermore, we found a 17.5% reduction in fSAcB in OVX rats (p < 0.0001). In a longitudinal study, FSA showed that the age-related increase in higher frequency structures was abolished in OVX rats, being replaced with a 78-194% increase in lower frequency structures (2.4-2.8 objects/mm range), indicating a more sparse trabecular lattice (p < 0.05). The MR-based fine structure analysis enables high-resolution, radiation-free, rapid quantification of bone structures in one dimension (the specific point and direction being chosen by the clinician) of the spine.
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Affiliation(s)
- B A J Evans
- Institute of Molecular and Experimental Medicine, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
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Park SB, Moon MH, Sung CK, Oh S, Lee YH. Dynamic Contrast-Enhanced MR Imaging of Endometrial Cancer: Optimizing the Imaging Delay for Tumour-Myometrium Contrast. Eur Radiol 2014; 24:2795-9. [PMID: 25056550 DOI: 10.1007/s00330-014-3327-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 05/01/2014] [Accepted: 07/08/2014] [Indexed: 01/30/2023]
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23
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Wang J, He L, Zheng H, Lu ZL. Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence. PLoS One 2014; 9:e96899. [PMID: 24879508 PMCID: PMC4039442 DOI: 10.1371/journal.pone.0096899] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 04/13/2014] [Indexed: 11/19/2022] Open
Abstract
The three-dimension (3D) magnetization-prepared rapid gradient-echo (MP-RAGE) sequence is one of the most popular sequences for structural brain imaging in clinical and research settings. The sequence captures high tissue contrast and provides high spatial resolution with whole brain coverage in a short scan time. In this paper, we first computed the optimal k-space sampling by optimizing the contrast of simulated images acquired with the MP-RAGE sequence at 3.0 Tesla using computer simulations. Because the software of our scanner has only limited settings for k-space sampling, we then determined the optimal k-space sampling for settings that can be realized on our scanner. Subsequently we optimized several major imaging parameters to maximize normal brain tissue contrasts under the optimal k-space sampling. The optimal parameters are flip angle of 12°, effective inversion time within 900 to 1100 ms, and delay time of 0 ms. In vivo experiments showed that the quality of images acquired with our optimal protocol was significantly higher than that of images obtained using recommended protocols in prior publications. The optimization of k-spacing sampling and imaging parameters significantly improved the quality and detection sensitivity of brain images acquired with MP-RAGE.
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Affiliation(s)
- Jinghua Wang
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail:
| | - Lili He
- Center for Perinatal Research, Nationwide Children’s Hospital, Columbus, Ohio, United States of America
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhong-Lin Lu
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, United States of America
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