Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis

Abstract

Methods for joint activity reconstruction and attenuation reconstruction of time-of-flight (TOF) PET data provide an effective solution to attenuation correction when no (or incomplete or inaccurate) information on attenuation is available. One of the main barriers limiting use of these methods in clinical practice is their lack of validation in a relatively large patient database. In this contribution, we aim to validate reconstruction performed with maximum-likelihood activity reconstruction and attenuation registration (MLRR) in a whole-body patient dataset. Furthermore, a partial validation (because the scale problem of the algorithm is avoided for now) of reconstruction performed with maximum-likelihood activity and attenuation (MLAA) is also provided. We present a quantitative comparison between these 2 methods of joint reconstruction and the current clinical gold standard, maximum-likelihood expectation maximization (MLEM) with CT-based attenuation correction. The whole-body TOF PET emission data of each patient dataset were processed as a whole to reconstruct an activity volume covering all the acquired bed positions, helping reduce the problem of a scale per bed position in MLAA to a global scale for the entire activity volume. Three reconstruction algorithms were used: MLEM, MLRR, and MLAA. A maximum-likelihood scaling of the single-scatter simulation estimate to the emission data was used for scatter correction. The reconstruction results for various regions of interest were then analyzed. The joint reconstructions of the whole-body patient dataset provided better quantification than the gold standard in cases of PET and CT misalignment caused by patient or organ motion. Our quantitative analysis showed a difference of -4.2% ± 2.3% between MLRR and MLEM and a difference of -7.5% ± 4.6% between MLAA and MLEM, averaged over all regions of interest. Joint reconstruction of activity and attenuation provides a useful means to estimate tracer distribution when CT-based-attenuation images are subject to misalignment or are not available. With an accurate estimate of the scatter contribution in the emission measurements, the joint reconstructions of TOF PET data are within clinically acceptable accuracy.

Improving the CT (140 kVp) to PET (511 keV) conversion in PET/MR hardware component attenuation correction

“…Today, the AC of human tissues in PET/MR is usually solved by applying dedicated MR sequences, subsequent tissue segmentation integrating bone models, atlas‐based methods and the emission‐based estimation of attenuation 13–16,20,21 . Moreover, joint reconstruction algorithms add to the palette of methods for human tissue AC, and deep learning now enters the field to provide MR‐derived pseudo‐CT data for AC of human tissues …”

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

“…Recent methods, including maximum-likelihood activity reconstruction and attenuation registration (MLRR), attempt to overcome this problem by estimating the deformation field based on the PET data. 103,132 However, these approaches currently do not take density changes during respiration into account. Moreover, it is difficult to see these methods becoming widespread in clinical practice due to the need for a previous scan and the associated data management issues even if it exists.…”

Simultaneous emission and attenuation reconstruction in time-of-flight PET using a reference object

“…However, as pointed by Defrise et al [16], a major drawback of this approach is that the attenuation sinogram can be only determined up to an additive constant. This limiting factor is considered to be the main reason why these methods are still not implemented in clinical practice [17].…”

“…Several methods have been proposed to overcome this limitation. Some of these methods rely on available CT data [17][18][19][20] which might not be available in most cases where the use of simultaneous reconstruction of emission and attenuation is valuable like PET/MRI and stand-alone PET scanners. Other methods propose searching for an optimal initialization of the attenuation map based on MR images [14,20,21], which are prone to segmentation and misclassification errors, are limited by data availability, and rely on patient databases.…”