The Promise of Hybrid PET\/MRI: Technical advances and clinical applications

Zaidi, Habib; Becker, Minerva

doi:10.1109/msp.2015.2482225

Cited by 27 publications

(28 citation statements)

References 103 publications

(189 reference statements)

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“…Consequently, simultaneous functional imaging using fPET and fMRI (by arterial spin labeling (ASL) and blood oxygen level dependent (BOLD) contrasts) enables the study of neurovascular coupling [182], especially in the study of drug challenges. Also, sMRI scans interleaved with PET acquisition can enable improved PET resolution and SNR following MRI-guided attenuation and partial-volume corrections, motion compensation, and reconstruction [183]. …”

Section: Future Directionsmentioning

confidence: 99%

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Silva

Plis

Sui

et al. 2016

IEEE J. Sel. Top. Signal Process.

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In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. The main focus has been on extracting “networks” represented as the underlying latent sources. While the broad success in learning latent representations from multiple datasets has promoted the wide presence of BSS in modern neuroscience, it also introduced a wide variety of objective functions, underlying graphical structures, and parameter constraints for each method. Such diversity, combined with a host of datatype-specific know-how, can cause a sense of disorder and confusion, hampering a practitioner’s judgment and impeding further development. We organize the diverse landscape of BSS models by exposing its key features and combining them to establish a novel unifying view of the area. In the process, we unveil important connections among models according to their properties and subspace structures. Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model for their applications. Equipped with that knowledge, we review the current state of BSS applications to neuroimaging. The gained insight into model connections elicits a broader sense of generalization, highlighting several directions for model development. In light of that, we discuss emerging multi-dataset multidimensional (MDM) models and summarize their benefits for the study of the healthy brain and disease-related changes.

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Section: Future Directionsmentioning

confidence: 99%

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Silva

Plis

Sui

et al. 2016

IEEE J. Sel. Top. Signal Process.

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“…Positron emission tomography/magnetic resonance imaging (PET/MRI) is a two-in-one hybrid imaging technology that could provide competitive advantages over PET/X-ray computed tomography (PET/CT), including superior soft tissue contrast, advanced options for multiparametric MRI and MR spectroscopy, superior motion-tracking capabilities, and reduced radiation exposure [1]. A number of studies reported that 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) PET/MRI has the potential to qualitatively and quantitatively improve tumor characterization in breast cancer [2,3].…”

Section: Introductionmentioning

confidence: 99%

Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

et al. 2019

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Purpose: The aim of this work is to investigate the impact of tissue classification in magnetic resonance imaging (MRI)-guided positron emission tomography (PET) attenuation correction (AC) for whole-body (WB) Patlak net uptake rate constant (K i ) imaging in PET/MRI studies. Procedures: WB dynamic PET/CT data were acquired for 14 patients. The CT images were utilized to generate attenuation maps (μ-map CTAC ) of continuous attenuation coefficient values (A coeff ). The μ-map CTAC were then segmented into four tissue classes (μ-map 4-classes ), namely background (air), lung, fat, and soft tissue, where a predefined A coeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-map CTAC were then assigned a predefined soft tissue A coeff (0.1 cm −1 ) to produce an AC μ-map without bones (μ-map no-bones ). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-map CTAC , μ-map 4-classes , and μ-map no-bones (PET CTAC, PET 4-classes , and PET no-bones ), respectively. WB indirect and direct parametric K i images were generated using Patlak graphical analysis. Malignant lesions were delineated on PET images with an automatic segmentation method that uses an active contour model (MASAC). Then, the quantitative metrics of the metabolically active tumor volume (MATV), target-to-background (TBR), contrast-to-noise ratio (CNR), peak region-of-interest (ROI peak ), maximum region-of-interest (ROI max ), mean region-of-interest (ROI mean ), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET 4-classes and PET no-bones against PET CTAC for all images. The same test was also adopted to compare the differences between SUV, indirect K i , and direct K i images for each evaluated AC method.Results: No significant differences in MATV, TBR, and CNR were observed between PET 4classes and PET CTAC for either SUV or K i images. PET 4-classes significantly overestimated ROI peak , ROI max , ROI mean , as well as MVP scores compared with PET CTAC in both SUV and K i images. SUV images exhibited the highest median relative errors for PET 4-classes with respect to PET CTAC (RE 4-classes ): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROI peak , ROI max , ROI mean , and MVP, respectively. On the contrary, K i images showed slightly reduced RE 4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROI peak , ROI max , ROI mean , and MVP, respectively. A higher TBR was observed on indirect and direct K i images relative to SUV, while direct K i images demonstrated the highest CNR. Conclusions: Four-tissue class AC may impact SUV and K i parameter estimation but only to a limited extent, thereby suggesting that WB Patlak K i imaging for dynamic WB PET/MRI studies is feasible. Patlak K i imaging can enhance TBR, thereby facilitating lesion segmentation and quantification. However, patient-specific A coeff for each tissue class should be used when possible to address the high inter...

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“…Most of the current approaches to tackle the problem of the reconstruction of isotope concentration distribution can be classified into two general categories, namely the analytical methods [11,12], which rely on the inversion of Radon transform, and the iterative approaches [13,14,15,16], which are mainly based on a statistical description of the physical problem. Because of the random nature of radioactive disintegration, the tomographic data are noisy and therefore it is logical to regard PET reconstruction as a statistical estimation problem.…”

Section: Introductionmentioning

confidence: 99%

RECONSTRUCTION OF MaxEnt IMAGES FROM PET CAMERA

Üstündağ¹

2017

RAD Conference Proceedings

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Abstract. We study here one of the imaging techniques, used in nuclear medicine, called a Positron Emission Tomographic (PET) imaging that provides information about many biological processes that are essential to the functioning of the organ that is being visualized. Our emphasis is given to applications of the maximum entropy image reconstruction method called "Cambridge MaxEnt Package" (CMEP) for recovering images of the human brain from data obtained by PET camera. Computer simulations demonstrate its usefulness.

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The Promise of Hybrid PET\/MRI: Technical advances and clinical applications

Cited by 27 publications

References 103 publications

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

RECONSTRUCTION OF MaxEnt IMAGES FROM PET CAMERA

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