Joint correction of respiratory motion artifact and partial volume effect in lung/thoracic PET/CT imaging

Abstract

Purpose: Respiratory motion artifacts and partial volume effects ͑PVEs͒ are two degrading factors that affect the accuracy of image quantification in PET/CT imaging. In this article, the authors propose a joint motion and PVE correction approach ͑JMPC͒ to improve PET quantification by simultaneously correcting for respiratory motion artifacts and PVE in patients with lung/thoracic cancer. The objective of this article is to describe this approach and evaluate its performance using phantom and patient studies. Methods: The proposed joint correction approach incorporates a model of motion blurring, PVE, and object size/shape. A motion blurring kernel ͑MBK͒ is then estimated from the deconvolution of the joint model, while the activity concentration ͑AC͒ of the tumor is estimated from the normalization of the derived MBK. To evaluate the performance of this approach, two phantom studies and eight patient studies were performed. In the phantom studies, two motion waveforms-a linear sinusoidal and a circular motion-were used to control the motion of a sphere, while in the patient studies, all participants were instructed to breathe regularly. For the phantom studies, the resultant MBK was compared to the true MBK by measuring a correlation coefficient between the two kernels. The measured sphere AC derived from the proposed method was compared to the true AC as well as the ACs in images exhibiting PVE only and images exhibiting both PVE and motion blurring. For the patient studies, the resultant MBK was compared to the motion extent derived from a 4D-CT study, while the measured tumor AC was compared to the AC in images exhibiting both PVE and motion blurring. Results: For the phantom studies, the estimated MBK approximated the true MBK with an average correlation coefficient of 0.91. The tumor ACs following the joint correction technique were similar to the true AC with an average difference of 2%. Furthermore, the tumor ACs on the PVE only images and images with both motion blur and PVE effects were, on average, 75% and 47.5% ͑10%͒ of the true AC, respectively, for the linear ͑circular͒ motion phantom study. For the patient studies, the maximum and mean AC/SUV on the PET images following the joint correction are, on average, increased by 125.9% and 371.6%, respectively, when compared to the PET images with both PVE and motion. The motion extents measured from the derived MBK and 4D-CT exhibited an average difference of 1.9 mm. Conclusions: The proposed joint correction approach can improve the accuracy of PET quantification by simultaneously compensating for the respiratory motion artifacts and PVE in lung/ thoracic PET/CT imaging.

Application of Partial Volume Effect Correction and 4D PET in the Quantification of FDG Avid Lung Lesions

“…While gating may reduce motion artifacts, it fails to correct the partial volume effect (PVE) and PVE is most likely to dominate over motion effects for small lesions [16]. Some approaches have been suggested for PVE correction or combined motion and PVE correction [17,22]. However, it remains unclear how 4D PET/CT results are impacted by partial volume correction of metabolic lung lesions in the clinical setting.…”

Précautions, pièges et artéfacts en TEP/TDM du thorax

“…16). Certaines techniques de correction de l'effet de volume partiel (EVP) se développent mais ne sont pas encore implémentées en routine clinique, comme les coefficients de correction ou des fonctions de correction plus élaborées [37,38]. Ce décalage est ainsi à l'origine d'erreurs d'interprétation de lésions du dôme hépatique apparaissant au niveau de la base pulmonaire ou, inversement, de la base au dôme (Fig.…”

Advances in 4D radiation therapy for managing respiration: Part I – 4D imaging

“…The most common method is termed ‘motion-compensated image reconstruction’, which assumes a fixed, periodic trajectory of the anatomy using the model. Motion-compensated reconstruction was developed in PET and SPECT [64, 69, 71–74, 78] to improve the signal to noise ratio, but has since been applied to 4DCBCT. In 4DCBCT, the model is used to either deform the projection data [75, 76] or the reconstructed breathing state image [67, 68] to a reference state, where the data or images are then summed to create a single, high-quality reconstruction.…”