Recently introduced long-axial field-of-view (LAFOV) PET/CT systems represent one of the most significant advancements in nuclear medicine since the advent of multi-modality PET/CT imaging. The higher sensitivity exhibited by such systems allow for reductions in applied activity and short duration scans. However, we consider this to be just one small part of the story: Instead, the ability to image the body in its entirety in a single FOV affords insights which standard FOV systems cannot provide. For example, we now have the ability to capture a wider dynamic range of a tracer by imaging it over multiple half-lives without detrimental image noise, to leverage lower radiopharmaceutical doses by using dual-tracer techniques and with improved quantification. The potential for quantitative dynamic whole-body imaging using abbreviated protocols potentially makes these techniques viable for routine clinical use, transforming PET-reporting from a subjective analysis of semi-quantitative maps of radiopharmaceutical uptake at a single time-point to an accurate and quantitative, non-invasive tool to determine human function and physiology and to explore organ interactions and to perform whole-body systems analysis. This article will share the insights obtained from 2 years of clinical operation of the first Biograph Vision Quadra (Siemens Healthineers) LAFOV system. It will also survey the current state-of-the-art in PET technology. Several technologies are poised to furnish systems with even greater sensitivity and resolution than current systems, potentially with orders of magnitude higher sensitivity. Current barriers which remain to be surmounted, such as data pipelines, patient throughput and the hindrances to implementing kinetic analysis for routine patient care will also be discussed.

OBJECTIVE: Long axial field-of-view (LAFOV) PET/CT showed improved performance resulting from higher sensitivity. The aim was to quantify the impact of using the full acceptance angle (UHS) in image reconstructions with the Biograph Vision Quadra LAFOV PET/CT (Siemens Healthineers) compared to the limited acceptance angle (high sensitivity mode, HS). METHODS: 38 oncological patients examined on a LAFOV Biograph Vision Quadra PET/CT were analysed. 15 patients underwent [18F]FDG-PET/CT, 15 patients underwent [18F]PSMA-1007 PET/CT, and 8 patients underwent [68Ga]Ga-DOTA-TOC PET/CT. Signal-to-noise ratio (SNR) and standardised uptake values (SUVmean/max/peak) were used to compare UHS and HS with different acquisition times. RESULTS: The SNR was significantly higher for UHS compared to HS over all acquisition times (SNR UHS/HS [18F]FDG: 1.35 ± 0.02, p < 0.001; [18F]PSMA-1007: 1.25 ± 0.02, p < 0.001; [68Ga]Ga-DOTA-TOC: 1.29 ± 0.02, p < 0.001). CONCLUSION: UHS showed significantly higher SNR opening the possibility of halving short acquisition times. This is of advantage in further reduction of whole-body PET/CT acquisition.

OBJECTIVES: The objective of this study was to evaluate the influence of a long-axial field-of-view (LAFOV) on stage migration using a large single-centre retrospective cohort in lymphoma and non-small cell lung cancer (NSCLC). METHODS: A retrospective study is performed for patients undergoing PET/computed tomography (CT) on either a short-axial field-of-view (SAFOV) or LAFOV PET/CT system for the staging of known or suspected NSCLC or for therapeutic response in lymphoma. The primary endpoint was the Deauville therapy response score for patients with lymphoma for the two systems. Secondary endpoints were the American Joint Committee on Cancer stage for NSCLC, the frequency of cN3 and cM1 findings, the probability for a positive nodal staging (cN1-3) for NSCLC and the diagnostic accuracy for nodal staging in NSCLC. RESULTS: One thousand two hundred eighteen records were screened and 597 patients were included for analysis ( N  = 367 for lymphoma and N  = 291 for NSCLC). For lymphoma, no significant differences were found in the proportion of patients with complete metabolic response versus non-complete metabolic response Deauville response scores ( P  = 0.66). For NSCLC no significant differences were observed between the two scanners for the frequency of cN3 and cM1 findings, for positive nodal staging, neither the sensitivity nor the specificity. CONCLUSIONS: In this study use of a LAFOV system was neither associated with upstaging in lymphoma nor NSCLC compared to a digital SAFOV system. Diagnostic accuracy was comparable between the two systems in NSCLC despite shorter acquisition times for LAFOV.

Purpose

High blood glucose (hBG) in patients undergoing [¹⁸F]FDG PET/CT scans often results in rescheduling the examination, which may lead to clinical delay for the patient and decrease productivity for the department. The aim of this study was to evaluate whether long-axial field-of-view (LAFOV) PET/CT can minimize the effect of altered bio-distribution in hBG patients and is able to provide diagnostic image quality in hBG situations.

Materials and methods

Oncologic patients with elevated blood glucose (≥ 8.0 mmol/l) and normal blood glucose (< 8.0 mmol/l, nBG) levels were matched for tumor entity, gender, age, and BMI. hBG patients were further subdivided into two groups (BG 8-11 mmol/l and BG > 11 mmol/l). Tracer uptake in the liver, muscle, and tumor was evaluated. Furthermore, image quality was compared between long acquisitions (ultra-high sensitivity mode, 360 s) on a LAFOV PET/CT and routine acquisitions equivalent to a short-axial field-of-view scanner (simulated (sSAFOV), obtained with high sensitivity mode, 120 s). Tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) were used as the main image quality criteria.

Results

Thirty-one hBG patients met the inclusion criteria and were matched with 31 nBG patients. Overall, liver uptake was significantly higher in hBG patients (SUV_mean, 3.07 ± 0.41 vs. 2.37 ± 0.33; p = 0.03), and brain uptake was significantly lower (SUV_max, 7.58 ± 0.74 vs. 13.38 ± 3.94; p < 0.001), whereas muscle (shoulder/gluteal) uptake showed no statistically significant difference. Tumor uptake was lower in hBG patients, resulting in a significantly lower TBR in the hBG cohort (3.48 ± 0.74 vs. 5.29 ± 1.48, p < 0.001). CNR was higher in nBG compared to hBG patients (12.17 ± 4.86 vs. 23.31 ± 12.22, p < 0.001). However, subgroup analysis of nBG 8-11 mmol/l on sSAFOV PET/CT compared to hBG (> 11 mmol/l) patients examined with LAFOV PET/CT showed no statistical significant difference in CNR (19.84 ± 8.40 vs. 17.79 ± 9.3, p = 0.08).

Conclusion

While elevated blood glucose (> 11 mmol) negatively affected TBR and CNR in our cohort, the images from a LAFOV PET-scanner had comparable CNR to PET-images acquired from nBG patients using sSAFOV PET/CT. Therefore, we argue that oncologic patients with increased blood sugar levels might be imaged safely with LAFOV PET/CT when rescheduling is not feasible.

PURPOSE: Long axial field-of-view (LAFOV) positron emission tomography (PET) systems allow to image all major organs with one bed position, which is particularly useful for acquiring whole-body dynamic data using short-lived radioisotopes like 82Rb. METHODS: We determined the absorbed dose in target organs of three subjects (29, 40, and 57 years old) using two different methods, i.e., MIRD and voxel dosimetry. The subjects were injected with 407.0 to 419.61 MBq of [82Rb]Cl and were scanned dynamically for 7 min with a LAFOV PET/CT scanner. RESULTS: Using the MIRD formalism and voxel dosimetry, the absorbed dose ranged from 1.84 to 2.78 μGy/MBq (1.57 to 3.92 μGy/MBq for voxel dosimetry) for the heart wall, 2.76 to 5.73 μGy/MBq (3.22 to 5.37 μGy/MBq for voxel dosimetry) for the kidneys, and 0.94 to 1.88 μGy/MBq (0.98 to 1.92 μGy/MBq for voxel dosimetry) for the lungs. The total body effective dose lied between 0.50 and 0.76 μSv/MBq. CONCLUSION: Our study suggests that the radiation dose associated with [82Rb]Cl PET/CT can be assessed by means of dynamic LAFOV PET and that it is lower compared to literature values.

PURPOSE: While acquisition of images in [68 Ga]Ga-PSMA-11 following longer uptake times can improve lesion uptake and contrast, resultant imaging quality and count statistics are limited by the isotopes half-life (68 min). Here, we present a series of cases demonstrating that when performed using a long axial field-of-view (LAFOV) PET/CT system, late imaging is feasible and can even provide improved image quality compared to regular acquisitions. METHODS: In this retrospective case series, we report our initial experiences with 10 patients who underwent standard imaging at 1 h p.i. following administration of 192 ± 36 MBq [68 Ga]Ga-PSMA-11 with additional late imaging performed at 4 h p.i. Images were acquired in a single bed position for 6 min at 1 h p.i. and 16 min p.i. at 4 h p.i. using a LAFOV scanner (106 cm axial FOV). Two experienced nuclear medicine physicians reviewed all scans in consensus and evaluated overall image quality (5-point Likert scale), lesion uptake in terms of standardised uptake values (SUV), tumour to background ratio (TBR) and target-lesion signal to background noise (SNR). RESULTS: Subjective image quality as rated on a 5-point Likert scale was only modestly lower for late acquisitions (4.2/5 at 4 h p.i.; 5/5 1 h p.i.), TBR was significantly improved (4 h: 3.41 vs 1 h: 1.93, p < 0.001) and SNR was improved with borderline significance (4 h: 33.02 vs 1 h: 24.80, p = 0.062) at later imaging. Images were obtained with total acquisition times comparable to routine examinations on standard axial FOV scanners. CONCLUSION: Late acquisition in tandem with a LAFOV PET/CT resulted in improvements in TBR and SNR and was associated with only modest impairment in subjective visual imaging quality. These data show that later acquisition times for [68 Ga]Ga-PSMA-11 may be preferable when performed on LAFOV systems.

BACKGROUND: Accurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling. METHODS: Dynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35-65, 40-65, 45-65, 50-65, and 55-65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed. RESULTS: There was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55-65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in Ki estimates in tumor lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 min of PET data as reference, Patlak Ki images generated using sPBIF55-65 with 20 min of PET data (t* = 45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB. CONCLUSION: We demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation.

Purpose

Inflamed, prone-to-rupture coronary plaques are an important cause of myocardial infarction and their early identification is crucial. Atherosclerotic plaques are characterized by overexpression of the type-2 somatostatin receptor (SST₂) in activated macrophages. SST₂ ligand imaging (e.g. with [⁶⁸ Ga]Ga-DOTA-TOC) has shown promise in detecting and quantifying the inflammatory activity within atherosclerotic plaques. However, the sensitivity of standard axial field of view (SAFOV) PET scanners may be suboptimal for imaging coronary arteries. Long-axial field of view (LAFOV) PET/CT scanners may help overcome this limitation. We aim to assess the ability of [⁶⁸ Ga]Ga-DOTA-TOC LAFOV-PET/CT in detecting calcified, SST₂ overexpressing coronary artery plaques.

Methods

In this retrospective study, 108 oncological patients underwent [⁶⁸ Ga]Ga-DOTA-TOC PET/CT on a LAFOV system. [⁶⁸ Ga]Ga-DOTA-TOC uptake and calcifications in the coronary arteries were evaluated visually and semi-quantitatively. Data on patients' cardiac risk factors and coronary artery calcium score were also collected. Patients were followed up for 21.5 ± 3.4 months.

Results

A total of 66 patients (61.1%) presented with calcified coronary artery plaques. Of these, 32 patients had increased [⁶⁸ Ga]Ga-DOTA-TOC uptake in at least one coronary vessel (TBR: 1.65 ± 0.53). Patients with single-vessel calcifications showed statistically significantly lower uptake (SUV_max 1.10 ± 0.28) compared to patients with two- (SUV_max 1.31 ± 0.29, p < 0.01) or three-vessel calcifications (SUV_max 1.24 ± 0.33, p < 0.01). There was a correlation between coronary artery calcium score (CACS) and [⁶⁸ Ga]Ga-DOTA-TOC uptake, especially in the LAD (p = 0.02). Stroke and all-cause death occurred more frequently in patients with increased [⁶⁸ Ga]Ga-DOTA-TOC uptake (15.63% vs. 0%; p:0.001 and 21.88% vs. 6.58%; p: 0.04, respectively) during the follow-up period.

Conclusion

[⁶⁸ Ga]Ga-DOTA-TOC as a marker for the macrophage activity can reveal unknown cases of inflamed calcified coronary artery plaques using a LAFOV PET system. [⁶⁸ Ga]Ga-DOTA-TOC uptake increased with the degree of calcification and correlated with higher risk of stroke and all-cause death. [⁶⁸ Ga]Ga-DOTA-TOC LAFOV PET/CT may be useful to assess patients' cardiovascular risk.

PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators. METHODS: Data from 18 subjects were used to train convolutional neural networks to enhance initial µ-maps generated using joint activity and attenuation reconstruction algorithm (MLACF) with transmission data from LSO background radiation acquired before and after the administration of 18F-fluorodeoxyglucose (18F-FDG) (µ-mapMLACF-PRE and µ-mapMLACF-POST respectively). The deep learning-enhanced µ-maps (µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST) were compared against MLACF-derived and CT-based maps (µ-mapCT). The performance of the method was also evaluated by assessing PET images reconstructed using each µ-map and computing volume-of-interest based standard uptake value measurements and percentage relative mean error (rME) and relative mean absolute error (rMAE) relative to CT-based method. RESULTS: No statistically significant difference was observed in rME values for µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST both in fat-based and water-based soft tissue as well as bones, suggesting that presence of the radiopharmaceutical activity in the body had negligible effects on the resulting µ-maps. The rMAE values µ-mapDL-MLACF-POST were reduced by a factor of 3.3 in average compared to the rMAE of µ-mapMLACF-POST. Similarly, the average rMAE values of PET images reconstructed using µ-mapDL-MLACF-POST (PETDL-MLACF-POST) were 2.6 times smaller than the average rMAE values of PET images reconstructed using µ-mapMLACF-POST. The mean absolute errors in SUV values of PETDL-MLACF-POST compared to PETCT were less than 5% in healthy organs, less than 7% in brain grey matter and 4.3% for all tumours combined. CONCLUSION: We describe a deep learning-based method to accurately generate µ-maps from PET emission data and LSO background radiation, enabling CT-free attenuation and scatter correction in LAFOV PET scanners.