Respiratory-resolved MR-based attenuation correction for motion-compensated cardiac PET-MR

Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography

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.

Assessing the qualitative and quantitative impacts of simple two-class vs multiple tissue-class MR-based attenuation correction for cardiac PET/MR

BACKGROUND

Hybrid PET/MR imaging has significant potential in cardiology due to its combination of molecular PET imaging and cardiac MR. Multi-tissue-class MR-based attenuation correction (MRAC) is necessary for accurate PET quantification. Moreover, for thoracic PET imaging, respiration is known to lead to misalignments of MRAC and PET data that result in PET artifacts. These factors can be addressed by using multi-echo MR for tissue segmentation and motion-robust or motion-gated acquisitions. However, the combination of these strategies is not routinely available and can be prone to errors. In this study, we examine the qualitative and quantitative impacts of multi-class MRAC compared to a more widely available simple two-class MRAC for cardiac PET/MR.

METHODS AND RESULTS

In a cohort of patients with cardiac sarcoidosis, we acquired MRAC data using multi-echo radial gradient-echo MR imaging. Water-fat separation was used to produce attenuation maps with up to 4 tissue classes including water-based soft tissue, fat, lung, and background air. Simultaneously acquired 18F-fluorodeoxyglucose PET data were subsequently reconstructed using each attenuation map separately. PET uptake values were measured in the myocardium and compared between different PET images. The inclusion of lung and subcutaneous fat in the MRAC maps significantly affected the quantification of 18F-fluorodeoxyglucose activity in the myocardium but only moderately altered the appearance of the PET image without introduction of image artifacts.

CONCLUSION

Optimal MRAC for cardiac PET/MR applications should include segmentation of all tissues in combination with compensation for the respiratory-related motion of the heart. Simple two-class MRAC is adequate for qualitative clinical assessment.

Clinical Applications of PET/MR Imaging

PET/MR imaging is in routine clinical use and is at least as effective as PET/CT for oncologic and neurologic studies with advantages with certain PET radiopharmaceuticals and applications. In addition, whole body PET/MR imaging substantially reduces radiation dosages compared with PET/CT which is particularly relevant to pediatric and young adult population. For cancer imaging, assessment of hepatic, pelvic, and soft-tissue malignancies may benefit from PET/MR imaging. For neurologic imaging, volumetric brain MR imaging can detect regional volume loss relevant to cognitive impairment and epilepsy. In addition, the single-bed position acquisition enables dynamic brain PET imaging without extending the total study length which has the potential to enhance the diagnostic information from PET.

Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Evaluation of synergistic image registration for motion-corrected coronary NaF-PET-MR

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'.

MR-guided motion-corrected PET image reconstruction for cardiac PET-MR

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.

Imaging coronary plaques using 3D motion-compensated [18F]NaF PET/MR

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.

Attenuation correction for human PET/MRI studies

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.

Motion correction and noise removing in lung diffusion-weighted MRI using low-rank decomposition

Lung diffusion-weighted magnetic resonance imaging (DWI) has shown a promising value in lung lesion detection, diagnosis, differentiation, and staging. However, the respiratory and cardiac motion, blood flow, and lung hysteresis may contribute to the blurring, resulting in unclear lung images. The image blurring could adversely affect diagnosis performance. The purpose of this study is to reduce the DWI blurring and assess its positive effect on diagnosis. The retrospective study includes 71 patients. In this paper, a motion correction and noise removal method using low-rank decomposition is proposed, which can reduce the DWI blurring by exploit the spatiotemporal continuity sequences. The deblurring performances are evaluated by qualitative and quantitative assessment, and the performance of diagnosis of lung cancer is measured by area under curve (AUC). In the view of the qualitative assessment, the deformation of the lung mass is reduced, and the blurring of the lung tumor edge is alleviated. Noise in the apparent diffusion coefficient (ADC) map is greatly reduced. For quantitative assessment, mutual information (MI) and Pearson correlation coefficient (Pearson-Coff) are 1.30 and 0.82 before the decomposition and 1.40 and 0.85 after the decomposition. Both the difference in MI and Pearson-Coff are statistically significant (p < 0.05). For the positive effect of deblurring on diagnosis of lung cancer, the AUC was improved from 0.731 to 0.841 using three-fold cross validation. We conclude that the low-rank matrix decomposition method is promising in reducing the errors in DWI lung images caused by noise and artifacts and improving diagnostics. Further investigations are warranted to understand the full utilities of the low-rank decomposition on lung DWI images. Graphical abstract.

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single‐valued population‐based lung LAC, and better estimation is needed to improve quantification. Given the under‐appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single‐valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission‐based schemes. Potential strategies for future developments are also presented.

The developing role of FDG PET imaging for prognostication and radiotherapy target volume delineation in non-small cell lung cancer

Advancements in functional imaging technology have allowed new possibilities in contouring of target volumes, monitoring therapy, and predicting treatment outcome in non-small cell lung cancer (NSCLC). Consequently, the role of ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG PET) has expanded in the last decades from a stand-alone diagnostic tool to a versatile instrument integrated with computed tomography (CT), with a prominent role in lung cancer radiotherapy. This review outlines the most recent literature on developments in FDG PET imaging for prognostication and radiotherapy target volume delineation (TVD) in NSCLC. We also describe the challenges facing the clinical implementation of these developments and present new ideas for future research.