Single STE-MR Acquisition in MR-Based Attenuation Correction of Brain PET Imaging Employing a Fully Automated and Reproducible Level-Set Segmentation Approach

Kazerooni, Anahita Fathi; Ay, Mohammad Reza; Arfaie, Saman; Khateri, Parisa; Rad, Hamidreza Saligheh

doi:10.1007/s11307-016-0990-5

Cited by 4 publications

(6 citation statements)

References 28 publications

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“…Consequently, the bone is often ignored or estimated using atlas registration methods [ 2 ]. Specialized MRI acquisitions using a short echo time (STE), ultrashort echo time (UTE), or zero echo time (ZTE) can be implemented to allow the measurement of the rapidly decaying MR signal in the bone tissue to estimate the bone [ 3 – 6 ]. Unfortunately, UTE and ZTE acquisitions provide little clinical value compared to the conventional diagnostic imaging sequences.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for 18F-FDG PET attenuation correction

et al. 2018

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BackgroundTo develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was developed utilizing deep learning to generate continuously valued pseudo-computed tomography (CT) images from uncorrected 18F-fluorodeoxyglucose (18F-FDG) PET images. A deep convolutional encoder-decoder network was trained to identify tissue contrast in volumetric uncorrected PET images co-registered to CT data. A set of 100 retrospective 3D FDG PET head images was used to train the model. The model was evaluated in another 28 patients by comparing the generated pseudo-CT to the acquired CT using Dice coefficient and mean absolute error (MAE) and finally by comparing reconstructed PET images using the pseudo-CT and acquired CT for attenuation correction. Paired-sample t tests were used for statistical analysis to compare PET reconstruction error using deepAC with CT-based attenuation correction.ResultsdeepAC produced pseudo-CTs with Dice coefficients of 0.80 ± 0.02 for air, 0.94 ± 0.01 for soft tissue, and 0.75 ± 0.03 for bone and MAE of 111 ± 16 HU relative to the PET/CT dataset. deepAC provides quantitatively accurate 18F-FDG PET results with average errors of less than 1% in most brain regions.ConclusionsWe have developed an automated approach (deepAC) that allows generation of a continuously valued pseudo-CT from a single 18F-FDG non-attenuation-corrected (NAC) PET image and evaluated it in PET/CT brain imaging.

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Section: Introductionmentioning

confidence: 99%

A deep learning approach for 18F-FDG PET attenuation correction

et al. 2018

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“…For increased accuracy, the STE and Dixon data were later combined using a segmentation protocol based on fuzzy C-means clustering and morphological operations (Khateri et al 2015). More recently, advanced level-set segmentation was used to reliably generate segmented attenuation maps only from the MR data acquired at a single STE (Fathi Kazerooni et al 2017).…”

Section: Headmentioning

confidence: 99%

Attenuation correction for human PET/MRI studies

Catana

2020

Phys. Med. Biol.

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Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.

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“…Nowadays, the commercial PET/MR systems segment images into three or four tissue classes [50], with the voxels of each tissue class assigned an approximated predefined linear attenuation coefficient [51] producing the attenuation correction map. MR image segmentation was performed using different approaches starting with simple techniques such as level set [52,53], thresholding [50,[54][55][56][57][58][59][60], and radon transform [59] until more complicated techniques such as clustering [61][62][63], classification [64] and deep learning [65][66][67]. Table 1 illustrates different segmentation methods applied to different MR image sequences.…”

Section: Mr Image-based Attenuation Correction For Brain Pet Imagingmentioning

confidence: 99%

“…The quality of PET reconstruction is highly dependent on the registration algorithms accuracy. [52] L e v e ls e t S T E [53] L e v e ls e t U T E [55] Thresholding UTE [57] Thresholding ZTE [58] Thresholding Dixon [59] Radon transform T1 weighted [62] Clustering STE and Dixon [63] Clustering T1 weighted [64] Classification DCE, MP-RAGE, T1 weighted [73] Classification Dixon [65] Deep learning T1 weighted [66] Deep learning UTE and out-of-phase echo [84] Deep learning T1 weighted Different atlas-based techniques were proposed in the literature [46,[68][69][70]. Most of the atlas-based methods use machine learning to estimate the pseudo CT image using MR image features such as signal intensity and geometric metrics to learn the relationship between MR signal and Hounsfield units in CT.…”

Section: Mr Image-based Attenuation Correction For Brain Pet Imagingmentioning

confidence: 99%

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MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

et al. 2020

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Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

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Single STE-MR Acquisition in MR-Based Attenuation Correction of Brain PET Imaging Employing a Fully Automated and Reproducible Level-Set Segmentation Approach

Abstract: The results suggest that the proposed automated segmentation approach can reliably discriminate bony structures from the proximal air and soft tissue in single STE-MR images, which is suitable for generating MR-based μ-maps for attenuation correction of PET data.

Cited by 4 publications

References 28 publications

A deep learning approach for 18F-FDG PET attenuation correction

A deep learning approach for 18F-FDG PET attenuation correction

Attenuation correction for human PET/MRI studies

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

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