Ninon Burgos

Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies

Attenuation correction is an essential requirement for quantification of positron emission tomography (PET) data. In PET/CT acquisition systems, attenuation maps are derived from computed tomography (CT) images. However, in hybrid PET/MR scanners, magnetic resonance imaging (MRI) images do not directly provide a patient-specific attenuation map. The aim of the proposed work is to improve attenuation correction for PET/MR scanners by generating synthetic CTs and attenuation maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patients morphology to a database of MRI/CT pairs, using a local image similarity measure. Results show significant improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an ultrashort-echo-time MRI sequence and to a simplified atlas-based method.

A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients.

Aim: To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor‐implemented MR‐AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas‐based, segmentation‐based, and reconstruction‐based. These proposed methods in general demonstrated improvements compared to vendor‐implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT‐AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT‐based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use. Methods: In total, 11 AC methods were evaluated: two vendor‐implemented (MR‐ACDIXON and MR‐ACUTE), five based on template/atlas information (MR‐ACSEGBONE (Koesters et al., 2016), MR‐ACONTARIO (Anazodo et al., 2014), MR‐ACBOSTON (Izquierdo‐Garcia et al., 2014), MR‐ACUCL (Burgos et al., 2014), and MR‐ACMAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR‐ACMLAA (Benoit et al., 2015)), and three based on image‐segmentation (MR‐ACMUNICH (Cabello et al., 2015), MR‐ACCAR‐RiDR (Juttukonda et al., 2015), and MR‐ACRESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [18F]FDG (210), [11C]PiB (51), and [18F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis. Results: The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR‐ACDIXON (−11.3±3.5)%, MR‐ACUTE (−5.7±2.0)%, MR‐ACONTARIO (−4.3±3.6)%, MR‐ACMUNICH (3.7±2.1)%, MR‐ACMLAA (−1.9±2.6)%, MR‐ACSEGBONE (−1.7±3.6)%, MR‐ACUCL (0.8±1.2)%, MR‐ACCAR‐RiDR (−0.4±1.9)%, MR‐ACMAXPROB (−0.4±1.6)%, MR‐ACBOSTON (−0.3±1.8)%, and MR‐ACRESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR‐ACBOSTON, MR‐ACMAXPROB, MR‐ACRESOLUTE and MR‐ACUCL, ordered alphabetically) showed regional average errors within ±3% of PET with CT‐AC in all regions of the brain with FDG, and the same four methods, as well as MR‐ACCAR‐RiDR, showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir. Conclusions: All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT‐based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR‐ACBOSTON, MR‐ACMAXPROB, MR‐ACRESOLUTE and MR‐ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR‐ACMUNICH and MR‐ACCAR‐RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR‐ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor‐provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR‐AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging.

Attenuation Correction Synthesis for Hybrid PET-MR Scanners

The combination of functional and anatomical imaging technologies such as Positron Emission Tomography (PET) and Computed Tomography (CT) has shown its value in the preclinical and clinical fields. In PET/CT hybrid acquisition systems, CT-derived attenuation maps enable a more accurate PET reconstruction. However, CT provides only very limited soft-tissue contrast and exposes the patient to an additional radiation dose. In comparison, Magnetic Resonance Imaging (MRI) provides good soft-tissue contrast and the ability to study functional activation and tissue microstructures, but does not directly provide patient-specific electron density maps for PET reconstruction. The aim of the proposed work is to improve PET/MR reconstruction by generating synthetic CTs and attenuation-maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patients morphology to a database of pre-acquired MRI/CT pairs. Results show improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an Ultrashort-Echo-Time MRI sequence.

Robust CT Synthesis for Radiotherapy Planning: Application to the Head and Neck Region

In this work, we propose to tackle the problem of magnetic resonance (MR)-based radiotherapy treatment planning in the head & neck area by synthesising computed tomography (CT) from MR images using an iterative multi-atlas approach. The proposed method relies on pre-acquired pairs of non-rigidly aligned T2-weighted MRI and CT images of the neck. To synthesise a pseudo CT, all the MRIs in the database are first registered to the target MRI using a robust affine followed by a deformable registration. An initial pseudo CT is obtained by fusing the mapped atlases according to their morphological similarity to the target. This initial pseudo CT is then combined with the target MR image in order to improve both the registration and fusion stages and refine the synthesis in the bone region.

Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

Highlights • Establishing MRI-only RTP workflows requires synthetic CTs for dose calculation.• This study evaluates the feasibility of using a multi-atlas CT synthesis approach.• The proposed method was validated on head and neck and prostate cancer patients.• Results showed an accurate bone estimation for future patient positioning.• Results showed that synthetic CTs are suitable to perform clinical dose calculations.

Multi atlas-based attenuation correction for brain FDG-PET imaging using a TOF-PET/MR scanner– comparison with clinical single atlas- and CT-based attenuation correction

In this work, we assessed the feasibility of attenuation correction (AC) based on a multi-atlas–based method (m-Atlas) by comparing it with a clinical AC method (single-atlas–based method [s-Atlas]), on a time-of-flight (TOF) PET/MRI scanner. Methods: We enrolled 15 patients. The median patient age was 59 y (age range, 31–80). All patients underwent clinically indicated whole-body 18F-FDG PET/CT for staging, restaging, or follow-up of malignant disease. All patients volunteered for an additional PET/MRI scan of the head (no additional tracer being injected). For each patient, 3 AC maps were generated. Both s-Atlas and m-Atlas AC maps were generated from the same patient-specific LAVA-Flex T1-weighted images being acquired by default on the PET/MRI scanner during the first 18 s of the PET scan. An s-Atlas AC map was extracted by the PET/MRI scanner, and an m-Atlas AC map was created using a Web service tool that automatically generates m-Atlas pseudo-CT images. For comparison, the AC map generated by PET/CT was registered and used as a gold standard. PET images were reconstructed from raw data on the TOF PET/MRI scanner using each AC map. All PET images were normalized to the SPM5 PET template, and 18F-FDG accumulation was quantified in 67 volumes of interest (VOIs; automated anatomic labeling atlas). Relative (%diff) and absolute differences (|%diff|) between images based on each atlas AC and CT-AC were calculated. 18F-FDG uptake in all VOIs and generalized merged VOIs were compared using the paired t test and Bland–Altman test. Results: The range of error on m-Atlas in all 1,005 VOIs was −4.99% to 4.09%. The |%diff| on the m-Atlas was improved by about 20% compared with s-Atlas (s-Atlas vs. m-Atlas: 1.49% ± 1.06% vs. 1.21% ± 0.89%, P < 0.01). In generalized VOIs, %diff on m-Atlas in the temporal lobe and cerebellum was significantly smaller (s-Atlas vs. m-Atlas: temporal lobe, 1.49% ± 1.37% vs. −0.37% ± 1.41%, P < 0.01; cerebellum, 1.55% ± 1.97% vs. −1.15% ± 1.72%, P < 0.01). Conclusion: The errors introduced using either s-Atlas or m-Atlas did not exceed 5% in any brain region investigated. When compared with the clinical s-Atlas, m-Atlas is more accurate, especially in regions close to the skull base.

Voxelwise atlas rating for computer assisted diagnosis: Application to congenital heart diseases of the great arteries

Highlights • This paper presents a voxelwise atlas rating approach for computer-aided diagnosis.• The method relies on multiple atlas databases, but does not require annotated images.• The method reports an accuracy of 97.3%, which is higher than other state-of-the-art methods.

Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning.

Abstract To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}

Joint parametric reconstruction and motion correction framework for dynamic PET data.

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Using florbetapir positron emission tomography to explore cerebrospinal fluid cut points and gray zones in small sample sizes

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