1
|
Kazimierczyk R, Kaminski KA, Nekolla SG. Cardiac PET/MRI: Recent Developments and Future Aspects. Semin Nucl Med 2024; 54:733-746. [PMID: 38853039 DOI: 10.1053/j.semnuclmed.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 06/11/2024]
Abstract
Positron emission tomography/magnetic resonance (PET/MRI) hybrid imaging is now available for over a decade and although the quantity of installed systems is rather low, the number of emerging applications for cardiovascular diseases is still growing. PET/MRI provides integrated images of high quality anatomical and functional assessment obtained by MRI with the possibilities of PET for quantification of molecular parameters such as metabolism, inflammation, and perfusion. In recent years, sequential co-registration of myocardial tissue characterization with its molecular data had become an increasingly helpful tool in clinical practice and an integrated device simplifies this task. This review summarizes recent developments and future possibilities in the use of the PET/MRI in the diagnosis and treatment of cardiovascular disorders.
Collapse
Affiliation(s)
| | - Karol A Kaminski
- Department of Cardiology, Medical University of Bialystok, Bialystok, Poland; Department of Population Medicine and Lifestyle Diseases, Medical University of Bialystok, Bialystok, Poland
| | - Stephan G Nekolla
- Department of Nuclear Medicine, Technical University Munich, Ismaninger Str., Munich, Germany; DZKH (Deutsches Zentrum für Herz-Kreislauf-Forschung e.V.), partner site Munich Heart Alliance, Munich, Germany.
| |
Collapse
|
2
|
Ebrahimi S, Lundström E, Batasin SJ, Hedlund E, Stålberg K, Ehman EC, Sheth VR, Iranpour N, Loubrie S, Schlein A, Rakow-Penner R. Application of PET/MRI in Gynecologic Malignancies. Cancers (Basel) 2024; 16:1478. [PMID: 38672560 PMCID: PMC11048306 DOI: 10.3390/cancers16081478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
The diagnosis, treatment, and management of gynecologic malignancies benefit from both positron emission tomography/computed tomography (PET/CT) and MRI. PET/CT provides important information on the local extent of disease as well as diffuse metastatic involvement. MRI offers soft tissue delineation and loco-regional disease involvement. The combination of these two technologies is key in diagnosis, treatment planning, and evaluating treatment response in gynecological malignancies. This review aims to assess the performance of PET/MRI in gynecologic cancer patients and outlines the technical challenges and clinical advantages of PET/MR systems when specifically applied to gynecologic malignancies.
Collapse
Affiliation(s)
- Sheida Ebrahimi
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Elin Lundström
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Surgical Sciences, Radiology, Uppsala University, 751 85 Uppsala, Sweden
- Center for Medical Imaging, Uppsala University Hospital, 751 85 Uppsala, Sweden
| | - Summer J. Batasin
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Elisabeth Hedlund
- Department of Surgical Sciences, Radiology, Uppsala University, 751 85 Uppsala, Sweden
| | - Karin Stålberg
- Department of Women’s and Children’s Health, Uppsala University, 751 85 Uppsala, Sweden
| | - Eric C. Ehman
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Vipul R. Sheth
- Department of Radiology, Stanford University, Palo Alto, CA 94305, USA; (V.R.S.)
| | - Negaur Iranpour
- Department of Radiology, Stanford University, Palo Alto, CA 94305, USA; (V.R.S.)
| | - Stephane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Alexandra Schlein
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| |
Collapse
|
3
|
Aizaz M, van der Pol JAJ, Schneider A, Munoz C, Holtackers RJ, van Cauteren Y, van Langen H, Meeder JG, Rahel BM, Wierts R, Botnar RM, Prieto C, Moonen RPM, Kooi ME. Extended MRI-based PET motion correction for cardiac PET/MRI. EJNMMI Phys 2024; 11:36. [PMID: 38581561 PMCID: PMC10998820 DOI: 10.1186/s40658-024-00637-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/25/2024] [Indexed: 04/08/2024] Open
Abstract
PURPOSE A 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data. METHODS Twelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30-90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively. RESULTS A pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio. CONCLUSION The proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio. Trial registration METC162043 registered 01/03/2017.
Collapse
Affiliation(s)
- Mueez Aizaz
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Jochem A J van der Pol
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Alina Schneider
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Robert J Holtackers
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Yvonne van Cauteren
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Herman van Langen
- Department of Medical Physics and Devices, VieCuri Medical Centre, Venlo, The Netherlands
| | - Joan G Meeder
- Department of Cardiology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Braim M Rahel
- Department of Cardiology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Roel Wierts
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
- Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
| | - Rik P M Moonen
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - M Eline Kooi
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands.
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands.
| |
Collapse
|
4
|
Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Nuklearmedizin 2023; 62:306-313. [PMID: 37802058 DOI: 10.1055/a-2157-6670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
BACKGROUND Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. RESULTS AND CONCLUSION In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. KEY POINTS · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..
Collapse
Affiliation(s)
- Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Tobias Hepp
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Ferdinand Seith
- Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| |
Collapse
|
5
|
Zhong H, Ren L, Lu Y, Liu Y. On the correction of respiratory motion-induced image reconstruction errors in positron-emission tomography-guided radiation therapy. Phys Imaging Radiat Oncol 2023; 26:100430. [PMID: 36970447 PMCID: PMC10036920 DOI: 10.1016/j.phro.2023.100430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/04/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023] Open
Abstract
Background and purpose Free breathing (FB) positron emission tomography (PET) images are routinely used in radiotherapy for lung cancer patients. Respiration-induced artifacts in these images compromise treatment response assessment and obstruct clinical implementation of dose painting and PET-guided radiotherapy. The purpose of this study is to develop a blurry image decomposition (BID) method to correct motion-induced image-reconstruction errors in FB-PETs. Materials and methods Assuming a blurry PET is represented as an average of multi-phase PETs. A four-dimensional computed-tomography image is deformably registered from the end-inhalation (EI) phase to other phases. With the registration-derived deformation maps, PETs at other phases can be deformed from a PET at the EI phase. To reconstruct the EI-PET, the difference between the blurry PET and the average of the deformed EI-PETs is minimized using a maximum-likelihood expectation-maximization algorithm. The developed method was evaluated with computational and physical phantoms as well as PET/CT images acquired from three patients. Results The BID method increased the signal-to-noise ratio from 1.88 ± 1.05 to 10.5 ± 3.3 and universal-quality index from 0.72 ± 0.11 to 1.0 for the computational phantoms, and reduced the motion-induced error from 69.9% to 10.9% in the maximum of activity concentration and from 317.5% to 8.7% in the full width at half maximum of the physical PET-phantom. The BID-based corrections increased the maximum standardized-uptake values by 17.7 ± 15.4% and reduced tumor volumes by 12.5 ± 10.4% on average for the three patients. Conclusions The proposed image-decomposition method reduces respiration-induced errors in PET images and holds potential to improve the quality of radiotherapy for thoracic and abdominal cancer patients.
Collapse
|
6
|
Wang Z, She H, Zhang Y, Du YP. Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI. Med Image Anal 2023; 84:102701. [PMID: 36470148 DOI: 10.1016/j.media.2022.102701] [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: 09/10/2021] [Revised: 10/02/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022]
Abstract
Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical and physiological limitations. The spatial-temporal dictionary learning (DL) approach accelerates dynamic MRI by learning spatial-temporal correlations, but the regularization parameters need to be manually adjusted, the performance at high acceleration rate is limited, and the reconstruction can be time-consuming. Deep learning techniques have shown good performance in accelerating MRI due to the powerful representational capabilities of neural networks. In this work, we propose a parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) framework that combines dictionary learning with deep learning algorithms and utilizes the spatial-temporal prior information of dynamic MRI data to achieve better reconstruction quality and efficiency. The coefficient estimation modules (CEM) are designed in the framework to adaptively adjust the regularization coefficients. Experimental results show that combining dictionary learning with deep neural networks and using spatial-temporal dictionaries can obviously improve the image quality and computational efficiency compared with the state-of-the-art non-Cartesian imaging methods for accelerating the 4D-MRI especially at high acceleration rate.
Collapse
Affiliation(s)
- Zhijun Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Huajun She
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Yufei Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yiping P Du
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| |
Collapse
|
7
|
Recent Advances in Cardiovascular Diseases Research Using Animal Models and PET Radioisotope Tracers. Int J Mol Sci 2022; 24:ijms24010353. [PMID: 36613797 PMCID: PMC9820417 DOI: 10.3390/ijms24010353] [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: 11/10/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Cardiovascular diseases (CVD) is a collective term describing a range of conditions that affect the heart and blood vessels. Due to the varied nature of the disorders, distinguishing between their causes and monitoring their progress is crucial for finding an effective treatment. Molecular imaging enables non-invasive visualisation and quantification of biological pathways, even at the molecular and subcellular levels, what is essential for understanding the causes and development of CVD. Positron emission tomography imaging is so far recognized as the best method for in vivo studies of the CVD related phenomena. The imaging is based on the use of radioisotope-labelled markers, which have been successfully used in both pre-clinical research and clinical studies. Current research on CVD with the use of such radioconjugates constantly increases our knowledge and understanding of the causes, and brings us closer to effective monitoring and treatment. This review outlines recent advances in the use of the so-far available radioisotope markers in the research on cardiovascular diseases in rodent models, points out the problems and provides a perspective for future applications of PET imaging in CVD studies.
Collapse
|
8
|
Guo X, Zhou B, Pigg D, Spottiswoode B, Casey ME, Liu C, Dvornek NC. Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network. Med Image Anal 2022; 80:102524. [PMID: 35797734 PMCID: PMC10923189 DOI: 10.1016/j.media.2022.102524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 06/08/2022] [Accepted: 06/24/2022] [Indexed: 11/24/2022]
Abstract
Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, but have yet been investigated with consideration for tracer distribution changes or in the whole-body scope. In this work, we developed an unsupervised automatic deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. Our dataset contains 27 subjects each under a 90-min FDG whole-body dynamic PET scan. Evaluating performance in motion simulation studies and a 9-fold cross-validation on the human subject dataset, compared with both traditional and deep learning baselines, we demonstrated that the proposed network achieved the lowest motion prediction error, obtained superior performance in enhanced qualitative and quantitative spatial alignment between parametric Ki and Vb images, and significantly reduced parametric fitting error. We also showed the potential of the proposed motion correction method for impacting downstream analysis of the estimated parametric images, improving the ability to distinguish malignant from benign hypermetabolic regions of interest. Once trained, the motion estimation inference time of our proposed network was around 460 times faster than the conventional registration baseline, showing its potential to be easily applied in clinical settings.
Collapse
Affiliation(s)
- Xueqi Guo
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - David Pigg
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | | | - Michael E Casey
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA.
| | - Nicha C Dvornek
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA.
| |
Collapse
|
9
|
Aizaz M, van der Pol JAJ, Wierts R, Zwart H, van der Werf AJ, Wildberger JE, Bucerius JA, Moonen RPM, Kooi ME. Evaluation of a Dedicated Radiofrequency Carotid PET/MRI Coil. J Clin Med 2022; 11:jcm11092569. [PMID: 35566694 PMCID: PMC9101928 DOI: 10.3390/jcm11092569] [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: 01/26/2022] [Revised: 04/27/2022] [Accepted: 05/02/2022] [Indexed: 01/25/2023] Open
Abstract
Carotid radiofrequency coils inside a PET/MRI system can result in PET quantification errors. We compared the performance of a dedicated PET/MRI carotid coil against a coil for MRI-only use. An 18F-fluorodeoxyglucose (18F-FDG) phantom was scanned without and with an MRI-only coil and with the PET/MRI coil. The decay-corrected normalized activity was compared for the different coil configurations. Eighteen patients were scanned with the three coil configurations. The maximal standardized uptake values (SUVmax) and signal-to-noise ratios (SNR) were calculated. Repeated measures ANOVA was performed to assess the differences in SUVmax and SNR between the coil configurations. In the phantom study, the PET/MRI coil demonstrated a slight decrease (<5%), while the MRI-only coil showed a substantial decrease (up to 10%) in normalized activity at the position of coil elements compared to no dedicated coil configuration. In the patient study, the SUVmax values for both no surface coil (3.59 ± 0.15) and PET/MRI coil (3.54 ± 0.15) were significantly higher (p = 0.03 and p = 0.04, respectively) as compared to the MRI-only coil (3.28 ± 0.16). No significant difference was observed between PET/MRI and no surface coil (p = 1.0). The SNR values for both PET/MRI (7.31 ± 0.44) and MRI-only (7.62 ± 0.42) configurations demonstrated significantly higher (p < 0.001) SNR values as compared to the no surface coil (3.78 ± 0.22), while no significant difference was observed in SNR between the PET/MRI and MRI-only coil (p = 1.0). This study demonstrated that the PET/MRI coil can be used for PET imaging without requiring attenuation correction while acquiring high-resolution MR images.
Collapse
Affiliation(s)
- Mueez Aizaz
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands; (M.A.); (J.A.J.v.d.P.); (R.W.); (J.E.W.); (J.A.B.); (R.P.M.M.)
- CARIM School for Cardiovascular Diseases, 6229 ER Maastricht, The Netherlands
| | - Jochem A. J. van der Pol
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands; (M.A.); (J.A.J.v.d.P.); (R.W.); (J.E.W.); (J.A.B.); (R.P.M.M.)
| | - Roel Wierts
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands; (M.A.); (J.A.J.v.d.P.); (R.W.); (J.E.W.); (J.A.B.); (R.P.M.M.)
| | - Hans Zwart
- Machnet B.V, 9301 LK Roden, The Netherlands; (H.Z.); (A.J.v.d.W.)
| | | | - Joachim E. Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands; (M.A.); (J.A.J.v.d.P.); (R.W.); (J.E.W.); (J.A.B.); (R.P.M.M.)
- CARIM School for Cardiovascular Diseases, 6229 ER Maastricht, The Netherlands
| | - Jan A. Bucerius
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands; (M.A.); (J.A.J.v.d.P.); (R.W.); (J.E.W.); (J.A.B.); (R.P.M.M.)
- CARIM School for Cardiovascular Diseases, 6229 ER Maastricht, The Netherlands
- Department of Nuclear Medicine, University Medicine Goettingen, Georg-August-University Goettingen, 37073 Goettingen, Germany
| | - Rik P. M. Moonen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands; (M.A.); (J.A.J.v.d.P.); (R.W.); (J.E.W.); (J.A.B.); (R.P.M.M.)
- CARIM School for Cardiovascular Diseases, 6229 ER Maastricht, The Netherlands
| | - Marianne Eline Kooi
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands; (M.A.); (J.A.J.v.d.P.); (R.W.); (J.E.W.); (J.A.B.); (R.P.M.M.)
- CARIM School for Cardiovascular Diseases, 6229 ER Maastricht, The Netherlands
- Correspondence: ; Tel.: +31-43-387-4910
| |
Collapse
|
10
|
Chen S, Fraum TJ, Eldeniz C, Mhlanga J, Gan W, Vahle T, Krishnamurthy UB, Faul D, Gach HM, Binkley MM, Kamilov US, Laforest R, An H. MR-assisted PET respiratory motion correction using deep-learning based short-scan motion fields. Magn Reson Med 2022; 88:676-690. [PMID: 35344592 DOI: 10.1002/mrm.29233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/03/2022] [Accepted: 02/23/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan. METHODS The evaluation of PET MoCo was performed in a respiratory motion phantom study with varying lesion sizes and tumor to background ratios (TBRs) using a static scan as the ground truth. MRI-based MVFs were derived from either 2000 spokes (MoCo2000 , 5-6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCoP2P200 , 30-40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesions, the performance of PET MoCo was evaluated using quantitative metrics (SUVmax , SUVpeak , SUVmean , lesion volume) and a blinded radiological review on lesion conspicuity. RESULTS MRI-assisted PET MoCo methods provided similar results to static scans across most lesions with varying TBRs in the phantom. Both MoCo2000 and MoCoP2P200 PET images had significantly higher SUVmax , SUVpeak , SUVmean and significantly lower lesion volume than non-motion-corrected (non-MoCo) PET images. There was no statistical difference between MoCo2000 and MoCoP2P200 PET images for SUVmax , SUVpeak , SUVmean or lesion volume. Both radiological reviewers found that MoCo2000 and MoCoP2P200 PET significantly improved lesion conspicuity. CONCLUSION An MRI-assisted PET MoCo method was evaluated using the ground truth in a phantom study. In patients with hepatic lesions, PET MoCo images improved quantitative and qualitative metrics based on only 30-40 s of MRI motion modeling data.
Collapse
Affiliation(s)
- Sihao Chen
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tyler J Fraum
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Joyce Mhlanga
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Weijie Gan
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - David Faul
- Siemens Medical Solutions USA, Inc., Malvern, PA, USA
| | - H Michael Gach
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.,Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.,Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael M Binkley
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ulugbek S Kamilov
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA.,Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Hongyu An
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.,Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA.,Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
11
|
Ismail TF, Strugnell W, Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med 2022; 9:826283. [PMID: 35310962 PMCID: PMC8927633 DOI: 10.3389/fcvm.2022.826283] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
Collapse
Affiliation(s)
- Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Wendy Strugnell
- Queensland X-Ray, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - Chiara Coletti
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Maša Božić-Iven
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | | | - Kerstin Hammernik
- Lab for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre of Marine Sciences, Faro, Portugal
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
| |
Collapse
|
12
|
Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. ROFO-FORTSCHR RONTG 2022; 194:605-612. [PMID: 35211929 DOI: 10.1055/a-1718-4128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. RESULTS AND CONCLUSION In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. KEY POINTS · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET.. CITATION FORMAT · Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1718-4128.
Collapse
Affiliation(s)
- Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Tobias Hepp
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Ferdinand Seith
- Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| |
Collapse
|
13
|
Munoz C, Qi H, Cruz G, Küstner T, Botnar RM, Prieto C. Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography. Magn Reson Imaging 2022; 85:10-18. [PMID: 34655727 DOI: 10.1016/j.mri.2021.10.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 10/01/2021] [Accepted: 10/10/2021] [Indexed: 11/17/2022]
Abstract
PURPOSE To accelerate non-rigid motion corrected coronary MR angiography (CMRA) reconstruction by developing a deep learning based non-rigid motion estimation network and combining this with an efficient implementation of the undersampled motion corrected reconstruction. METHODS Undersampled and respiratory motion corrected CMRA with overall short scans of 5 to 10 min have been recently proposed. However, image reconstruction with this approach remains lengthy, since it relies on several non-rigid image registrations to estimate the respiratory motion and on a subsequent iterative optimization to correct for motion during the undersampled reconstruction. Here we introduce a self-supervised diffeomorphic non-rigid respiratory motion estimation network, DiRespME-net, to speed up respiratory motion estimation. We couple this with an efficient GPU-based implementation of the subsequent motion-corrected iterative reconstruction. DiRespME-net is based on a U-Net architecture, and is trained in a self-supervised fashion, with a loss enforcing image similarity and spatial smoothness of the motion fields. Motion predicted by DiRespME-net was used for GPU-based motion-corrected CMRA in 12 test subjects and final images were compared to those produced by state-of-the-art reconstruction. Vessel sharpness and visible length of the right coronary artery (RCA) and the left anterior descending (LAD) coronary artery were used as metrics of image quality for comparison. RESULTS No statistically significant difference in image quality was found between images reconstructed with the proposed approach (MC:DiRespME-net) and a motion-corrected reconstruction using cubic B-splines (MC:Nifty-reg). Visible vessel length was not significantly different between methods (RCA: MC:Nifty-reg 5.7 ± 1.7 cm vs MC:DiRespME-net 5.8 ± 1.7 cm, P = 0.32; LAD: MC:Nifty-reg 7.0 ± 2.6 cm vs MC:DiRespME-net 6.9 ± 2.7 cm, P = 0.81). Similarly, no statistically significant difference between methods was observed in terms of vessel sharpness (RCA: MC:Nifty-reg 60.3 ± 7.2% vs MC:DiRespME-net 61.0 ± 6.8%, P = 0.19; LAD: MC:Nifty-reg 57.4 ± 7.9% vs MC:DiRespME-net 58.1 ± 7.5%, P = 0.27). The proposed approach achieved a 50-fold reduction in computation time, resulting in a total reconstruction time of approximately 20 s. CONCLUSIONS The proposed self-supervised learning-based motion corrected reconstruction enables fast motion-corrected CMRA image reconstruction, holding promise for integration in clinical routine.
Collapse
Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Küstner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Medical Image and Data Analysis, Department of Interventional and Diagnostic Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
14
|
Bogdanovic B, Solari EL, Villagran Asiares A, McIntosh L, van Marwick S, Schachoff S, Nekolla SG. PET/MR Technology: Advancement and Challenges. Semin Nucl Med 2021; 52:340-355. [PMID: 34969520 DOI: 10.1053/j.semnuclmed.2021.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 01/07/2023]
Abstract
When this article was written, it coincided with the 11th anniversary of the installation of our PET/MR device in Munich. In fact, this was the first fully integrated device to be in clinical use. During this time, we have observed many interesting behaviors, to put it kindly. However, it is more critical that in this process, our understanding of the system also improved - including the advantages and limitations from a technical, logistical, and medical perspective. The last decade of PET/MRI research has certainly been characterized by most sites looking for a "key application." There were many ideas in this context and before and after the devices became available, some of which were based on the earlier work with integrating data from single devices. These involved validating classical PET methods with MRI (eg, perfusion or oncology diagnostics). More important, however, were the scenarios where intermodal synergies could be expected. In this review, we look back on this decade-long journey, at the challenges overcome and those still to come.
Collapse
Affiliation(s)
- Borjana Bogdanovic
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Esteban Lucas Solari
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Alberto Villagran Asiares
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Lachlan McIntosh
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Sandra van Marwick
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Sylvia Schachoff
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Stephan G Nekolla
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
| |
Collapse
|
15
|
Lamare F, Bousse A, Thielemans K, Liu C, Merlin T, Fayad H, Visvikis D. PET respiratory motion correction: quo vadis? Phys Med Biol 2021; 67. [PMID: 34915465 DOI: 10.1088/1361-6560/ac43fc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022]
Abstract
Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.
Collapse
Affiliation(s)
- Frederic Lamare
- Nuclear Medicine Department, University Hospital Centre Bordeaux Hospital Group South, ., Bordeaux, Nouvelle-Aquitaine, 33604, FRANCE
| | - Alexandre Bousse
- LaTIM, INSERM UMR1101, Université de Bretagne Occidentale, ., Brest, Bretagne, 29285, FRANCE
| | - Kris Thielemans
- University College London Institute of Nuclear Medicine, UCL Hospital, Tower 5, 235 Euston Road, London, NW1 2BU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Chi Liu
- Department of Diagnostic Radiology, Yale University School of Medicine Department of Radiology and Biomedical Imaging, PO Box 208048, 801 Howard Avenue, New Haven, Connecticut, 06520-8042, UNITED STATES
| | - Thibaut Merlin
- LaTIM, INSERM UMR1101, Universite de Bretagne Occidentale, ., Brest, Bretagne, 29285, FRANCE
| | - Hadi Fayad
- Weill Cornell Medicine - Qatar, ., Doha, ., QATAR
| | - Dimitris Visvikis
- LaTIM, UMR1101, Universite de Bretagne Occidentale, INSERM, Brest, Bretagne, 29285, FRANCE
| |
Collapse
|
16
|
Kustner T, Pan J, Qi H, Cruz G, Gilliam C, Blu T, Yang B, Gatidis S, Botnar R, Prieto C. LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3686-3697. [PMID: 34242163 DOI: 10.1109/tmi.2021.3096131] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.
Collapse
|
17
|
Mohammadi I, Castro IF, Rahmim A, Veloso JFCA. Motion in nuclear cardiology imaging: types, artifacts, detection and correction techniques. Phys Med Biol 2021; 67. [PMID: 34826826 DOI: 10.1088/1361-6560/ac3dc7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 11/26/2021] [Indexed: 11/12/2022]
Abstract
In this paper, the authors review the field of motion detection and correction in nuclear cardiology with single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging systems. We start with a brief overview of nuclear cardiology applications and description of SPECT and PET imaging systems, then explaining the different types of motion and their related artefacts. Moreover, we classify and describe various techniques for motion detection and correction, discussing their potential advantages including reference to metrics and tasks, particularly towards improvements in image quality and diagnostic performance. In addition, we emphasize limitations encountered in different motion detection and correction methods that may challenge routine clinical applications and diagnostic performance.
Collapse
Affiliation(s)
- Iraj Mohammadi
- Department of Physics, University of Aveiro, Aveiro, PORTUGAL
| | - I Filipe Castro
- i3n Physics Department, Universidade de Aveiro, Aveiro, PORTUGAL
| | - Arman Rahmim
- Radiology and Physics, The University of British Columbia, Vancouver, British Columbia, CANADA
| | | |
Collapse
|
18
|
Polycarpou I, Soultanidis G, Tsoumpas C. Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200207. [PMID: 34218675 PMCID: PMC8255946 DOI: 10.1098/rsta.2020.0207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 05/04/2023]
Abstract
Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
Collapse
Affiliation(s)
- Irene Polycarpou
- Department of Health Sciences, European University of Cyprus, Nicosia, Cyprus
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charalampos Tsoumpas
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Biomedical Imaging Science Department, University of Leeds, West Yorkshire, UK
- Invicro, London, UK
| |
Collapse
|
19
|
Min LA, Castagnoli F, Vogel WV, Vellenga JP, van Griethuysen JJM, Lahaye MJ, Maas M, Beets Tan RGH, Lambregts DMJ. A decade of multi-modality PET and MR imaging in abdominal oncology. Br J Radiol 2021; 94:20201351. [PMID: 34387508 PMCID: PMC9328040 DOI: 10.1259/bjr.20201351] [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] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To investigate trends observed in a decade of published research on multimodality PET(/CT)+MR imaging in abdominal oncology, and to explore how these trends are reflected by the use of multimodality imaging performed at our institution. METHODS First, we performed a literature search (2009-2018) including all papers published on the multimodality combination of PET(/CT) and MRI in abdominal oncology. Retrieved papers were categorized according to a structured labelling system, including study design and outcome, cancer and lesion type under investigation and PET-tracer type. Results were analysed using descriptive statistics and evolutions over time were plotted graphically. Second, we performed a descriptive analysis of the numbers of MRI, PET/CT and multimodality PET/CT+MRI combinations (performed within a ≤14 days interval) performed during a similar time span at our institution. RESULTS Published research papers involving multimodality PET(/CT)+MRI combinations showed an impressive increase in numbers, both for retrospective combinations of PET/CT and MRI, as well as hybrid PET/MRI. Main areas of research included new PET-tracers, visual PET(/CT)+MRI assessment for staging, and (semi-)quantitative analysis of PET-parameters compared to or combined with MRI-parameters as predictive biomarkers. In line with literature, we also observed a vast increase in numbers of multimodality PET/CT+MRI imaging in our institutional data. CONCLUSIONS The tremendous increase in published literature on multimodality imaging, reflected by our institutional data, shows the continuously growing interest in comprehensive multivariable imaging evaluations to guide oncological practice. ADVANCES IN KNOWLEDGE The role of multimodality imaging in oncology is rapidly evolving. This paper summarizes the main applications and recent developments in multimodality imaging, with a specific focus on the combination of PET+MRI in abdominal oncology.
Collapse
Affiliation(s)
- Lisa A Min
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | | | - Wouter V Vogel
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jisk P Vellenga
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands.,Faculty or Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| |
Collapse
|
20
|
Mayer J, Jin Y, Wurster TH, Makowski MR, Kolbitsch C. Evaluation of synergistic image registration for motion-corrected coronary NaF-PET-MR. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200202. [PMID: 33966463 PMCID: PMC8107649 DOI: 10.1098/rsta.2020.0202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Coronary artery disease (CAD) is caused by the formation of plaques in the coronary arteries and is one of the most common cardiovascular diseases. NaF-PET can be used to assess plaque composition, which could be important for therapy planning. One of the main challenges of NaF-PET is cardiac and respiratory motion which can strongly impair diagnostic accuracy. In this study, we investigated the use of a synergistic image registration approach which combined motion-resolved MR and PET data to estimate cardiac and respiratory motion. This motion estimation could then be used to improve the NaF-PET image quality. The approach was evaluated with numerical simulations and in vivo scans of patients suffering from CAD. In numerical simulations, it was shown, that combining MR and PET information can improve the accuracy of motion estimation by more than 15%. For the in vivo scans, the synergistic image registration led to an improvement in uptake visualization. This is the first study to assess the benefit of combining MR and NaF-PET for cardiac and respiratory motion estimation. Further patient evaluation is required to fully evaluate the potential of this approach. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
Collapse
Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Yining Jin
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Thomas-Heinrich Wurster
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Marcus R. Makowski
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| |
Collapse
|
21
|
Kyme AZ, Fulton RR. Motion estimation and correction in SPECT, PET and CT. Phys Med Biol 2021; 66. [PMID: 34102630 DOI: 10.1088/1361-6560/ac093b] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 06/08/2021] [Indexed: 11/11/2022]
Abstract
Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.
Collapse
Affiliation(s)
- Andre Z Kyme
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, AUSTRALIA
| | - Roger R Fulton
- Sydney School of Health Sciences, The University of Sydney, Sydney, New South Wales, AUSTRALIA
| |
Collapse
|
22
|
Munoz C, Ellis S, Nekolla SG, Kunze KP, Vitadello T, Neji R, Botnar RM, Schnabel JA, Reader AJ, Prieto C. MR-guided motion-corrected PET image reconstruction for cardiac PET-MR. J Nucl Med 2021; 62:jnumed.120.254235. [PMID: 34049978 PMCID: PMC8612202 DOI: 10.2967/jnumed.120.254235] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 11/16/2022] Open
Abstract
Simultaneous PET-MR imaging has shown potential for the comprehensive assessment of myocardial health from a single examination. Furthermore, MR-derived respiratory motion information has been shown to improve PET image quality by incorporating this information into the PET image reconstruction. Separately, MR-based anatomically guided PET image reconstruction has been shown to perform effective denoising, but this has been so far demonstrated mainly in brain imaging. To date the combined benefits of motion compensation and anatomical guidance have not been demonstrated for myocardial PET-MR imaging. This work addresses this by proposing a single cardiac PET-MR image reconstruction framework which fully utilises MR-derived information to allow both motion compensation and anatomical guidance within the reconstruction. Methods: Fifteen patients underwent a 18F-FDG cardiac PET-MR scan with a previously introduced acquisition framework. The MR data processing and image reconstruction pipeline produces respiratory motion fields and a high-resolution respiratory motion-corrected MR image with good tissue contrast. This MR-derived information was then included in a respiratory motion-corrected, cardiac-gated, anatomically guided image reconstruction of the simultaneously acquired PET data. Reconstructions were evaluated by measuring myocardial contrast and noise and compared to images from several comparative intermediate methods using the components of the proposed framework separately. Results: Including respiratory motion correction, cardiac gating, and anatomical guidance significantly increased contrast. In particular, myocardium-to-blood pool contrast increased by 143% on average (p<0.0001) compared to conventional uncorrected, non-guided PET images. Furthermore, anatomical guidance significantly reduced image noise compared to non-guided image reconstruction by 16.1% (p<0.0001). Conclusion: The proposed framework for MR-derived motion compensation and anatomical guidance of cardiac PET data was shown to significantly improve image quality compared to alternative reconstruction methods. Each component of the reconstruction pipeline was shown to have a positive impact on the final image quality. These improvements have the potential to improve clinical interpretability and diagnosis based on cardiac PET-MR images.
Collapse
Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sam Ellis
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Stephan G. Nekolla
- Nuklearmedizinische Klinik und Poliklinik, Technische Technical University of Munich, Munich, Germany
| | - Karl P. Kunze
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare, Frimley, United Kingdom
| | - Teresa Vitadello
- Department of Internal Medicine I, University Hospital Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; and
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare, Frimley, United Kingdom
| | - Rene M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Julia A. Schnabel
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andrew J. Reader
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
23
|
Li H, Meng X, Guan X, Zhou N, Liu H, Zhang Y, Yu B, Zhu H, Li N, Yang Z. Clinical Evaluation of MR-Gated Respiratory Motion Correction in Simultaneous PET/MRI. Clin Nucl Med 2021; 46:297-302. [PMID: 33512946 DOI: 10.1097/rlu.0000000000003510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
PURPOSE The recently available gated T1-weighted imaging with the Dixon technique enables the synchronized gating signal for both MR acquisition and PET reconstruction. Herein, we evaluated the clinical value of this MR-gated PET reconstruction in the thoracic-abdominal PET/MRI compared with non-MR-gated method. METHODS Twenty patients (28 hypermetabolic target lesions) underwent PET/MRI. Four types of PET images were reconstructed: non-MR-gating + gated attenuation correction (AC) (group A), MR-gating + gated AC (group B), non-MR-gating + breath-hold (BH) AC (group C), and MR-gating + BH AC (group D). A 4-point objective scale (from well match to obvious mismatch was scored from 3 to 0) was proposed to evaluate the mismatch. The detection rate and quantitative metrics were also evaluated. RESULTS In the patient-based analysis, for groups A through D, the detection rates were 90%, 100%, 85%, and 90% as well as 95%, 100%, 85%, and 85%, assessed by readers 1 and 2, respectively, and significant difference of mismatch score was observed with the highest proportion of 3 points in group B (85%, 90%, 35%, and 40%, and 80%, 90%, 35%, and 20%, assessed by readers 1 and 2, respectively). The lesion-based analysis demonstrated significant differences in quantitative metrics for groups A through D (all P's < 0.05), with the highest quantitative metrics in group B (SUVmax: 7.49 ± 3.37, 8.45 ± 3.82, 6.90 ± 3.24, and 7.69 ± 3.50; SUVmean: 3.90 ± 1.60, 4.34 ± 1.84, 3.67 ± 1.61, and 4.03 ± 1.81; SUVpeak: 5.60 ± 2.50, 6.10 ± 2.80, 5.22 ± 2.40, and 5.65 ± 2.68; signal-to-noise ratio: 136.06 ± 90.58, 136.24 ± 81.63, 99.52 ± 53.16, and 107.57 ± 69.05). CONCLUSIONS The MR-gated reconstruction using gated AC reduced the mismatch between MR and PET images and improved the thoracic-abdominal PET image quality in simultaneous PET/MRI systems.
Collapse
Affiliation(s)
- Hui Li
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Xiangxi Meng
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Xiangping Guan
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Nina Zhou
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Hui Liu
- United Imaging Healthcare, Shanghai, China
| | - Yan Zhang
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Boqi Yu
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Hua Zhu
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Nan Li
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| | - Zhi Yang
- From the Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing
| |
Collapse
|
24
|
Mayer J, Wurster TH, Schaeffter T, Landmesser U, Morguet A, Bigalke B, Hamm B, Brenner W, Makowski MR, Kolbitsch C. Imaging coronary plaques using 3D motion-compensated [ 18F]NaF PET/MR. Eur J Nucl Med Mol Imaging 2021; 48:2455-2465. [PMID: 33474584 PMCID: PMC8241750 DOI: 10.1007/s00259-020-05180-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/26/2020] [Indexed: 12/19/2022]
Abstract
Background Cardiac PET has recently found novel applications in coronary atherosclerosis imaging using [18F]NaF as a radiotracer, highlighting vulnerable plaques. However, the resulting uptakes are relatively small, and cardiac motion and respiration-induced movement of the heart can impair the reconstructed images due to motion blurring and attenuation correction mismatches. This study aimed to apply an MR-based motion compensation framework to [18F]NaF data yielding high-resolution motion-compensated PET and MR images. Methods Free-breathing 3-dimensional Dixon MR data were acquired, retrospectively binned into multiple respiratory and cardiac motion states, and split into fat and water fraction using a model-based reconstruction framework. From the dynamic MR reconstructions, both a non-rigid cardiorespiratory motion model and a motion-resolved attenuation map were generated and applied to the PET data to improve image quality. The approach was tested in 10 patients and focal tracer hotspots were evaluated concerning their target-to-background ratio, contrast-to-background ratio, and their diameter. Results MR-based motion models were successfully applied to compensate for physiological motion in both PET and MR. Target-to-background ratios of identified plaques improved by 7 ± 7%, contrast-to-background ratios by 26 ± 38%, and the plaque diameter decreased by −22 ± 18%. MR-based dynamic attenuation correction strongly reduced attenuation correction artefacts and was not affected by stent-related signal voids in the underlying MR reconstructions. Conclusions The MR-based motion correction framework presented here can improve the target-to-background, contrast-to-background, and width of focal tracer hotspots in the coronary system. The dynamic attenuation correction could effectively mitigate the risk of attenuation correction artefacts in the coronaries at the lung-soft tissue boundary. In combination, this could enable a more reproducible and reliable plaque localisation. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05180-4.
Collapse
Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany.
| | - Thomas-Heinrich Wurster
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany.,School of Biomedical Imaging Sciences, King's College London, London, UK.,Department of Medical Engineering, Technische Universität Berlin, Berlin, Germany
| | - Ulf Landmesser
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Morguet
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Boris Bigalke
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Marcus R Makowski
- Department of Medical Engineering, Technische Universität Berlin, Berlin, Germany.,Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, München, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany.,School of Biomedical Imaging Sciences, King's College London, London, UK
| |
Collapse
|
25
|
Marin T, Djebra Y, Han PK, Chemli Y, Bloch I, El Fakhri G, Ouyang J, Petibon Y, Ma C. Motion correction for PET data using subspace-based real-time MR imaging in simultaneous PET/MR. Phys Med Biol 2020; 65:235022. [PMID: 33263317 DOI: 10.1088/1361-6560/abb31d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Image quality of positron emission tomography (PET) reconstructions is degraded by subject motion occurring during the acquisition. Magnetic resonance (MR)-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of the low-rank characteristics of dynamic MR images to reconstruct high-resolution MR images at high frame rates from highly undersampled k-space data. Reconstructed dynamic MR images are used to determine motion phases for PET reconstruction and estimate phase-to-phase nonrigid motion fields able to capture complex motion patterns such as irregular respiratory and bulk motion. MR-derived binning and motion fields are used for PET reconstruction to generate motion-corrected PET images. The proposed method was evaluated on in vivo data with irregular motion patterns. MR reconstructions accurately captured motion, outperforming state-of-the-art dynamic MR reconstruction techniques. Evaluation of PET reconstructions demonstrated the benefits of the proposed method in terms of motion artifacts reduction, improving the contrast-to-noise ratio by up to a factor 3 and achieveing a target-to-background ratio up to 90% superior compared to standard/uncorrected methods. The proposed method can improve the image quality of motion-corrected PET reconstructions in clinical applications.
Collapse
Affiliation(s)
- Thibault Marin
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America. Harvard Medical School, Boston MA, 02115, United States of America. Equal contribution
| | | | | | | | | | | | | | | | | |
Collapse
|
26
|
Lee BC, Moody JB, Poitrasson-Rivière A, Melvin AC, Weinberg RL, Corbett JR, Murthy VL, Ficaro EP. Automated dynamic motion correction using normalized gradient fields for 82rubidium PET myocardial blood flow quantification. J Nucl Cardiol 2020; 27:1982-1998. [PMID: 30406609 PMCID: PMC6504625 DOI: 10.1007/s12350-018-01471-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/13/2018] [Indexed: 12/01/2022]
Abstract
BACKGROUND Patient motion can lead to misalignment of left ventricular (LV) volumes-of-interest (VOIs) and subsequently inaccurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from dynamic PET myocardial perfusion images. We aimed to develop an image-based 3D-automated motion-correction algorithm that corrects the full dynamic sequence for translational motion, especially in the early blood phase frames (~ first minute) where the injected tracer activity is transitioning from the blood pool to the myocardium and where conventional image registration algorithms have had limited success. METHODS We studied 225 consecutive patients who underwent dynamic rest/stress rubidium-82 chloride (82Rb) PET imaging. Dynamic image series consisting of 30 frames were reconstructed with frame durations ranging from 5 to 80 seconds. An automated algorithm localized the RV and LV blood pools in space and time and then registered each frame to a tissue reference image volume using normalized gradient fields with a modification of a signed distance function. The computed shifts and their global and regional flow estimates were compared to those of reference shifts that were assessed by three physician readers. RESULTS The automated motion-correction shifts were within 5 mm of the manual motion-correction shifts across the entire sequence. The automated and manual motion-correction global MBF values had excellent linear agreement (R = 0.99, y = 0.97x + 0.06). Uncorrected flows outside of the limits of agreement with the manual motion-corrected flows were brought into agreement in 90% of the cases for global MBF and in 87% of the cases for global MFR. The limits of agreement for stress MBF were also reduced twofold globally and by fourfold in the RCA territory. CONCLUSIONS An image-based, automated motion-correction algorithm for dynamic PET across the entire dynamic sequence using normalized gradient fields matched the results of manual motion correction in reducing bias and variance in MBF and MFR, particularly in the RCA territory.
Collapse
Affiliation(s)
- Benjamin C Lee
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 48108, USA
| | - Jonathan B Moody
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 48108, USA
| | | | - Amanda C Melvin
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Richard L Weinberg
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - James R Corbett
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 48108, USA
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Venkatesh L Murthy
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Edward P Ficaro
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 48108, USA.
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
27
|
Perez-Liva M, Yoganathan T, Herraiz JL, Porée J, Tanter M, Balvay D, Viel T, Garofalakis A, Provost J, Tavitian B. Ultrafast Ultrasound Imaging for Super-Resolution Preclinical Cardiac PET. Mol Imaging Biol 2020; 22:1342-1352. [PMID: 32602084 PMCID: PMC7497458 DOI: 10.1007/s11307-020-01512-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/13/2020] [Accepted: 05/27/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE Physiological motion and partial volume effect (PVE) significantly degrade the quality of cardiac positron emission tomography (PET) images in the fast-beating hearts of rodents. Several Super-resolution (SR) techniques using a priori anatomical information have been proposed to correct motion and PVE in PET images. Ultrasound is ideally suited to capture real-time high-resolution cine images of rodent hearts. Here, we evaluated an ultrasound-based SR method using simultaneously acquired and co-registered PET-CT-Ultrafast Ultrasound Imaging (UUI) of the beating heart in closed-chest rodents. PROCEDURES The method was tested with numerical and animal data (n = 2) acquired with the non-invasive hybrid imaging system PETRUS that acquires simultaneously PET, CT, and UUI. RESULTS We showed that ultrasound-based SR drastically enhances the quality of PET images of the beating rodent heart. For the simulations, the deviations between expected and mean reconstructed values were 2 % after applying SR. For the experimental data, when using Ultrasound-based SR correction, contrast was improved by a factor of two, signal-to-noise ratio by 11 %, and spatial resolution by 56 % (~ 0.88 mm) with respect to static PET. As a consequence, the metabolic defect following an acute cardiac ischemia was delineated with much higher anatomical precision. CONCLUSIONS Our results provided a proof-of-concept that image quality of cardiac PET in fast-beating rodent hearts can be significantly improved by ultrasound-based SR, a portable low-cost technique. Improved PET imaging of the rodent heart may allow new explorations of physiological and pathological situations related with cardiac metabolism.
Collapse
Affiliation(s)
- Mailyn Perez-Liva
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France.
| | | | - Joaquin L Herraiz
- Nuclear Physics Group and IPARCOS, Complutense University of Madrid, Plaza de las Ciencias, 1, 28020, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Jonathan Porée
- Physics for Medicine Paris, Inserm/ESPCI Paris-PSL/PSL-University/CNRS, 17 rue Moreau, 75012, Paris, France
- Engineering physics department, Polytechnique Montréal, Montréal, Canada
| | - Mickael Tanter
- Physics for Medicine Paris, Inserm/ESPCI Paris-PSL/PSL-University/CNRS, 17 rue Moreau, 75012, Paris, France
| | - Daniel Balvay
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France
| | - Thomas Viel
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France
| | | | - Jean Provost
- Engineering physics department, Polytechnique Montréal, Montréal, Canada
- Montreal Heart Institute, Montréal, Canada
| | - Bertrand Tavitian
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France
- Service de Radiologie, APHP Centre, Hôpital Européen Georges Pompidou, Paris, France
| |
Collapse
|
28
|
Manabe O, Oyama-Manabe N, Tamaki N. Positron emission tomography/MRI for cardiac diseases assessment. Br J Radiol 2020; 93:20190836. [PMID: 32023123 DOI: 10.1259/bjr.20190836] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Functional imaging tools have emerged in the last few decades and are increasingly used to assess the function of the human heart in vivo. Positron emission tomography (PET) is used to evaluate myocardial metabolism and blood flow. Magnetic resonance imaging (MRI) is an essential tool for morphological and functional evaluation of the heart. In cardiology, PET is successfully combined with CT for hybrid cardiac imaging. The effective integration of two imaging modalities allows simultaneous data acquisition combining functional, structural and molecular imaging. After PET/CT has been successfully accepted for clinical practices, hybrid PET/MRI is launched. This review elaborates the current evidence of PET/MRI in cardiovascular imaging and its expected clinical applications for a comprehensive assessment of cardiovascular diseases while highlighting the advantages and limitations of this hybrid imaging approach.
Collapse
Affiliation(s)
- Osamu Manabe
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Noriko Oyama-Manabe
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Nagara Tamaki
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| |
Collapse
|
29
|
Aizaz M, Moonen RPM, van der Pol JAJ, Prieto C, Botnar RM, Kooi ME. PET/MRI of atherosclerosis. Cardiovasc Diagn Ther 2020; 10:1120-1139. [PMID: 32968664 DOI: 10.21037/cdt.2020.02.09] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Myocardial infarction and stroke are the most prevalent global causes of death. Each year 15 million people worldwide die due to myocardial infarction or stroke. Rupture of a vulnerable atherosclerotic plaque is the main underlying cause of stroke and myocardial infarction. Key features of a vulnerable plaque are inflammation, a large lipid-rich necrotic core (LRNC) with a thin or ruptured overlying fibrous cap, and intraplaque hemorrhage (IPH). Noninvasive imaging of these features could have a role in risk stratification of myocardial infarction and stroke and can potentially be utilized for treatment guidance and monitoring. The recent development of hybrid PET/MRI combining the superior soft tissue contrast of MRI with the opportunity to visualize specific plaque features using various radioactive tracers, paves the way for comprehensive plaque imaging. In this review, the use of hybrid PET/MRI for atherosclerotic plaque imaging in carotid and coronary arteries is discussed. The pros and cons of different hybrid PET/MRI systems are reviewed. The challenges in the development of PET/MRI and potential solutions are described. An overview of PET and MRI acquisition techniques for imaging of atherosclerosis including motion correction is provided, followed by a summary of vessel wall imaging PET/MRI studies in patients with carotid and coronary artery disease. Finally, the future of imaging of atherosclerosis with PET/MRI is discussed.
Collapse
Affiliation(s)
- Mueez Aizaz
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Rik P M Moonen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Jochem A J van der Pol
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - M Eline Kooi
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
30
|
Mayer J, Brown R, Thielemans K, Ovtchinnikov E, Pasca E, Atkinson D, Gillman A, Marsden P, Ippoliti M, Makowski M, Schaeffter T, Kolbitsch C. Flexible numerical simulation framework for dynamic PET-MR data. Phys Med Biol 2020; 65:145003. [PMID: 32692725 DOI: 10.1088/1361-6560/ab7eee] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms. Contrast and tracer kinetics can also be simulated and are available as ground truth information. To closely emulate medical examination, input and output of the simulation are files in standardised open-source raw data formats. This enables the use of existing raw data as a template input and ensures seamless integration of the output into existing reconstruction pipelines. The proposed framework was validated in PET-MR and image registration applications. It was used to simulate a FDG-PET-MR scan with cardiac and respiratory motion. Ground truth motion information could be utilised to optimise parameters for PET and synergistic PET-MR image registration. In addition, a free-breathing dynamic contrast enhancement (DCE) abdominal scan of a patient with hepatic lesions was simulated. In order to correct for breathing motion, a motion-corrected image reconstruction scheme was used and a Toft's model was fit to the DCE data to obtain quantitative DCE-MRI parameters. Utilising the ground truth motion information, the dependency of quantitative DCE-MR images on the accuracy of the motion estimation was evaluated. We demonstrated that respiratory motion had to be available with an average accuracy of at least the spatial resolution of the DCE-MR images in order to ensure an improvement in lesions visualisation and quantification compared to no motion correction. The proposed framework provides a valuable tool with a wide range of scientific PET and MR applications and will be available as part of the open-source project Synergistic Image Reconstruction Framework (SIRF).
Collapse
Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany. Author to whom any correspondence should be addressed
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Tran EH, Eiben B, Wetscherek A, Oelfke U, Meedt G, Hawkes DJ, McClelland JR. Evaluation of MRI-derived surrogate signals to model respiratory motion. Biomed Phys Eng Express 2020; 6:045015. [PMID: 33194224 PMCID: PMC7655234 DOI: 10.1088/2057-1976/ab944c] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 05/07/2020] [Accepted: 05/19/2020] [Indexed: 12/25/2022]
Abstract
An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.
Collapse
Affiliation(s)
- Elena H Tran
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Björn Eiben
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Gustav Meedt
- Elekta, Medical Intelligence Medizintechnik GmbH, Schwabmünchen, Germany
| | - David J Hawkes
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Jamie R McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| |
Collapse
|
32
|
Perez-Liva M, Yoganathan T, Herraiz JL, Porée J, Tanter M, Balvay D, Viel T, Garofalakis A, Provost J, Tavitian B. Ultrafast Ultrasound Imaging for Super-Resolution Preclinical Cardiac PET. Mol Imaging Biol 2020. [DOI: https://doi.org/10.1007/s11307-020-01512-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract
Purpose
Physiological motion and partial volume effect (PVE) significantly degrade the quality of cardiac positron emission tomography (PET) images in the fast-beating hearts of rodents. Several Super-resolution (SR) techniques using a priori anatomical information have been proposed to correct motion and PVE in PET images. Ultrasound is ideally suited to capture real-time high-resolution cine images of rodent hearts. Here, we evaluated an ultrasound-based SR method using simultaneously acquired and co-registered PET-CT-Ultrafast Ultrasound Imaging (UUI) of the beating heart in closed-chest rodents.
Procedures
The method was tested with numerical and animal data (n = 2) acquired with the non-invasive hybrid imaging system PETRUS that acquires simultaneously PET, CT, and UUI.
Results
We showed that ultrasound-based SR drastically enhances the quality of PET images of the beating rodent heart. For the simulations, the deviations between expected and mean reconstructed values were 2 % after applying SR. For the experimental data, when using Ultrasound-based SR correction, contrast was improved by a factor of two, signal-to-noise ratio by 11 %, and spatial resolution by 56 % (~ 0.88 mm) with respect to static PET. As a consequence, the metabolic defect following an acute cardiac ischemia was delineated with much higher anatomical precision.
Conclusions
Our results provided a proof-of-concept that image quality of cardiac PET in fast-beating rodent hearts can be significantly improved by ultrasound-based SR, a portable low-cost technique. Improved PET imaging of the rodent heart may allow new explorations of physiological and pathological situations related with cardiac metabolism.
Collapse
|
33
|
Gratz M, Ruhlmann V, Umutlu L, Fenchel M, Hong I, Quick HH. Impact of respiratory motion correction on lesion visibility and quantification in thoracic PET/MR imaging. PLoS One 2020; 15:e0233209. [PMID: 32497135 PMCID: PMC7272064 DOI: 10.1371/journal.pone.0233209] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 04/30/2020] [Indexed: 01/04/2023] Open
Abstract
The impact of a method for MR-based respiratory motion correction of PET data on lesion visibility and quantification in patients with oncologic findings in the lung was evaluated. Twenty patients with one or more lesions in the lung were included. Hybrid imaging was performed on an integrated PET/MR system using 18F-FDG as radiotracer. The standard thoracic imaging protocol was extended by a free-breathing self-gated acquisition of MR data for motion modelling. PET data was acquired simultaneously in list-mode for 5-10 mins. One experienced radiologist and one experienced nuclear medicine specialist evaluated and compared the post-processed data in consensus regarding lesion visibility (scores 1-4, 4 being best), image noise levels (scores 1-3, 3 being lowest noise), SUVmean and SUVmax. Motion-corrected (MoCo) images were additionally compared with gated images. Non-motion-corrected free-breathing data served as standard of reference in this study. Motion correction generally improved lesion visibility (3.19 ± 0.63) and noise ratings (2.95 ± 0.22) compared to uncorrected (2.81 ± 0.66 and 2.95 ± 0.22, respectively) or gated PET data (2.47 ± 0.93 and 1.30 ± 0.47, respectively). Furthermore, SUVs (mean and max) were compared for all methods to estimate their respective impact on the quantification. Deviations of SUVmax were smallest between the uncorrected and the MoCo lesion data (average increase of 9.1% of MoCo SUVs), while SUVmean agreed best for gated and MoCo reconstructions (MoCo SUVs increased by 1.2%). The studied method for MR-based respiratory motion correction of PET data combines increased lesion sharpness and improved lesion activity quantification with high signal-to-noise ratio in a clinical setting. In particular, the detection of small lesions in moving organs such as the lung and liver may thus be facilitated. These advantages justify the extension of the PET/MR imaging protocol by 5-10 minutes for motion correction.
Collapse
Affiliation(s)
- Marcel Gratz
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg Essen, Essen, Germany
- High Field and Hybrid MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Verena Ruhlmann
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | | | - Inki Hong
- Siemens Medical Solutions Inc, Knoxville, Tennessee, United States of America
| | - Harald H. Quick
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg Essen, Essen, Germany
- High Field and Hybrid MR Imaging, University of Duisburg-Essen, Essen, Germany
| |
Collapse
|
34
|
|
35
|
Lee BC, Moody JB, Poitrasson-Rivière A, Melvin AC, Weinberg RL, Corbett JR, Ficaro EP, Murthy VL. Blood pool and tissue phase patient motion effects on 82rubidium PET myocardial blood flow quantification. J Nucl Cardiol 2019; 26:1918-1929. [PMID: 29572594 PMCID: PMC6151305 DOI: 10.1007/s12350-018-1256-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 03/05/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND Patient motion can lead to misalignment of left ventricular volumes of interest and subsequently inaccurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from dynamic PET myocardial perfusion images. We aimed to identify the prevalence of patient motion in both blood and tissue phases and analyze the effects of this motion on MBF and MFR estimates. METHODS We selected 225 consecutive patients that underwent dynamic stress/rest rubidium-82 chloride (82Rb) PET imaging. Dynamic image series were iteratively reconstructed with 5- to 10-second frame durations over the first 2 minutes for the blood phase and 10 to 80 seconds for the tissue phase. Motion shifts were assessed by 3 physician readers from the dynamic series and analyzed for frequency, magnitude, time, and direction of motion. The effects of this motion isolated in time, direction, and magnitude on global and regional MBF and MFR estimates were evaluated. Flow estimates derived from the motion corrected images were used as the error references. RESULTS Mild to moderate motion (5-15 mm) was most prominent in the blood phase in 63% and 44% of the stress and rest studies, respectively. This motion was observed with frequencies of 75% in the septal and inferior directions for stress and 44% in the septal direction for rest. Images with blood phase isolated motion had mean global MBF and MFR errors of 2%-5%. Isolating blood phase motion in the inferior direction resulted in mean MBF and MFR errors of 29%-44% in the RCA territory. Flow errors due to tissue phase isolated motion were within 1%. CONCLUSIONS Patient motion was most prevalent in the blood phase and MBF and MFR errors increased most substantially with motion in the inferior direction. Motion correction focused on these motions is needed to reduce MBF and MFR errors.
Collapse
Affiliation(s)
- Benjamin C Lee
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 8108, USA.
| | - Jonathan B Moody
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 8108, USA
| | | | - Amanda C Melvin
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Richard L Weinberg
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - James R Corbett
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 8108, USA
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Edward P Ficaro
- INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 8108, USA
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Venkatesh L Murthy
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
36
|
Turco A, Gheysens O, Duchenne J, Nuyts J, Rega F, Voigt JU, Vunckx K, Claus P. Partial volume and motion correction in cardiac PET: First results from an in vs ex vivo comparison using animal datasets. J Nucl Cardiol 2019; 26:2034-2044. [PMID: 30644052 DOI: 10.1007/s12350-018-01581-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 11/07/2018] [Indexed: 11/26/2022]
Abstract
BACKGROUND In a previous study on ex vivo, static cardiac datasets, we investigated the benefits of performing partial volume correction (PVC) in cardiac 18F-Fluorodeoxyglucose(FDG) PET datasets. In the present study, we extend the analysis to in vivo cardiac datasets, with the aim of defining which reconstruction technique maximizes quantitative accuracy and, ultimately, makes PET a better diagnostic tool for cardiac pathologies. METHODS In vivo sheep datasets were acquired and reconstructed with/without motion correction and using several reconstruction algorithms (with/without resolution modeling, with/without non-anatomical priors). Corresponding ex vivo scans of the excised sheep hearts were performed on a small-animal PET scanner (Siemens Focus 220, microPET) to provide high-resolution reference data unaffected by respiratory and cardiac motion. A comparison between the in vivo cardiac reconstructions and the corresponding ex vivo ground truth was performed. RESULTS The use of an edge-preserving prior (Total Variation (TV) prior in this work) in combination with motion correction reduces the bias in absolute quantification when compared to the standard clinical reconstructions (- 0.83 vs - 3.74 SUV units), when the end-systolic gate is considered. At end-diastole, motion correction improves absolute quantification but the PVC with priors does not improve the similarity to the ground truth more than a regular iterative reconstruction with motion correction and without priors. Relative quantification was not influenced much by the chosen reconstruction algorithm. CONCLUSIONS The relative ranking of the algorithms suggests superiority of the PVC reconstructions with dual gating in terms of overall absolute quantification and noise properties. A well-tuned edge-preserving prior, such as TV, enhances the noise properties of the resulting images of the heart. The end-systolic gate yields the most accurate quantification of cardiac datasets.
Collapse
Affiliation(s)
- A Turco
- Department of Imaging and Pathology, Nuclear Medicine and Molecular imaging, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
- Department of Cardiovascular Sciences, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
| | - O Gheysens
- Department of Imaging and Pathology, Nuclear Medicine and Molecular imaging, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
- Department of Nuclear Medicine, University Hospitals Leuven, 3000, Leuven, Belgium
| | - J Duchenne
- Department of Cardiovascular Sciences, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
| | - J Nuyts
- Department of Imaging and Pathology, Nuclear Medicine and Molecular imaging, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
| | - F Rega
- Department of Cardiovascular Sciences, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
- Department of Cardiac Surgery, University Hospitals Leuven, 3000, Leuven, Belgium
| | - J U Voigt
- Department of Cardiovascular Sciences, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
- Department of Cardiovascular Diseases, University Hospitals Leuven, 3000, Leuven, Belgium
| | - K Vunckx
- Department of Imaging and Pathology, Nuclear Medicine and Molecular imaging, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium
| | - P Claus
- Department of Cardiovascular Sciences, Medical Imaging Research Center (MIRC), KU Leuven - University of Leuven, 3000, Leuven, Belgium.
| |
Collapse
|
37
|
Petibon Y, Sun T, Han PK, Ma C, Fakhri GE, Ouyang J. MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging. Phys Med Biol 2019; 64:195009. [PMID: 31394518 PMCID: PMC7007962 DOI: 10.1088/1361-6560/ab39c2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Motion of the myocardium deteriorates the quality and quantitative accuracy of cardiac PET images. We present a method for MR-based cardiac and respiratory motion correction of cardiac PET data and evaluate its impact on estimation of activity and kinetic parameters in human subjects. Three healthy subjects underwent simultaneous dynamic 18F-FDG PET and MRI on a hybrid PET/MR scanner. A cardiorespiratory motion field was determined for each subject using navigator, tagging and golden-angle radial MR acquisitions. Acquired coincidence events were binned into cardiac and respiratory phases using electrocardiogram and list mode-driven signals, respectively. Dynamic PET images were reconstructed with MR-based motion correction (MC) and without motion correction (NMC). Parametric images of 18F-FDG consumption rates (Ki) were estimated using Patlak's method for both MC and NMC images. MC alleviated motion artifacts in PET images, resulting in improved spatial resolution, improved recovery of activity in the myocardium wall and reduced spillover from the myocardium to the left ventricle cavity. Significantly higher myocardium contrast-to-noise ratio and lower apparent wall thickness were obtained in MC versus NMC images. Likewise, parametric images of Ki calculated with MC data had improved spatial resolution as compared to those obtained with NMC. Consistent with an increase in reconstructed activity concentration in the frames used during kinetic analyses, MC led to the estimation of higher Ki values almost everywhere in the myocardium, with up to 18% increase (mean across subjects) in the septum as compared to NMC. This study shows that MR-based motion correction of cardiac PET results in improved image quality that can benefit both static and dynamic studies.
Collapse
Affiliation(s)
| | | | - P K Han
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA
| | - C Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA
| | - G El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA
| | - J Ouyang
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA
| |
Collapse
|
38
|
Hope TA, Fayad ZA, Fowler KJ, Holley D, Iagaru A, McMillan AB, Veit-Haiback P, Witte RJ, Zaharchuk G, Catana C. Summary of the First ISMRM-SNMMI Workshop on PET/MRI: Applications and Limitations. J Nucl Med 2019; 60:1340-1346. [PMID: 31123099 PMCID: PMC6785790 DOI: 10.2967/jnumed.119.227231] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/21/2019] [Indexed: 12/12/2022] Open
Abstract
Since the introduction of simultaneous PET/MRI in 2011, there have been significant advancements. In this review, we highlight several technical advancements that have been made primarily in attenuation and motion correction and discuss the status of multiple clinical applications using PET/MRI. This review is based on the experience at the first PET/MRI conference cosponsored by the International Society for Magnetic Resonance in Medicine and the Society of Nuclear Medicine and Molecular Imaging.
Collapse
Affiliation(s)
- Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
- Department of Radiology, San Francisco VA Medical Center, San Francisco, California
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | - Zahi A Fayad
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kathryn J Fowler
- Department of Radiology, University of California San Diego, San Diego, California
| | - Dawn Holley
- Department of Radiology, Stanford University Medical Center, Stanford, California
| | - Andrei Iagaru
- Department of Radiology, Stanford University Medical Center, Stanford, California
| | - Alan B McMillan
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Patrick Veit-Haiback
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Robert J Witte
- Department of Radiology, Mayo Clinic, Rochester, Minnesota; and
| | - Greg Zaharchuk
- Department of Radiology, Stanford University Medical Center, Stanford, California
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| |
Collapse
|
39
|
Zaidi H, Nkoulou R. Artifact-free quantitative cardiovascular PET/MR imaging: An impossible dream? J Nucl Cardiol 2019; 26:1119-1121. [PMID: 29344918 DOI: 10.1007/s12350-017-1163-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 11/30/2017] [Indexed: 10/18/2022]
Affiliation(s)
- Habib Zaidi
- Division of Nuclear Médicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, Netherlands
| | - Rene Nkoulou
- Division of Nuclear Médicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
- Division of Cardiology, Geneva University Hospital, Geneva, Switzerland.
| |
Collapse
|
40
|
Zhu Y, Zhu X. MRI-Driven PET Image Optimization for Neurological Applications. Front Neurosci 2019; 13:782. [PMID: 31417346 PMCID: PMC6684790 DOI: 10.3389/fnins.2019.00782] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 07/12/2019] [Indexed: 12/12/2022] Open
Abstract
Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.
Collapse
Affiliation(s)
- Yuankai Zhu
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohua Zhu
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
41
|
Wang L, Clarysse P, Liu Z, Gao B, Liu W, Croisille P, Delachartre P. A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images. Med Image Anal 2019; 57:136-148. [PMID: 31302510 DOI: 10.1016/j.media.2019.06.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 06/27/2019] [Accepted: 06/27/2019] [Indexed: 11/25/2022]
Abstract
A new method is proposed to quantify the myocardial motion from both 2D C(ine)-MRI and T(agged)-MRI sequences. The tag pattern offers natural landmarks within the image that makes it possible to accurately quantify the motion within the myocardial wall. Therefore, several methods have been proposed for T-MRI. However, the lack of salient features within the cardiac wall in C-MRI hampers local motion estimation. Our method aims to ensure the local intensity and shape features invariance during motion through the iterative minimization of a cost function via a random walk scheme. The proposed approach is evaluated on realistic simulated C-MRI and T-MRI sequences. The results show more than 53% improvements on displacement estimation, and more than 24% on strain estimation for both C-MRI and T-MRI sequences, as compared to state-of-the-art cardiac motion estimators. Preliminary experiments on clinical data have shown a good ability of the proposed method to detect abnormal motion patterns related to pathology. If those results are confirmed on large databases, this would open up the possibility for more accurate diagnosis of cardiac function from standard C-MRI examinations and also the retrospective study of prior studies.
Collapse
Affiliation(s)
- Liang Wang
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France.
| | - Patrick Clarysse
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France
| | - Zhengjun Liu
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin 150001, People's Republic of China
| | - Bin Gao
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin 150001, People's Republic of China; College of data science and technology, Heilongjiang University, Harbin 150080, People's Republic of China
| | - Wanyu Liu
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin 150001, People's Republic of China
| | - Pierre Croisille
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France; Department of Radiology, University Hospital of Saint-Etienne, Université Jean-Monnet, Saint-Etienne, France
| | - Philippe Delachartre
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France
| |
Collapse
|
42
|
Murphy DJ, Mak SM, Mallia A, Jeljeli S, Stirling JJ, Goh V, Bille A, Cook GJR. Loco-regional staging of malignant pleural mesothelioma by integrated 18F-FDG PET/MRI. Eur J Radiol 2019; 115:46-52. [PMID: 31084758 DOI: 10.1016/j.ejrad.2019.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 03/04/2019] [Accepted: 04/04/2019] [Indexed: 01/21/2023]
Abstract
AIM To examine the performance of 18F-FDG PET/MRI in the loco-regional staging of malignant pleural mesothelioma (MPM). METHODS Consecutive subjects with MPM undergoing pre-operative staging with 18F-FDG PET/CT who underwent a same day integrated 18F-FDG PET/MRI were prospectively studied. Clinical TNM staging (AJCC 7th edition) was performed separately and in consensus by two readers on the 18F-FDG PET/MRI studies, and compared with staging by 18F-FDG PET/CT, and with final pathological stage, determined by a combination of intra-operative and histological findings. RESULTS 10 subjects (9 male, mean age 68 years) with biopsy-proven MPM (9 epithelioid tumours, 1 biphasic) were included. One subject underwent neo-adjuvant chemotherapy between imaging and surgery and was excluded from the clinical versus pathological stage analysis. Pathological staging was concordant with staging by 18F-FDG PET/MRI in 67% (n = 6) of subjects, and with 18F-FDG PET/CT staging in 33% (n = 3). Pathological T stage was concordant with 18F-FDG PET/MRI in 78% (n = 7), and with 18F-FDG PET/CT in 33% (n = 3) of subjects. Pathological N stage was concordant with both 18F-FDG PET/MRI and 18F-FDG PET/CT in 78% (n = 7) of cases. No subject had metastatic disease. There was good inter-observer agreement for overall PET/MRI staging (weighted kappa 0.63) with moderate inter-reader agreement for T staging (weighted kappa 0.59). All 6 subjects with prior talc pleurodesis demonstrated mismatch between elevated FDG uptake and restricted diffusion in areas of visible talc deposition. CONCLUSION Clinical MPM staging by 18F-FDG PET/MRI is feasible, and potentially provides more accurate loco-regional staging than PET/CT, particularly in T staging.
Collapse
Affiliation(s)
- D J Murphy
- King's College London & Guy's and St Thomas' PET Centre, London UK; Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London UK.
| | - S M Mak
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London UK
| | - A Mallia
- King's College London & Guy's and St Thomas' PET Centre, London UK; Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London UK
| | - S Jeljeli
- King's College London & Guy's and St Thomas' PET Centre, London UK
| | - J J Stirling
- King's College London & Guy's and St Thomas' PET Centre, London UK
| | - V Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London UK; Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London UK
| | - A Bille
- Department of Cardiothoracic Surgery, Guy's and St Thomas' NHS Foundation Trust, London UK
| | - G J R Cook
- King's College London & Guy's and St Thomas' PET Centre, London UK; Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London UK
| |
Collapse
|
43
|
Munoz C, Neji R, Kunze KP, Nekolla SG, Botnar RM, Prieto C. Respiratory- and cardiac motion-corrected simultaneous whole-heart PET and dual phase coronary MR angiography. Magn Reson Med 2019; 81:1671-1684. [PMID: 30320931 PMCID: PMC6492195 DOI: 10.1002/mrm.27517] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/25/2018] [Accepted: 08/13/2018] [Indexed: 01/07/2023]
Abstract
PURPOSE To develop a framework for efficient and simultaneous acquisition of motion-compensated whole-heart coronary MR angiography (CMRA) and left ventricular function by MR and myocardial integrity by PET on a 3T PET-MR system. METHODS An acquisition scheme based on a dual-phase CMRA sequence acquired simultaneously with cardiac PET data has been developed. The framework is integrated with a motion-corrected image reconstruction approach, so that non-rigid respiratory and cardiac deformation fields estimated from MR images are used to correct both the CMRA (respiratory motion correction for each cardiac phase) and the PET data (respiratory and cardiac motion correction). The proposed approach was tested in a cohort of 8 healthy subjects and 6 patients with coronary artery disease. Left ventricular (LV) function estimated from motion-corrected dual-phase CMRA was compared to the gold standard estimated from a stack of 2D CINE images for the healthy subjects. Relative increase of signal in motion-corrected PET images compared to uncorrected images was computed for standard 17-segment polar maps for each patient. RESULTS Motion-corrected dual-phase CMRA images allow for visualization of the coronary arteries in both systole and diastole for all healthy subjects and cardiac patients. LV functional indices from healthy subjects result in good agreement with the reference method, underestimating stroke volume by 3.07 ± 3.26 mL and ejection fraction by 0.30 ± 1.01%. Motion correction improved delineation of the myocardium in PET images, resulting in an increased 18 F-FDG signal of up to 28% in basal segments of the myocardial wall compared to uncorrected images. CONCLUSION The proposed motion-corrected dual-phase CMRA and cardiac PET produces co-registered good quality images in both modalities in a single efficient examination of ~13 min.
Collapse
Affiliation(s)
- Camila Munoz
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | - Radhouene Neji
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Siemens Healthcare, MR Research CollaborationsFrimleyUnited Kingdom
| | - Karl P. Kunze
- Technische Universität München, Nuklearmedizinische Klinik und PoliklinikMunichGermany
| | - Stephan G. Nekolla
- Technische Universität München, Nuklearmedizinische Klinik und PoliklinikMunichGermany
- DZHK (Deutsches Zentrum für Herz‐Kreislauf‐Forschung e.V.), partner site Munich Heart AllianceMunichGermany
| | - Rene M. Botnar
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Catolica de Chile, Escuela de IngenieriaSantiagoChile
| | - Claudia Prieto
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Catolica de Chile, Escuela de IngenieriaSantiagoChile
| |
Collapse
|
44
|
Marchesseau S, Totman JJ, Fadil H, Leek FAA, Chaal J, Richards M, Chan M, Reilhac A. Cardiac motion and spillover correction for quantitative PET imaging using dynamic MRI. Med Phys 2019; 46:726-737. [PMID: 30575047 DOI: 10.1002/mp.13345] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 12/07/2018] [Accepted: 12/07/2018] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Cardiac positron emission tomography/magnetic resonance imaging (PET/MRI) acquisition presents novel clinical applications thanks to the combination of viability and metabolic imaging (PET) and functional and structural imaging (MRI). However, the resolution of PET, as well as cardiac and respiratory motion in nongated cardiac imaging acquisition protocols, leads to a reduction in image quality and severe quantitative bias. Respiratory or cardiac motion is customarily addressed with gated reconstruction which results in higher noise. METHODS Inspired by a method that has been used in brain PET, a practical correction approach, designed to overcome these existing limitations for quantitative PET imaging, was developed and applied in the context of cardiac PET/MRI. The correction approach for PET data consists of computing the mean density map of each underlying moving region, as obtained with MRI, and translating them to the PET space taking into account the PET spatial and temporal resolution. Using these tissue density maps, the method then constructs a system of linear equations that models the activity recovery and cross-contamination coefficients, which can be solved for the true activity values. Physical and numerical cardiac phantoms were employed in order to quantify the proposed correction. The full correction pipeline was then used to assess differences in metabolic function between scar and healthy myocardium in eight patients with recent acute myocardial infarction using [11 C]-acetate. Data from ten additional patients, injected with [18 F]-FDG, were used to compare the method to the standard electrocardiography (ECG)-gated approach. RESULTS The proposed method resulted in better recovery (from 32% to 95% on the simulated phantom model) and less residual activity than the standard approach. Higher signal-to-noise and contrast-to-noise ratios than ECG-gating were also witnessed (Signal-to-noise ratio (SNR) increased from 2.92 to 5.24, contrast-to-noise ratio (CNR) increased from 62.9 to 145.9 when compared to a four-gate reconstruction). Finally, the relevance of this correction using [11 C]-acetate PET patient data, for which erroneous physiological conclusions could have been made based on the uncorrected data, was established as the correction led to the expected clinical results. CONCLUSIONS An efficient and simple method to correct for the quantitative biases in PET measurements caused by cardiac motion has been developed. Validation experiments using phantom and patient data showed improved accuracy and reliability with this approach when compared to simpler strategies such as gated acquisition or optimal regions of interest (ROI).
Collapse
Affiliation(s)
| | - John J Totman
- Clinical Imaging Research Centre, A*STAR-NUS, 117599, Singapore
| | - Hakim Fadil
- Clinical Imaging Research Centre, A*STAR-NUS, 117599, Singapore
| | | | - Jasper Chaal
- Clinical Imaging Research Centre, A*STAR-NUS, 117599, Singapore
| | - Mark Richards
- Cardiovascular Research Institute, National University of Singapore, 119228, Singapore.,Christchurch Heart Institute, University of Otago, Christchurch, 8140, New Zealand
| | - Mark Chan
- Department of Medicine, Yong Loo Lin SoM, National University of Singapore, 117597, Singapore
| | | |
Collapse
|
45
|
Rigie D, Vahle T, Zhao T, Czekella B, Frohwein LJ, Schäfers K, Boada FE. Cardiorespiratory motion-tracking via self-refocused rosette navigators. Magn Reson Med 2019; 81:2947-2958. [PMID: 30615208 DOI: 10.1002/mrm.27609] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 09/27/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a flexible method for tracking respiratory and cardiac motions throughout MR and PET-MR body examinations that requires no additional hardware and minimal sequence modification. METHODS The incorporation of a contrast-neutral rosette navigator module following the RF excitation allows for robust cardiorespiratory motion tracking with minimal impact on the host sequence. Spatial encoding gradients are applied to the FID signal and the desired motion signals are extracted with a blind source separation technique. This approach is validated with an anthropomorphic, PET-MR-compatible motion phantom as well as in 13 human subjects. RESULTS Both respiratory and cardiac motions were reliably extracted from the proposed rosette navigator in phantom and patient studies. In the phantom study, the MR-derived motion signals were additionally validated against the ground truth measurement of diaphragm displacement and left ventricle model triggering pulse. CONCLUSION The proposed method yields accurate respiratory and cardiac motion-state tracking, requiring only a short (1.76 ms) additional navigator module, which is self-refocusing and imposes minimal constraints on sequence design.
Collapse
Affiliation(s)
- David Rigie
- Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York, New York
| | | | - Tiejun Zhao
- Siemens Medical Solutions, New York, New York
| | - Björn Czekella
- European Institute for Molecular Imaging, Münster, Germany
| | | | - Klaus Schäfers
- European Institute for Molecular Imaging, Münster, Germany
| | - Fernando E Boada
- Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York, New York
| |
Collapse
|
46
|
Robson PM, Trivieri M, Karakatsanis NA, Padilla M, Abgral R, Dweck MR, Kovacic JC, Fayad ZA. Correction of respiratory and cardiac motion in cardiac PET/MR using MR-based motion modeling. Phys Med Biol 2018; 63:225011. [PMID: 30426968 DOI: 10.1088/1361-6560/aaea97] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Cardiac positron emission tomography (PET) imaging suffers from image blurring due to the constant motion of the heart that can impact interpretation. Hybrid PET/magnetic resonance (MR) has the potential to use radiation-free MR imaging to correct for the effects of cardio-respiratory motion in the PET data, improving qualitative and quantitative PET imaging in the heart. The purpose of this study was (i) to implement a MR image-based motion-corrected PET/MR method and (ii) to perform a proof-of-concept study of quantitative myocardial PET data in patients. The proposed method takes reconstructions of respiratory and cardiac gated PET data and applies spatial transformations to a single reference frame before averaging to form a single motion-corrected PET (MC-PET) image. Motion vector fields (MVFs) describing the transformations were derived from affine or non-rigid registration of respiratory and cardiac gated MR data. Eight patients with suspected cardiac sarcoidosis underwent cardiac PET/MR imaging after injection of 5 MBq kg-1 of 18F-fluorodeoxyglucose (18F-FDG). Myocardial regions affected by motion were identified by expert readers within which target-to-background ratios (TBR) and contrast-to-noise ratios (CNR) were measured on non-MC-non-gated, MC-PET, and double respiratory and cardiac gated PET images. Paired t-tests were used to determine statistical differences in quantitative uptake-measures between the different types of PET images. MC-PET images showed less blurring compared to non-MC-non-gated PET and tracer activity qualitatively aligned better with the underlying myocardial anatomy when fused with MR. TBR and CNR were significantly greater for MC-PET (2.8 ± 0.9; 21 ± 22) compared to non-MC-non-gated PET (2.4 ± 0.9, p = 0.0001; 15 ± 13, p = 0.02), while TBR was lower and CNR greater compared to double-gated PET (3.2 ± 0.9, p = 0.04; 6 ± 3, p = 0.004). This study demonstrated in a patient cohort that motion-corrected (MC) cardiac PET/MR is feasible using a retrospective MR image-based method and that improvement in TBR and CNR are achievable. MC PET/MR holds promise for improving interpretation and quantification in cardiac PET imaging.
Collapse
Affiliation(s)
- Philip M Robson
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Pl, New York, NY 10029, United States of America. Author to whom any correspondence should be addressed
| | | | | | | | | | | | | | | |
Collapse
|
47
|
Munoz C, Kunze KP, Neji R, Vitadello T, Rischpler C, Botnar RM, Nekolla SG, Prieto C. Motion-corrected whole-heart PET-MR for the simultaneous visualisation of coronary artery integrity and myocardial viability: an initial clinical validation. Eur J Nucl Med Mol Imaging 2018; 45:1975-1986. [PMID: 29754161 PMCID: PMC6132558 DOI: 10.1007/s00259-018-4047-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 05/02/2018] [Indexed: 01/08/2023]
Abstract
PURPOSE Cardiac PET-MR has shown potential for the comprehensive assessment of coronary heart disease. However, image degradation due to physiological motion remains a challenge that could hinder the adoption of this technology in clinical practice. The purpose of this study was to validate a recently proposed respiratory motion-corrected PET-MR framework for the simultaneous visualisation of myocardial viability (18F-FDG PET) and coronary artery anatomy (coronary MR angiography, CMRA) in patients with chronic total occlusion (CTO). METHODS A cohort of 14 patients was scanned with the proposed PET-CMRA framework. PET and CMRA images were reconstructed with and without the proposed motion correction approach for comparison purposes. Metrics of image quality including visible vessel length and sharpness were obtained for CMRA for both the right and left anterior descending coronary arteries (RCA, LAD), and relative increase in 18F-FDG PET signal after motion correction for standard 17-segment polar maps was computed. Resulting coronary anatomy by CMRA and myocardial integrity by PET were visually compared against X-ray angiography and conventional Late Gadolinium Enhancement (LGE) MRI, respectively. RESULTS Motion correction increased CMRA visible vessel length by 49.9% and 32.6% (RCA, LAD) and vessel sharpness by 12.3% and 18.9% (RCA, LAD) on average compared to uncorrected images. Coronary lumen delineation on motion-corrected CMRA images was in good agreement with X-ray angiography findings. For PET, motion correction resulted in an average 8% increase in 18F-FDG signal in the inferior and inferolateral segments of the myocardial wall. An improved delineation of myocardial viability defects and reduced noise in the 18F-FDG PET images was observed, improving correspondence to subendocardial LGE-MRI findings compared to uncorrected images. CONCLUSION The feasibility of the PET-CMRA framework for simultaneous cardiac PET-MR imaging in a short and predictable scan time (~11 min) has been demonstrated in 14 patients with CTO. Motion correction increased visible length and sharpness of the coronary arteries by CMRA, and improved delineation of the myocardium by 18F-FDG PET, resulting in good agreement with X-ray angiography and LGE-MRI.
Collapse
Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK.
| | - Karl P Kunze
- Nuklearmedizinische Klinik und Poliklinik, Technische Universität München, Munich, Germany
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare, Frimley, UK
| | - Teresa Vitadello
- Nuklearmedizinische Klinik und Poliklinik, Technische Universität München, Munich, Germany
| | - Christoph Rischpler
- Nuklearmedizinische Klinik und Poliklinik, Technische Universität München, Munich, Germany
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
- Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Stephan G Nekolla
- Nuklearmedizinische Klinik und Poliklinik, Technische Universität München, Munich, Germany
- DZHK (Deutsches Zentrum für Herz-Kreislauf-Forschung e.V.) partner site Munich Heart Alliance, Munich, Germany
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
- Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| |
Collapse
|
48
|
A machine-learning framework for automatic reference-free quality assessment in MRI. Magn Reson Imaging 2018; 53:134-147. [PMID: 30036653 DOI: 10.1016/j.mri.2018.07.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 07/06/2018] [Accepted: 07/14/2018] [Indexed: 11/21/2022]
Abstract
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers (support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7% for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies.
Collapse
|
49
|
Abstract
OBJECTIVE The purpose of this article is to provide an update on clinical PET/MRI, including current and developing clinical indications and technical developments. CONCLUSION PET/MRI is evolving rapidly, transitioning from a predominant research focus to exciting clinical practice. Key technical obstacles have been overcome, and further technical advances promise to herald significant advancements in image quality. Further optimization of protocols to address challenges posed by this hybrid modality will ensure the long-term success of PET/MRI.
Collapse
|
50
|
Mannheim JG, Schmid AM, Schwenck J, Katiyar P, Herfert K, Pichler BJ, Disselhorst JA. PET/MRI Hybrid Systems. Semin Nucl Med 2018; 48:332-347. [PMID: 29852943 DOI: 10.1053/j.semnuclmed.2018.02.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Over the last decade, the combination of PET and MRI in one system has proven to be highly successful in basic preclinical research, as well as in clinical research. Nowadays, PET/MRI systems are well established in preclinical imaging and are progressing into clinical applications to provide further insights into specific diseases, therapeutic assessments, and biological pathways. Certain challenges in terms of hardware had to be resolved concurrently with the development of new techniques to be able to reach the full potential of both combined techniques. This review provides an overview of these challenges and describes the opportunities that simultaneous PET/MRI systems can exploit in comparison with stand-alone or other combined hybrid systems. New approaches were developed for simultaneous PET/MRI systems to correct for attenuation of 511 keV photons because MRI does not provide direct information on gamma photon attenuation properties. Furthermore, new algorithms to correct for motion were developed, because MRI can accurately detect motion with high temporal resolution. The additional information gained by the MRI can be employed to correct for partial volume effects as well. The development of new detector designs in combination with fast-decaying scintillator crystal materials enabled time-of-flight detection and incorporation in the reconstruction algorithms. Furthermore, this review lists the currently commercially available systems both for preclinical and clinical imaging and provides an overview of applications in both fields. In this regard, special emphasis has been placed on data analysis and the potential for both modalities to evolve with advanced image analysis tools, such as cluster analysis and machine learning.
Collapse
Affiliation(s)
- Julia G Mannheim
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Andreas M Schmid
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Johannes Schwenck
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany; Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Prateek Katiyar
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kristina Herfert
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany.
| | - Jonathan A Disselhorst
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| |
Collapse
|