Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors

Lung cancer is today the most common cause for cancer mortality, with an estimated 234,030 new cases occurring in the USA alone in 2018 [1]. Positron-emission tomography (PET) allows for imaging and quantitation of radiotracer uptake in vivo and may thereby visualize physiologic and pathophysiologic processes in patients [2]. For instance, 18F-fluorodeoxyglucose (18F-FDG) PET may be used to detect and quantify increased glucose metabolism in neoplastic lesions, such as primary tumors, lymph node metastases, or distant metastases [3]. Computed tomography (CT) enables a detailed assessment of local lung tumor extent, owing to its comparably higher spatial resolution [4, 5]. Therefore, hybrid imaging with 18F-FDG PET/CT has evolved as an important tool for comprehensive staging of lung cancer patients and is reimbursed by health insurances in many countries worldwide [6].

However, there are two main limitations of PET: first, the relatively low spatial resolution which may affect images both visually and quantitatively [7] and second, the generally relatively low signal-to-noise ratio [8]. There have been several technical advances within the last decade, including new hardware features, such as time-of-flight (TOF) acquisition [9] and silicon-based photodetectors (SIPM) as well as advanced image reconstruction methods, leading to an overall improvement of PET images. Iterative reconstruction methods have been widely adopted, replacing the initially used filtered back projection technique due to decreased artifacts and image noise [10]. Additionally, new reconstruction techniques, such as ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM), came into clinical use and lead to a further improvement of image quality [11].

Solid-state digital PET detectors use a novel combination of lutetium-based scintillator crystal arrays with a silicon photomultiplier, which improves intrinsic sensitivity and temporal resolution [12]. These novel detector elements were made available clinically with the introduction of integrated digital PET/MR in 2013 [13]. Several technical and clinical studies showed a superior performance of digital compared to analog detector systems [14]. While PET/MR today is mainly limited to academic environments, silicon-based digital detector technology became available to PET/CT in the beginning of 2017, paving the way for a dissemination of this technique into the clinical field worldwide. A first study carried out in a mixed population of cancer patients showed an improved performance of digital PET/CT with regard to pathologic and physiologic structures [12].

The purpose of our study was to evaluate different reconstruction algorithms on the latest-generation digital PET/CT scanner and to identify the optimal reconstruction method for the quantitation of histopathologically confirmed lung cancer.

This study sought to evaluate the impact of different PET reconstruction algorithms on image quality and quantitative parameters in patients with histopathologically confirmed lung cancer using a latest-generation silicon-based digital detector PET/CT scanner. The major findings of our study are as follows: (1) BSREM reconstruction algorithms lead to an increased image quality, image sharpness, and tumor lesion conspicuity compared to OSEM; (2) adjusting β-values to the injected 18F-FDG activity allows for an individual dose-based optimization of image quality of PET images; and (3) BSREM reconstruction leads to a significant increase of SUV_max, which is most prominent with lower β-values (e.g., 350).

PET/CT using 18F-FDG as radiotracer has evolved to be the most important cross-sectional imaging modality for whole-body staging of patients with lung cancer in recent years and is recommended by various international guidelines [6, 21]. There is, however, an inherent relatively low spatial resolution [7] as well as a generally low signal-to-noise ratio of PET [8]. This is why improving the image quality of PET is an ongoing subject of research and, besides new hardware features such as TOF acquisition [9], advanced PET data reconstruction methods are being developed. For example, iterative reconstruction methods such as OSEM have been widely adopted instead of the initially used filtered back projection, leading to an overall image improvement [10, 22]. Based on raw data sinograms, OSEM repeatedly iterates different possibilities in order to find the most probable image. Thereby, with each iteration step, an image with a greater likelihood of describing the measured data is achieved. The main disadvantage of OSEM, however, is the impossibility to run iterations to full convergence, because the image noise increases with each iteration, leading to rather unacceptable image quality before full convergence is reached [23, 24]. Therefore, OSEM is stopped after a predefined number of iterations, resulting in under-converged images. As a main consequence, this leads to an underestimation of the true SUV.

BSREM on the other hand, as a fast and globally convergent reconstruction algorithm, may increase the accuracy of lesion quantitation compared to OSEM, with a particular improvement in cold background regions such as the lungs as indicated in previous studies using NEMA and anthropomorphic phantom data [16]. Moreover, in a clinical setting, Teoh et al. showed that BSREM may significantly increase the SUV_max and increase signal-to-background/noise of lung lesions [25]. These observations are in line with the results of our study, e.g., a median increase of lung tumor SUV_max by 9.3% or 17.7% with BSREM₆₀₀ or BSREM₃₅₀, respectively, compared with OSEM_PSF, or by 18.5% or 28.0% with BSREM₆₀₀ or BSREM₃₅₀, respectively, compared with OSEM_TOF.

It is understood that increased quantitative accuracy in PET does not necessarily translate into an improvement of clinical readings. We therefore included in our study performance assessments of readers to complement the quantitative approach and enable a meaningful clinical implication. We could show that several aspects of reading lung cancer PET exams are enhanced with BSREM, such as lesion conspicuity and image sharpness. Indeed, in all 45 patients, a BSREM reconstruction was selected as preferred reconstruction for image assessment by the readers. An “intermediate” β-value (i.e., 450–600) seems to be ideal for lung cancer assessment and was selected in most cases. This is paralleled by the observation that by applying incrementally higher β-values, a steady increase of signal-to-noise ratio comes at the expense of reduced tumor signal-to-background ratio as a quantitative term but also at the expense of image sharpness as qualitative/subjective term.

As expected based on the objectives of BSREM, we observed a significant shift of the selected “preferred image reconstruction” towards higher β-values (i.e., from 450 to 600) in patients who received lower 18F-FDG doses (< 2 MBq/kg) compared with patients who received higher doses (> 2 MBq/kg). This observation reflects the apparent ability of BSREM to balance image quality and noise levels according to the injected dose and/or patient BMI by choosing different β-values, with higher β-values appearing more appropriate for patients with lower 18F-FDG doses. Hence, the appropriate selection of this relative difference penalty may become a valuable tool for adjusting PET image quality on a per-patient base, allowing both for a more patient-tailored PET imaging and for maintaining adequate image quality while reducing the dose. Notably, it is yet not known at which threshold particularly small lesions are lost with increasing β-values using BSREM reconstruction [25].

While limiting the 18F-FDG dose may not seem to be overly important in patients with lung cancer, dose reduction in PET in general is a worthwhile goal to achieve. This is particularly true in patients with diseases requiring repeat examinations such as lymphoma and especially for young patients who have a comparably high life expectancy. In this patient group, the imaging modality with the lowest achievable absorbed radiation dose per imaging study is desired. Future studies on, e.g., lymphoma patients may further refine protocols to let BMI-based dose adaption become reality also for this patient group, who are at particular stochastic risk for potential adverse radiation effects.

We acknowledge that our study has some limitations. First, a clinical reader assessment as the one we performed might carry an inherent bias since it is virtually impossible to totally blind readers to the image “appearance” of different reconstruction algorithms. Second, analyses of tumor SUV were restricted to measurement of SUV_max, which is the single most important PET parameter in clinical care. Further evaluation of corrected SUV would possibly alter the results. Third, we did not stratify our analyses by tumor size. Fourth, we used only a small range of possible β-values based on pretests. Fifth, the FDG dose regimen was based on BMI and body weight and—while having been specifically developed for digital detector PET—may differ from other protocols used on analog detector scanners in conjunction with BSREM. Sixth, the number of patients in this single-center study is comparably small, and therefore, conclusions drawn from the present analysis await further proof in larger (and ideally multi-centric) observations. Future studies are also warranted to assess the impact of BSREM on diagnosis, clinical management, and patient outcome.

In conclusion, BSREM reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor SUV_max, signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness.