Positron Emission Tomography (PET) in combination with X-ray computed tomography (CT) is a widely accepted imaging tool in clinical practice for accurate diagnosis and clinical staging of a variety of diseases1. The advantage of PET imaging is the ability to visualize and quantify a myriad of biological processes in vivo with high sensitivity and accuracy2. This is achieved through intravenously administering a radioactively labelled compound, also known as a radiotracer, to the patient. Depending on the radiotracer being used, tissue characteristics such as glucose metabolism, cellular proliferation, degree of hypoxia, amino acid transport, and expression of proteins and receptors, can be visualized and quantified2.
Although several radiotracers have been developed, validated, and used in clinical practice, the radioactive glucose analogue 18F-fluorodeoxyglucose (FDG) is the most widely used radiotracer in clinical practice. Given that FDG predominantly accumulates in cells with an elevated glycolytic rate (i.e., cells with elevated glucose uptake and conversion to pyruvate for energy production), it is possible to discriminate tissues with different metabolic states. Similar to glucose, the first step of FDG uptake is transport from the extra-cellular space over the plasma membrane to the intra-cellular space, which is facilitated by glucose transporters (GLUT)3. Once the FDG is in the intra-cellular space, phosphorylation by hexokinases will result in the generation of FDG-6-phosphate. However, in contrast to glucose-6-phosphate, FDG-6-phosphate cannot enter the Krebs cycle for further aerobic dissimilation due to the absence of a hydroxyl (OH) group at the second (2’) carbon position. Given that the reverse reaction, the dephosphorylation of FDG-6-phosphate back to FDG, hardly occurs in most tissues, the FDG-6-phosphate is trapped intracellularly3. Therefore, the degree of FDG uptake is dependent on the expression of the GLUT (in particular GLUT1 and GLUT3) on the plasma membrane, and the intracellular enzymatic activity of hexokinases. The concept of this continuous uptake and trapping of FDG is referred to as metabolic trapping. The fact that FDG preferentially accumulates in tissues with an elevated metabolic activity is shown in Figure 1a, demonstrating the physiological distribution of FDG in a patient. This FDG-PET image shows higher uptake in heart, brain, and liver tissues, which are known to be metabolically active organs under normal conditions.
The high sensitivity for detecting differences in the metabolic state of tissues makes FDG an excellent radiotracer for discriminating normal from diseased tissues, given that an altered metabolism is an important hallmark for many diseases. This is readily depicted in Figure 1b, showing an FDG-PET image of a patient with stage IV non-small cell lung cancer (NSCLC). There is increased uptake in the primary tumor as well as in metastatic lesions. In addition to visualization, quantification of radiotracer uptake plays an important role in clinical management of patients. Quantitative indices derived from PET images reflecting the degree of radiotracer uptake, such as the standardized uptake value (SUV), metabolic volumes, and total lesion glycolysis (TLG), can be used to provide important prognostic information and measure treatment response for different patient groups4,5,6. In this regard, FDG-PET imaging is increasingly being used to personalize radiotherapy and systemic treatment in oncology patients7. Furthermore, the use of FDG-PET for monitoring acute treatment induced toxicity, such as radiation induced esophagitis8, pneumonitis9 and systemic inflammatory responses10, has been described and provides important information for making image-guided treatment decisions.
Given the important role of PET for clinical management of patients, image quality and quantitative accuracy is important for appropriately guiding treatment decisions based on PET images. However, there are numerous technical factors that can compromise quantitative accuracy of PET images11. An important factor that can significantly influence image quantification in PET is related to the longer acquisition times of PET compared to other radiological imaging modalities, typically several minutes per bed position. As a consequence, patients are usually instructed to breath freely during PET imaging. The result is that PET images suffer from respiratory induced motion, which can lead to significant blurring of organs located within the thorax and upper abdomen. This respiratory-induced motion blurring can significantly impair adequate visualization and quantitative accuracy of radiotracer uptake, which can affect clinical management of patients when using PET images for diagnosis and staging, target volume definition for radiation treatment planning applications, and monitoring of therapy response12.
Several respiratory gating methods have been developed in an attempt to correct PET images for respiratory motion artefacts13. These methods can be categorized into prospective, retrospective, and data-driven gating strategies. Prospective and retrospective respiratory gating techniques typically rely on the acquisition of a respiratory surrogate signal during PET imaging14. These respiratory surrogate signals are used to track and monitor the patient’s respiratory cycle. Examples of respiratory tracking devices are detection of chest wall excursion using pressure sensors12 or optical tracking systems (e.g., video cameras)15, thermocouples to measure the temperature of breathed air16, and spirometers to measure airflow and thereby indirectly estimating volume changes in the patient’s lungs17.
Respiratory gating is then typically accomplished by continuously and simultaneously recording a surrogate signal (designated S(t)), with the PET data during image acquisition. Using the surrogate signal acquired, PET data corresponding to a particular respiratory phase or amplitude range (amplitude-based gating) can be selected12,13,18. Phase-based gating is performed by dividing each respiratory cycle into a fixed number of gates, as depicted in Figure 2a. Respiratory gating is then performed by selecting data acquired at a particular phase during the patient’s respiratory cycle to be used for image reconstruction. Similarly, amplitude-based gating relies on defining an amplitude range of the respiratory signal, as shown in Figure 2b. When the value of the respiratory signal falls within the set amplitude range, the corresponding PET listmode data will be used for image reconstruction. For retrospective gating approaches, all data is collected and re-binning of the PET data is performed after image acquisition. Although prospective respiratory gating methods use the same concepts as retrospective gating approaches for re-binning of PET data, these methods rely on collecting data prospectively during image acquisition. When a sufficient amount of PET data is collected, image acquisition will be finalized. The difficulty of such prospective and retrospective gating approaches is maintaining acceptable image quality without significantly prolonging image acquisition times when irregular breathing occurs13. In this regard, phase-based respiratory gating methods are particularly sensitive to irregular breathing patterns13,19, where significant amounts of PET data can be discarded due to rejection of inappropriate triggers, resulting in considerable reduction of image quality or unacceptable lengthening of image acquisition time. Additionally, when inappropriate triggers are accepted, the performance of the respiratory gating algorithm and thereby the effectiveness of motion rejection from the PET images can be reduced due to the fact that respiratory gates are defined at different phases of the respiratory cycle, as depicted in Figure 2a. Indeed, it has been reported that amplitude-based respiratory gating is more stable than phase-based approaches in case of irregularities in the respiratory signal13. Though amplitude-based respiratory gating algorithms are more robust in the presence of irregular breathing frequencies, these algorithms are more sensitive to baseline drifting of the respiratory signal. Drifting of the baseline signal can occur due to numerous reasons when the patient’s muscle tension (i.e., transition of a patient into a more relaxed state during image acquisition) or breathing pattern changes. In order to prevent such baseline drifting of the signal, care should be taken to securely attach tracking sensors to the patient and perform regular monitoring of the respiratory signal.
Although these problems are known, traditional respiratory gating algorithms only allow limited control over image quality and usually require significant lengthening of image acquisition time or increased amounts of radiotracer to be administered to the patient. These factors resulted in limited adoption of such protocols in clinical routine. In order to circumvent these problems related to the variable quality of the respiratory gated images , a specific type of amplitude-based gating algorithm, also known as optimal respiratory gating (ORG), has been proposed18. Respiratory gating with ORG permits the user to specify image quality of the respiratory gated images by providing a duty cycle as input to the algorithm. The duty cycle is defined as a percentage of the acquired PET list-mode data that is used for image reconstruction. In contrast to many other respiratory gating algorithms, this concept permits the user to directly determine image quality of the reconstructed PET images. Based on the duty cycle specified, an optimal amplitude range is calculated, which takes the specific characteristics of the entire respiratory surrogate signal into account18. The optimal amplitude range for a specific duty cycle will be calculated by starting with a selection of different values for the lower amplitude limit, designated (L), of the respiratory signal. For each selected lower limit, the upper amplitude limit, designated (U), is adjusted in such a way that the sum of the selected PET data, defined as data acquired when the respiratory signal falls within the amplitude range (L<S(t)<U), is equal to the specified duty cycle. For example, for a duty cycle of 50% and six minutes of acquired PET listmode data, the amplitude range is adapted to include three minutes (50%) of PET data. The optimal amplitude range (W) is defined as the smallest amplitude range used for respiratory gating that still contains the required amount of PET data (i.e., ArgMax([U-L])), as depicted in Figure 2c12. Thus, by specifying the duty cycle, the user makes a trade-off between the amount of noise and the degree of residual motion residing in the ORG PET images. Lowering the duty cycle will increase the amount of noise, though this will also reduce the amount of residual motion in the PET images (and vice versa). Although the concepts and effects of ORG have been described in previous reports, the purpose of this manuscript is to provide clinicians with details on the specific protocols when using ORG in clinical practice. Therefore, the use of ORG in a clinical imaging protocol is described. Several practical aspects, including patient preparation, image acquisition and reconstruction protocols will be provided. Furthermore, the manuscript will cover the user interface of the ORG software and specific choices that can be made when performing respiratory gating during PET imaging. Lastly, the effect of ORG on lesion detectability and image quantification, as shown in previous studies, are discussed.