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Li H, Liu H, Tang Y, Yan R, Jiang X, Fan G, Sun W. Decreased Functional Connectivity of Vermis-Ventral Prefrontal Cortex in Bipolar Disorder. Front Hum Neurosci 2021; 15:711688. [PMID: 34335214 PMCID: PMC8322441 DOI: 10.3389/fnhum.2021.711688] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 06/25/2021] [Indexed: 11/21/2022] Open
Abstract
Objectives: To investigate changes in functional connectivity between the vermis and cerebral regions in the resting state among subjects with bipolar disorder (BD). Methods: Thirty participants with BD and 28 healthy controls (HC) underwent the resting state functional magnetic resonance imaging (fMRI). Resting-state functional connectivity (rsFC) of the anterior and posterior vermis was examined. For each participant, rsFC maps of the anterior and posterior vermis were computed and compared across the two groups. Results: rsFC between the whole vermis and ventral prefrontal cortex (VPFC) was significantly lower in the BD groups compared to the HC group, and rsFC between the anterior vermis and the middle cingulate cortex was likewise significantly decreased in the BD group. Limitations: 83.3% of the BD participants were taking medication at the time of the study. Our findings may in part be attributed to treatment differences because we did not examine the effects of medication on rsFC. Further, the mixed BD subtypes in our current study may have confounding effects influencing the results. Conclusions: These rsFC differences of vermis-VPFC between groups may contribute to the BD mood regulation.
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Affiliation(s)
- Huanhuan Li
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Hu Liu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China
| | - Rongkai Yan
- Department of Radiology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China.,Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xiaowei Jiang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Wenge Sun
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
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2
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Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET. Neuroimage 2021; 233:117928. [PMID: 33716154 DOI: 10.1016/j.neuroimage.2021.117928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 02/08/2021] [Accepted: 02/28/2021] [Indexed: 02/07/2023] Open
Abstract
Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.
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Chen S, Yan D, Qin A, Maniawski P, Krauss DJ, Wilson GD. Effect of uncertainties in quantitative 18 F-FDG PET/CT imaging feedback for intratumoral dose-response assessment and dose painting by number. Med Phys 2020; 47:5681-5692. [PMID: 32966627 DOI: 10.1002/mp.14482] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/09/2020] [Accepted: 08/18/2020] [Indexed: 01/14/2023] Open
Abstract
PURPOSE Intratumoral dose response can be detected using serial fluoro-2-deoxyglucose-(FDG) positron emission tomography (PET)/computed tomography (CT) imaging feedback during treatment and used to guide adaptive dose painting by number (DPbN). However, to reliably implement this technique, the effect of uncertainties in quantitative PET/CT imaging feedback on tumor voxel dose-response assessment and DPbN needs to be determined and reduced. METHODS Three major uncertainties, induced by (a) PET imaging partial volume effect (PVE) and (b) tumor deformable image registration (DIR), and (c) variation of the time interval between FDG injection and PET image acquisition (TI), were determined using serial FDG-PET/CT images acquired during chemoradiotherapy of 18 head and neck cancer patients. PET imaging PVE was simulated using the discrepancy between with and without iterative deconvolution-based PVE corrections. Effect of tumor DIR uncertainty was simulated using the discrepancy between two DIR algorithms, including one with and one without soft-tissue mechanical correction for the voxel displacement. The effect of TI variation was simulated using linear interpolation on the dual-point PET/CT images. Tumor voxel pretreatment metabolic activity (SUV0 ) and dose-response matrix (DRM) discrepancies induced by each of the three uncertainties were quantified, respectively. Adverse effects of tumor voxel SUV0 and DRM discrepancies on tumor control probability (TCP) in DPbN were assessed. RESULTS Partial volume effect and TI variations of 10 mins induced a mean ± standard deviation (SD) of tumor voxel SUV0 discrepancies to be -0.7% ± 9.2% and 0% ± 4.8%, respectively. Tumor voxel DRM discrepancies induced by PVE, tumor DIR discrepancy, and TI variations were 0.6% ± 8.9%, 1.7% ± 9.1%, and 0% ± 7%, respectively. Partial volume effect induced SUV0 and DRM discrepancies correlated significantly with the tumor shape and FDG uptake heterogeneity. Tumor DIR uncertainty-induced DRM discrepancy correlated significantly with the tumor volume and shrinkage during treatment. Among the three uncertainties, PVE dominated the adverse effects on the TCP, with a mean ± SD of TCP reduction to be 12.7% ± 9.8% for all tumors if no compensation was applied for. CONCLUSIONS Effect of uncertainties in quantitative FDG-PET/CT imaging feedback on intratumoral dose-response quantification was not negligible. These uncertainties primarily caused by PVE and tumor DIR were highly dependent on individual tumor shape, volume, shrinkage during treatment, and pretreatment SUV heterogeneity, which can be managed individually. The adverse effects of these uncertainties could be minimized by using proper PVE corrections and DIR methods and compensated for in the clinical implementation of DPbN.
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Affiliation(s)
- Shupeng Chen
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, 48073, USA.,Medical Physics, School of Medicine, Wayne State University, Detroit, MI, 48201, USA
| | - Di Yan
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, 48073, USA
| | - An Qin
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, 48073, USA
| | - Piotr Maniawski
- Advanced Molecular Imaging, Philips, Cleveland, OH, 44143, USA
| | - Daniel J Krauss
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, 48073, USA
| | - George D Wilson
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, 48073, USA
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Song TA, Chowdhury SR, Yang F, Dutta J. Super-Resolution PET Imaging Using Convolutional Neural Networks. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2020; 6:518-528. [PMID: 32055649 PMCID: PMC7017584 DOI: 10.1109/tci.2020.2964229] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Positron emission tomography (PET) suffers from severe resolution limitations which reduce its quantitative accuracy. In this paper, we present a super-resolution (SR) imaging technique for PET based on convolutional neural networks (CNNs). To facilitate the resolution recovery process, we incorporate high-resolution (HR) anatomical information based on magnetic resonance (MR) imaging. We introduce the spatial location information of the input image patches as additional CNN inputs to accommodate the spatially-variant nature of the blur kernels in PET. We compared the performance of shallow (3-layer) and very deep (20-layer) CNNs with various combinations of the following inputs: low-resolution (LR) PET, radial locations, axial locations, and HR MR. To validate the CNN architectures, we performed both realistic simulation studies using the BrainWeb digital phantom and clinical studies using neuroimaging datasets. For both simulation and clinical studies, the LR PET images were based on the Siemens HR+ scanner. Two different scenarios were examined in simulation: one where the target HR image is the ground-truth phantom image and another where the target HR image is based on the Siemens HRRT scanner - a high-resolution dedicated brain PET scanner. The latter scenario was also examined using clinical neuroimaging datasets. A number of factors affected relative performance of the different CNN designs examined, including network depth, target image quality, and the resemblance between the target and anatomical images. In general, however, all deep CNNs outperformed classical penalized deconvolution and partial volume correction techniques by large margins both qualitatively (e.g., edge and contrast recovery) and quantitatively (as indicated by three metrics: peak signal-to-noise-ratio, structural similarity index, and contrast-to-noise ratio).
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Affiliation(s)
- Tzu-An Song
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854 USA and co-affiliated with Massachusetts General Hospital, Boston, MA, 02114
| | - Samadrita Roy Chowdhury
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854 USA and co-affiliated with Massachusetts General Hospital, Boston, MA, 02114
| | - Fan Yang
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854 USA and co-affiliated with Massachusetts General Hospital, Boston, MA, 02114
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854 USA and co-affiliated with Massachusetts General Hospital, Boston, MA, 02114
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Song TA, Yang F, Chowdhury SR, Kim K, Johnson KA, El Fakhri G, Li Q, Dutta J. PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2019; 5:530-539. [PMID: 31723575 PMCID: PMC6853071 DOI: 10.1109/tci.2019.2913287] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The intrinsically limited spatial resolution of PET confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution MR images. The framework relies on image-domain post-processing of already-reconstructed PET images by means of spatially-variant deconvolution stabilized by an MR-based joint entropy penalty function. The method is validated through simulation studies based on the BrainWeb digital phantom, experimental studies based on the Hoffman phantom, and clinical neuroimaging studies pertaining to aging and Alzheimer's disease. The developed technique was compared with direct deconvolution and deconvolution stabilized by a quadratic difference penalty, a total variation penalty, and a Bowsher penalty. The BrainWeb simulation study showed improved image quality and quantitative accuracy measured by contrast-to-noise ratio, structural similarity index, root-mean-square error, and peak signal-to-noise ratio generated by this technique. The Hoffman phantom study indicated noticeable improvement in the structural similarity index (relative to the MR image) and gray-to-white contrast-to-noise ratio. Finally, clinical amyloid and tau imaging studies for Alzheimer's disease showed lowering of the coefficient of variation in several key brain regions associated with two target pathologies.
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Affiliation(s)
- Tzu-An Song
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Fan Yang
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Samadrita Roy Chowdhury
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Kyungsang Kim
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | | | | | - Quanzheng Li
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
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6
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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.
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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
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Abstract
In this article, an overview of the current developments and research applications for non-proton magnetic resonance imaging (MRI) at ultrahigh magnetic fields (UHFs) is given. Due to technical and methodical advances, efficient MRI of physiologically relevant nuclei, such as Na, Cl, Cl, K, O, or P has become feasible and is of interest to obtain spatially and temporally resolved information that can be used for biomedical and diagnostic applications. Sodium (Na) MRI is the most widespread multinuclear imaging method with applications ranging over all regions of the human body. Na MRI yields the second largest in vivo NMR signal after the clinically used proton signal (H). However, other nuclei such as O and P (energy metabolism) or Cl and K (cell viability) are used in an increasing number of MRI studies at UHF. One major advancement has been the increased availability of whole-body MR scanners with UHFs (B0 ≥7T) expanding the range of detectable nuclei. Nevertheless, efforts in terms of pulse sequence and post-processing developments as well as hardware designs must be made to obtain valuable information in clinically feasible measurement times. This review summarizes the available methods in the field of non-proton UHF MRI, especially for Na MRI, as well as introduces potential applications in clinical research.
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Affiliation(s)
- Sebastian C Niesporek
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Armin M Nagel
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Institute of Medical Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Tanja Platt
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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8
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Hanzouli-Ben Salah H, Lapuyade-Lahorgue J, Bert J, Benoit D, Lambin P, Van Baardwijk A, Monfrini E, Pieczynski W, Visvikis D, Hatt M. A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation. Med Phys 2017; 44:5835-5848. [PMID: 28837224 DOI: 10.1002/mp.12531] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 07/05/2017] [Accepted: 08/08/2017] [Indexed: 01/03/2023] Open
Abstract
PURPOSE The purpose of this study was to investigate the use of a probabilistic quad-tree graph (hidden Markov tree, HMT) to provide fast computation, robustness and an interpretational framework for multimodality image processing and to evaluate this framework for single gross tumor target (GTV) delineation from both positron emission tomography (PET) and computed tomography (CT) images. METHODS We exploited joint statistical dependencies between hidden states to handle the data stack using multi-observation, multi-resolution of HMT and Bayesian inference. This framework was applied to segmentation of lung tumors in PET/CT datasets taking into consideration simultaneously the CT and the PET image information. PET and CT images were considered using either the original voxels intensities, or after wavelet/contourlet enhancement. The Dice similarity coefficient (DSC), sensitivity (SE), positive predictive value (PPV) were used to assess the performance of the proposed approach on one simulated and 15 clinical PET/CT datasets of non-small cell lung cancer (NSCLC) cases. The surrogate of truth was a statistical consensus (obtained with the Simultaneous Truth and Performance Level Estimation algorithm) of three manual delineations performed by experts on fused PET/CT images. The proposed framework was applied to PET-only, CT-only and PET/CT datasets, and were compared to standard and improved fuzzy c-means (FCM) multimodal implementations. RESULTS A high agreement with the consensus of manual delineations was observed when using both PET and CT images. Contourlet-based HMT led to the best results with a DSC of 0.92 ± 0.11 compared to 0.89 ± 0.13 and 0.90 ± 0.12 for Intensity-based HMT and Wavelet-based HMT, respectively. Considering PET or CT only in the HMT led to much lower accuracy. Standard and improved FCM led to comparatively lower accuracy than HMT, even when considering multimodal implementations. CONCLUSIONS We evaluated the accuracy of the proposed HMT-based framework for PET/CT image segmentation. The proposed method reached good accuracy, especially with pre-processing in the contourlet domain.
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Affiliation(s)
| | | | - Julien Bert
- INSERM, UMR 1101, LaTIM, IBSAM, UBO, UBL, Brest, France
| | - Didier Benoit
- INSERM, UMR 1101, LaTIM, IBSAM, UBO, UBL, Brest, France
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Angela Van Baardwijk
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Emmanuel Monfrini
- SAMOVAR, Télécom SudParis, CNRS, Université Paris-Saclay, 9 rue Charles Fourier, 91000, Evry, France
| | - Wojciech Pieczynski
- SAMOVAR, Télécom SudParis, CNRS, Université Paris-Saclay, 9 rue Charles Fourier, 91000, Evry, France
| | | | - Mathieu Hatt
- INSERM, UMR 1101, LaTIM, IBSAM, UBO, UBL, Brest, France
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Akram MSH, Obata T, Suga M, Nishikido F, Yoshida E, Saito K, Yamaya T. MRI compatibility study of an integrated PET/RF-coil prototype system at 3T. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 283:62-70. [PMID: 28881235 DOI: 10.1016/j.jmr.2017.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2016] [Revised: 08/22/2017] [Accepted: 08/23/2017] [Indexed: 06/07/2023]
Abstract
We have been working on the development of a PET insert for existing magnetic resonance imaging (MRI) systems for simultaneous PET/MR imaging, which integrates radiofrequency (RF)-shielded PET detector modules with an RF head coil. In order to avoid interferences between the PET detector circuits and the different MRI-generated electromagnetic fields, PET detector circuits were installed inside eight Cu-shielded fiber-reinforced plastic boxes, and these eight shielded PET modules were integrated in between the eight elements of a 270-mm-diameter and 280-mm-axial-length cylindrical birdcage RF coil, which was designed to be used with a 3-T clinical MRI system. The diameter of the PET scintillators with a 12-mm axial field-of-view became 255mm, which was very close to the imaging region. In this study, we have investigated the effects of this PET/RF-coil integrated system on the performance of MRI, which include the evaluation of static field (Bo) inhomogeneity, RF field (B1) distribution, local specific absorption rate (SAR) distribution, average SAR, and signal-to-noise ratio (SNR). For the central 170-mm-diameter and 80-mm-axial-length of a homogenous cylindrical phantom (with the total diameter of 200mm and axial-length of 100mm), an increase of about a maximum of 3μT in the Bo inhomogeneity was found, both in the central and 40-mm off-centered transverse planes, and a 5 percentage point increase of B1 field inhomogeneity was observed in the central transverse plane (from 84% without PET to 79% with PET), while B1 homogeneity along the coronal plane was almost unchanged (77%) following the integration of PET with the RF head coil. The average SAR and maximum local SAR were increased by 1.21 and 1.62 times, respectively. However, the SNR study for both spin-echo and gradient-echo sequences showed a reduction of about 70% and 60%, respectively, because of the shielded PET modules. The overall results prove the feasibility of this integrated PET/RF-coil system for using with the existing MRI system.
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Affiliation(s)
- Md Shahadat Hossain Akram
- National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Japan.
| | - Takayuki Obata
- National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Japan
| | - Mikio Suga
- Center for Frontier Medical Engineering, Chiba University, Japan
| | - Fumihiko Nishikido
- National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Japan
| | - Eiji Yoshida
- National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Japan
| | - Kazuyuki Saito
- Center for Frontier Medical Engineering, Chiba University, Japan
| | - Taiga Yamaya
- National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Japan.
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Matsubara K, Ibaraki M, Shimada H, Ikoma Y, Suhara T, Kinoshita T, Ito H. Impact of spillover from white matter by partial volume effect on quantification of amyloid deposition with [ 11C]PiB PET. Neuroimage 2016; 143:316-324. [PMID: 27639351 DOI: 10.1016/j.neuroimage.2016.09.028] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 08/27/2016] [Accepted: 09/13/2016] [Indexed: 11/29/2022] Open
Abstract
High non-specific uptake of [11C]Pittsburgh compound B ([11C]PiB) in white matter and signal spillover from white matter, due to partial volume effects, confound radioactivity measured in positron emission tomography (PET) with [11C]PiB. We aimed to reveal the partial volume effect in absolute values of kinetic parameters for [11C]PiB, in terms of spillover from white matter. Dynamic data acquired in [11C]PiB PET scans with five healthy volunteers and eight patients with Alzheimer's disease were corrected with region-based and voxel-based partial volume corrections. Binding potential (BPND) was estimated using the two-tissue compartment model analysis with a plasma input function. Partial volume corrections significantly decreased cortical BPND values. The degree of decrease in healthy volunteers (-52.7±5.8%) was larger than that in Alzheimer's disease patients (-11.9±4.2%). The simulation demonstrated that white matter spillover signals due to the partial volume effect resulted in an overestimation of cortical BPND, with a greater degree of overestimation for lower BPND values. Thus, an overestimation due to partial volume effects is more severe in healthy volunteers than in Alzheimer's disease patients. Partial volume corrections may be useful for accurately quantifying Aβ deposition in cortical regions.
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Affiliation(s)
- Keisuke Matsubara
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita, Japan.
| | - Masanobu Ibaraki
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita, Japan
| | - Hitoshi Shimada
- Department of Functional Brain Imaging Research (DOFI), National Institute of Radiological Sciences (NIRS), National Institute for Quantum and Radiological Science and Technology (QST), Japan
| | - Yoko Ikoma
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences (NIRS), National Institute for Quantum and Radiological Science and Technology (QST), Japan
| | - Tetsuya Suhara
- Department of Functional Brain Imaging Research (DOFI), National Institute of Radiological Sciences (NIRS), National Institute for Quantum and Radiological Science and Technology (QST), Japan
| | - Toshibumi Kinoshita
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita, Japan
| | - Hiroshi Ito
- Department of Functional Brain Imaging Research (DOFI), National Institute of Radiological Sciences (NIRS), National Institute for Quantum and Radiological Science and Technology (QST), Japan; Department of Radiology and Nuclear Medicine, Fukushima Medical University, Japan
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12
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Abstract
Multimodal imaging has led to a more detailed exploration of different physiologic processes with integrated PET/MR imaging being the most recent entry. Although the clinical need is still questioned, it is well recognized that it represents one of the most active and promising fields of medical imaging research in terms of software and hardware. The hardware developments have moved from small detector components to high-performance PET inserts and new concepts in full systems. Conversely, the software focuses on the efficient performance of necessary corrections without the use of CT data. The most recent developments in both directions are reviewed.
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Affiliation(s)
- Charalampos Tsoumpas
- Division of Biomedical Imaging, Faculty of Medicine and Health, University of Leeds, 8.001a, Worsley Building, Clarendon Way, Leeds LS2 9JT, UK
| | - Dimitris Visvikis
- LaTIM UMR 1101, INSERM, University of Brest, Bat 1, 1er etage, 5 avenue Foch, Brest 29609, France
| | - George Loudos
- Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spiridonos 28, Egaleo, Athens 12210, Greece.
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Gunn RN, Slifstein M, Searle GE, Price JC. Quantitative imaging of protein targets in the human brain with PET. Phys Med Biol 2015; 60:R363-411. [DOI: 10.1088/0031-9155/60/22/r363] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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14
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Niesporek SC, Hoffmann SH, Berger MC, Benkhedah N, Kujawa A, Bachert P, Nagel AM. Partial volume correction for in vivo 23 Na-MRI data of the human brain. Neuroimage 2015; 112:353-363. [DOI: 10.1016/j.neuroimage.2015.03.025] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 02/02/2015] [Accepted: 03/11/2015] [Indexed: 12/16/2022] Open
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15
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Yan J, Lim JCS, Townsend DW. MRI-guided brain PET image filtering and partial volume correction. Phys Med Biol 2015; 60:961-76. [DOI: 10.1088/0031-9155/60/3/961] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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16
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Su Y, Blazey TM, Snyder AZ, Raichle ME, Marcus DS, Ances BM, Bateman RJ, Cairns NJ, Aldea P, Cash L, Christensen JJ, Friedrichsen K, Hornbeck RC, Farrar AM, Owen CJ, Mayeux R, Brickman AM, Klunk W, Price JC, Thompson PM, Ghetti B, Saykin AJ, Sperling RA, Johnson KA, Schofield PR, Buckles V, Morris JC, Benzinger TLS. Partial volume correction in quantitative amyloid imaging. Neuroimage 2014; 107:55-64. [PMID: 25485714 DOI: 10.1016/j.neuroimage.2014.11.058] [Citation(s) in RCA: 170] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 11/26/2014] [Accepted: 11/30/2014] [Indexed: 12/16/2022] Open
Abstract
Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.
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Affiliation(s)
- Yi Su
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA.
| | - Tyler M Blazey
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Abraham Z Snyder
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Marcus E Raichle
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Beau M Ances
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Nigel J Cairns
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Patricia Aldea
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Lisa Cash
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Jon J Christensen
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Karl Friedrichsen
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Russ C Hornbeck
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Angela M Farrar
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Christopher J Owen
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Richard Mayeux
- Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA
| | - Adam M Brickman
- Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA
| | - William Klunk
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Julie C Price
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA 90032, USA; Department of Neurology, University of Southern California, Los Angeles, CA 90032, USA; Department of Psychiatry, University of Southern California, Los Angeles, CA 90032, USA; Department of Engineering, University of Southern California, Los Angeles, CA 90032, USA; Department of Radiology, University of Southern California, Los Angeles, CA 90032, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA 90032, USA; Department of Ophthalmology, University of Southern California, Los Angeles, CA 90032, USA
| | - Bernadino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, NSW 2031, Australia; School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Virginia Buckles
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
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Funck T, Paquette C, Evans A, Thiel A. Surface-based partial-volume correction for high-resolution PET. Neuroimage 2014; 102 Pt 2:674-87. [PMID: 25175542 DOI: 10.1016/j.neuroimage.2014.08.037] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Revised: 08/09/2014] [Accepted: 08/20/2014] [Indexed: 10/24/2022] Open
Abstract
Tissue radioactivity concentrations, measured with positron emission tomography (PET) are subject to partial volume effects (PVE) due to the limited spatial resolution of the scanner. Last generation high-resolution PET cameras with a full width at half maximum (FWHM) of 2-4mm are less prone to PVEs than previous generations. Corrections for PVEs are still necessary, especially when studying small brain stem nuclei or small variations in cortical neuroreceptor concentrations which may be related to cytoarchitectonic differences. Although several partial-volume correction (PVC) algorithms exist, these are frequently based on a priori assumptions about tracer distribution or only yield corrected values of regional activity concentrations without providing PVE corrected images. We developed a new iterative deconvolution algorithm (idSURF) for PVC of PET images that aims to overcome these limitations by using two innovative techniques: 1) the incorporation of anatomic information from a cortical gray matter surface representation, extracted from magnetic resonance imaging (MRI) and 2) the use of anatomically constrained filtering to attenuate noise. PVE corrected images were generated with idSURF implemented into a non-interactive processing pipeline. idSURF was validated using simulated and clinical PET data sets and compared to a frequently used standard PVC method (Geometric Transfer Matrix: GTM). The results on simulated data sets show that idSURF consistently recovers accurate radiotracer concentrations within 1-5% of true values. Both radiotracer concentrations and non-displaceable binding potential (BPnd) values derived from clinical PET data sets with idSURF were highly correlated with those obtained with the standard PVC method (R(2) = 0.99, error = 0%-3.2%). These results suggest that idSURF is a valid and potentially clinically useful PVC method for automatic processing of large numbers of PET data sets.
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Affiliation(s)
- Thomas Funck
- Montreal Neurological Institute, McGill University, Montreal, Canada; Jewish General Hospital, Montreal Canada
| | - Caroline Paquette
- Jewish General Hospital, Montreal Canada; Department of Neurology and Neurosurgery, Montreal, Canada
| | - Alan Evans
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander Thiel
- Jewish General Hospital, Montreal Canada; Department of Neurology and Neurosurgery, Montreal, Canada.
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18
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Fung EK, Carson RE. Cerebral blood flow with [15O]water PET studies using an image-derived input function and MR-defined carotid centerlines. Phys Med Biol 2013; 58:1903-23. [PMID: 23442733 DOI: 10.1088/0031-9155/58/6/1903] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Full quantitative analysis of brain PET data requires knowledge of the arterial input function into the brain. Such data are normally acquired by arterial sampling with corrections for delay and dispersion to account for the distant sampling site. Several attempts have been made to extract an image-derived input function (IDIF) directly from the internal carotid arteries that supply the brain and are often visible in brain PET images. We have devised a method of delineating the internal carotids in co-registered magnetic resonance (MR) images using the level-set method and applying the segmentations to PET images using a novel centerline approach. Centerlines of the segmented carotids were modeled as cubic splines and re-registered in PET images summed over the early portion of the scan. Using information from the anatomical center of the vessel should minimize partial volume and spillover effects. Centerline time-activity curves were taken as the mean of the values for points along the centerline interpolated from neighboring voxels. A scale factor correction was derived from calculation of cerebral blood flow (CBF) using gold standard arterial blood measurements. We have applied the method to human subject data from multiple injections of [(15)O]water on the HRRT. The method was assessed by calculating the area under the curve (AUC) of the IDIF and the CBF, and comparing these to values computed using the gold standard arterial input curve. The average ratio of IDIF to arterial AUC (apparent recovery coefficient: aRC) across 9 subjects with multiple (n = 69) injections was 0.49 ± 0.09 at 0-30 s post tracer arrival, 0.45 ± 0.09 at 30-60 s, and 0.46 ± 0.09 at 60-90 s. Gray and white matter CBF values were 61.4 ± 11.0 and 15.6 ± 3.0 mL/min/100 g tissue using sampled blood data. Using IDIF centerlines scaled by the average aRC over each subjects' injections, gray and white matter CBF values were 61.3 ± 13.5 and 15.5 ± 3.4 mL/min/100 g tissue. Using global average aRC values, the means were unchanged, and intersubject variability was noticeably reduced. This MR-based centerline method with local re-registration to [(15)O]water PET yields a consistent IDIF over multiple injections in the same subject, thus permitting the absolute quantification of CBF without arterial input function measurements.
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Affiliation(s)
- Edward K Fung
- Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT 06520, USA.
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Erlandsson K, Buvat I, Pretorius PH, Thomas BA, Hutton BF. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol 2012; 57:R119-59. [DOI: 10.1088/0031-9155/57/21/r119] [Citation(s) in RCA: 320] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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