Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging.

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.

[18F]PSMA-1007 PET is comparable to [99mTc]Tc-DMSA SPECT for renal cortical imaging.

BACKGROUND: Scintigraphy using technetium-99m labelled dimercaptosuccinic acid ([99mTc]Tc-DMSA), taken up in the proximal tubules, is the standard in functional imaging of the renal cortex. Recent guidelines recommend performing [99mTc]Tc-DMSA scintigraphy with single photon emission computed tomography (SPECT). Prostate-specific membrane antigen (PSMA) targeted positron emission tomography (PET) is used for staging and localization of recurrence in prostate cancer. A high renal uptake is often seen on PSMA PET, concordant with known PSMA expression in proximal tubules. This suggests PSMA PET could be used analogous to [99mTc]Tc-DMSA scintigraphy for renal cortical imaging. [18F]PSMA-1007 is a promising radiopharmaceutical for this purpose due to low urinary clearance. In this study, we aimed to compare [18F]PSMA-1007 PET to [99mTc]Tc-DMSA SPECT regarding split renal uptake and presence of renal uptake defects, in patients with prostate cancer. Three readers interpreted PET and SPECT images regarding presence of renal uptake defects, with each kidney split into cranial, mid and caudal segments. Kidneys were segmented in PET and SPECT images, and left renal uptake as a percentage of total renal uptake was measured. RESULTS: Twenty patients with prostate cancer were included. 2 participants had single kidneys; thus 38 kidneys were evaluated. A total of 29 defects were found on both [99mTc]Tc-DMSA SPECT and [18F]PSMA-1007 PET. Cohen's kappa for concordance regarding presence of any defect was 0.76 on a per-segment basis and 0.67 on a per-kidney basis. Spearman's r for left renal uptake percentage between [99mTc]Tc-DMSA SPECT and [18F]PSMA-1007 PET was 0.95. CONCLUSIONS: [18F]PSMA-1007 PET is comparable to [99mTc]Tc-DMSA SPECT for detection of uptake defects in this setting. Measurements of split renal function made using [18F]PSMA-1007 PET are valid and strongly correlated to measurements made with [99mTc]Tc-DMSA SPECT.

AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT.

BACKGROUND: Segmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [68Ga]Ga-DOTA-TOC/TATE PET/CT images. METHODS: A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model. RESULTS: There were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians. CONCLUSION: It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.

PET/CT imaging 2 h after injection of [18F]PSMA-1007 can lead to higher staging of prostate cancer than imaging after 1 h.

BACKGROUND: [18F]PSMA-1007 is a prostate specific membrane antigen (PSMA) ligand for positron emission tomography (PET) imaging of prostate cancer. Current guidelines recommend imaging 90-120 min after injection but strong data about optimal timing is lacking. Our aim was to study whether imaging after 1 h and 2 h leads to a different number of detected lesions, with a specific focus on lesions that might lead to a change in treatment. METHODS: 195 patients underwent PET with computed tomography imaging 1 and 2 h after injection of [18F]PSMA-1007. Three readers assessed the status of the prostate or prostate bed and suspected metastases. We analyzed the location and number of found metastases to determine N- and M-stage of patients. We also analyzed standardized uptake values (SUV) in lesions and in normal tissue. RESULTS: Significantly more pelvic lymph nodes and bone metastases were found and higher N- and M-stages were seen after 2 h. In twelve patients (6.1%) two or three readers agreed on a higher N- or M-stage after 2 h. Conversely, in two patients (1.0%), two readers agreed on a higher stage at 1 h. SUVs in suspected malignant lesions and in normal tissues were higher at 2 h, but lower in the blood pool and urinary bladder. CONCLUSIONS: Imaging at 2 h after injection of [18F]PSMA-1007 leads to more suspected metastases found than after 1 h, with higher staging in some patients and possible effect on patient treatment.

[18 F]PSMA-1007 renal uptake parameters: Reproducibility and relationship to estimated glomerular filtration rate.

BACKGROUND: Scintigraphy with technetium-99m-labelled dimercaptosuccinic acid ([99m Tc]Tc-DMSA) is widely used for renal cortical imaging. Uptake of [99m Tc]Tc-DMSA has been shown to correlate with glomerular filtration rate (GFR). Prostate-specific membrane antigen (PSMA) radiopharmaceuticals used for positron emission tomography (PET) show high renal uptake and are being investigated for renal imaging. [68 Ga]Ga-PSMA-11 PET parameters have been shown to correlate with estimated GFR (eGFR). The aim of this study was to investigate the relationship between renal [18 F]PSMA-1007 uptake and eGFR. METHODS: Hundred and eighty-five patients underwent PET imaging at 1 and 2 h after injection of 4.0 ± 0.2 MBq [18 F]PSMA-1007. Serum creatinine levels were measured and GFR estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) equations. Fifteen patients were excluded due to missing or incorrect data. Thus, data from 170 patients were analyzed. Kidneys were segmented in the PET images using a convolutional neural network with manual correction. For each kidney, mean standardized uptake value (SUVmean ) and segmentation volume in millilitres were measured. Linear regression analyses were performed with eGFR as the outcome variable. RESULTS: Variation in the eGFR values was explained to a significant degree by SUVmean and renal segmentation volume in both the 1 and 2 h images. This correlation was stronger for CKD-EPI eGFR (1 h R2 = 0.64; 2 h R2 = 0.64) than for MDRD eGFR (1 h R2 = 0.47; 2 h R2 = 0.45). CONCLUSION: Renal [18 F]PSMA-1007 uptake parameters correlate with eGFR and are indicative of renal cortical function.

Biokinetics and dosimetry of 18 F-PSMA-1007 in patients with prostate cancer.

PURPOSE: Positron emission tomography-computed tomography (PET-CT) using prostate-specific membrane antigen (PSMA) ligands is a method for imaging prostate cancer. A recent tracer, 18 F-PSMA-1007, offers advantages concerning production and biokinetics compared to the standard tracer (68 Ga-PSMA-11). Until now, radiation dosimetry data for this ligand was limited to the material of three healthy volunteers. The purpose of this study is to study the biokinetics and dosimetry of 18 F-PSMA-1007. METHODS: Twelve patients with prostate cancer were injected with 4 MBq/kg 18 F-PSMA-1007. Eight PET-CT scans with concomitant blood sampling were performed up to 330 min after injection. Urine was collected until the following morning. Volumes of interest for radiation-sensitive organs and organs with high uptake of 18 F-PSMA-1007 were drawn in the PET images. A biokinetic compartment model was developed using activity data from PET images and blood and urine samples. Time-activity curves and time-integrated activity coefficients for all delineated organs were calculated. The software IDAC-dose 2.1 was used to calculate the absorbed and effective doses. RESULTS: High concentrations of activity were noted in the liver, kidneys, parts of the small intestine, spleen, salivary glands, and lacrimal glands. The elimination through urine was 8% of injected activity in 20 h. The highest absorbed doses coefficients were in the lacrimal glands, kidneys, salivary glands, liver, and spleen (98-66 µGy/MBq). The effective dose coefficient was 25 µSv/MBq. CONCLUSION: The effective dose of 18 F-PSMA-1007 is 6.0-8.0 mSv for a typical patient weighing 80 kg injected with 3-4 MBq/kg.

A multicentre simulation study of planar whole-body bone scintigraphy in Sweden.

BACKGROUND: Whole-body bone scintigraphy is a clinically useful non-invasive and highly sensitive imaging method enabling detection of metabolic changes at an early stage of disease, often earlier than with conventional radiologic procedures. Bone scintigraphy is one of the most common nuclear medicine methods used worldwide. Therefore, it is important that the examination is implemented and performed in an optimal manner giving the patient added value in the subsequent care process. The aim of this national multicentre survey was to investigate Swedish nuclear medicine departments compliance with European practice guidelines for bone scintigraphy. In addition, the effect of image acquisition parameters on the ability to detect metabolic lesions was investigated. METHODS: Twenty-five hospital sites participated in the study. The SIMIND Monte Carlo (MC) simulation and the XCAT phantom were used to simulate ten fictive patient cases with increased metabolic activity distributed at ten different locations in the skeleton. The intensity of the metabolic activity was set into six different levels. Individual simulations were performed for each site, corresponding to their specific camera system and acquisition parameters. Simulated image data sets were then sent to each site and were visually evaluated in terms of if there was one or several locations with increased metabolic activity relative to normal activity. RESULT: There is a high compliance in Sweden with the EANM guidelines regarding image acquisition parameters for whole-body bone scintigraphy. However, up to 40% of the participating sites acquire lower count density in the images than recommended. Despite this, the image quality was adequate to maintain a stable detection level. None of the hospital sites or individual responders deviated according to the statistical analysis. There is a need for at least 2.5 times metabolic activity compared to normal for a lesion to be detected. CONCLUSION: The imaging process is well harmonized throughout the country and there is a high compliance with the EANM guidelines. There is a need for at least 2.5 times the normal metabolic activity for a lesion to be detected as abnormal.

Dose-reduced [18F]PSMA-1007 PET is feasible for functional imaging of the renal cortex.

BACKGROUND: In Prostate-specific membrane antigen (PSMA) positron emission tomography with computed tomography (PET-CT), there is significant renal uptake. The standard in renal cortical functional imaging is scintigraphy with technetium-99m labeled dimercaptosuccinic acid (DMSA). Using [68Ga]Ga-PSMA-11 PET for renal imaging has been suggested, but using [18F]PSMA-1007 has not been explored. The aims of this study were to establish the optimal time point for renal imaging after [18F]PSMA-1007 injection, to investigate the reproducibility of split renal uptake measurements, and to determine the margin for reduction in administered activity. METHODS: Twelve adult male patients with prostate cancer underwent [18F]PSMA-1007 PET-CT at 8 time points up to 5.5 h post-injection (p.i.). List-mode data were binned to durations of 10 to 120 s per bed position (bp). Left renal percentage of total renal uptake (LRU%) was measured, and the difference between highest and lowest measurement per patient ("delta max") was calculated. Images acquired at 1 h, 2 h, and 5.5 h p.i. with durations of 10 to 120 s/bp were rated regarding image quality. RESULTS: Imaging at 2 h p.i. with 60 s/bp yielded acceptable quality in all cases. Increasing acquisition time to 15 min for a single bp would allow reducing administered activity to 0.27 MBq/kg, resulting in an effective dose of 0.4 mSv for a 1-year old child weighing 10 kg. The median delta max of LRU% measurements was 2.7% (range 1.8-7.3%). CONCLUSIONS: Renal [18F]PSMA-1007 PET-CT is feasible, with imaging 2 h p.i., acceptable split renal uptake variability, and effective dose and acquisition time comparable to those of [99mTc]Tc-DMSA scintigraphy.

Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network.

BACKGROUND: The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images. METHODS: A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN. RESULTS: Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest. CONCLUSIONS: AI can enhance [18F]FDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUVmax/peak stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUVmax and SUVpeak fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.

Head-to-head comparison of a Si-photomultiplier-based and a conventional photomultiplier-based PET-CT system.

BACKGROUND: A novel generation of PET scanners based on silicon (Si)-photomultiplier (PM) technology has recently been introduced. Concurrently, there has been development of new reconstruction methods aimed at increasing the detectability of small lesions without increasing image noise. The combination of new detector technologies and new reconstruction algorithms has been found to increase image quality. However, it is unknown to what extent the demonstrated improvement of image quality is due to scanner hardware development or improved reconstruction algorithms. To isolate the contribution of the hardware, this study aimed to compare the ability to detect small hotspots in phantoms using the latest generation SiPM-based PET/CT scanner (GE Discovery MI) relative to conventional PM-based PET/CT scanner (GE Discovery 690), using identical reconstruction protocols. MATERIALS AND METHODS: Two different phantoms (NEMA body and Jasczcak) with fillable spheres (31 µl to 26.5 ml) and varying sphere-to-background-ratios (SBR) were scanned in one bed position for 15-600 s on both scanners. The data were reconstructed using identical reconstruction parameters on both scanners. The recovery-coefficient (RC), noise level, contrast (spherepeak/backgroundpeak-value), and detectability of each sphere were calculated and compared between the scanners at each acquisition time. RESULTS: The RC-curves for the NEMA phantom were near-identical for both scanners at SBR 10:1. For smaller spheres in the Jaszczak phantom, the contrast was 1.22 higher for the DMI scanner at SBR 15:1. The ratio decreased for lower SBR, with a ratio of 1.03 at SBR 3.85:1. Regarding the detectability of spheres, the sensitivity was 98% and 88% for the DMI and D690, respectively, for SBR 15:1. For SBR 7.5, the sensitivity was 75% and 83% for the DMI and D690, respectively. For SBR 3.85:1, the sensitivity was 43% and 30% for the DMI and D690, respectively. CONCLUSION: Marginally higher contrast in small spheres was seen for the SiPM-based scanner but there was no significant difference in detectability between the scanners. It was difficult to detect differences between the scanners, suggesting that the SiPM-based detectors are not the primary reason for improved image quality.

Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth.

BACKGROUND: It is time-consuming for oncologists to delineate volumes for radiotherapy treatment in computer tomography (CT) images. Automatic delineation based on image processing exists, but with varied accuracy and moderate time savings. Using convolutional neural network (CNN), delineations of volumes are faster and more accurate. We have used CTs with the annotated structure sets to train and evaluate a CNN. MATERIAL AND METHODS: The CNN is a standard segmentation network modified to minimize memory usage. We used CTs and structure sets from 75 cervical cancers and 191 anorectal cancers receiving radiation therapy at Skåne University Hospital 2014-2018. Five structures were investigated: left/right femoral heads, bladder, bowel bag, and clinical target volume of lymph nodes (CTVNs). Dice score and mean surface distance (MSD) (mm) evaluated accuracy, and one oncologist qualitatively evaluated auto-segmentations. RESULTS: Median Dice/MSD scores for anorectal cancer: 0.91-0.92/1.93-1.86 femoral heads, 0.94/2.07 bladder, and 0.83/6.80 bowel bag. Median Dice scores for cervical cancer were 0.93-0.94/1.42-1.49 femoral heads, 0.84/3.51 bladder, 0.88/5.80 bowel bag, and 0.82/3.89 CTVNs. With qualitative evaluation, performance on femoral heads and bladder auto-segmentations was mostly excellent, but CTVN auto-segmentations were not acceptable to a larger extent. DISCUSSION: It is possible to train a CNN with high overlap using structure sets as ground truth. Manually delineated pelvic volumes from structure sets do not always strictly follow volume boundaries and are sometimes inaccurately defined, which leads to similar inaccuracies in the CNN output. More data that is consistently annotated is needed to achieve higher CNN accuracy and to enable future clinical implementation.

Optimization of [18F]PSMA-1007 PET-CT using regularized reconstruction in patients with prostate cancer.

BACKGROUND: Prostate-specific membrane antigen (PSMA) radiotracers such as [18F]PSMA-1007 used with positron emission tomography-computed tomography (PET-CT) is promising for initial staging and detection of recurrent disease in prostate cancer patients. The block-sequential regularization expectation maximization algorithm (BSREM) is a new PET reconstruction algorithm, which provides higher image contrast while also reducing noise. The aim of the present study was to evaluate the influence of different acquisition times and different noise-suppressing factors in BSREM (ß values) in [18F]PSMA-1007 PET-CT regarding quantitative data as well as a visual image quality assessment. We included 35 patients referred for clinical [18F]PSMA-1007 PET-CT. Four megabecquerels per kilogramme were administered and imaging was performed after 120 min. Eighty-four image series per patient were created with combinations of acquisition times of 1-4 min/bed position and ß values of 300-1400. The noise level in normal tissue and the contrast-to-noise ratio (CNR) of pathological uptakes versus the local background were calculated. Image quality was assessed by experienced nuclear medicine physicians. RESULTS: The noise level in the liver, spleen, and muscle was higher for low ß values and low acquisition times (written as activity time products (ATs = administered activity × acquisition time)) and was minimized at maximum AT (16 MBq/kg min) and maximum ß (1400). There was only a small decrease above AT 10. The median CNR increased slowly with AT from approximately 6 to 12 and was substantially lower at AT 4 and higher at AT 14-16. At AT 4-6, many images were regarded as being of unacceptable quality. For AT 8, ß values of 700-900 were considered of acceptable quality. CONCLUSIONS: An AT of 8 (for example as in our study, 4 MB/kg with an acquisition time of 2 min) with a ß value of 700 performs well regarding noise level, CNR, and visual image quality assessment.

Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network: A Monte Carlo Simulation Approach.

Scintillation camera images contain a large amount of Poisson noise. We have investigated whether noise can be removed in whole-body bone scans using convolutional neural networks (CNNs) trained with sets of noisy and noiseless images obtained by Monte Carlo simulation. Methods: Three CNNs were generated using 3 different sets of training images: simulated bone scan images, images of a cylindric phantom with hot and cold spots, and a mix of the first two. Each training set consisted of 40,000 noiseless and noisy image pairs. The CNNs were evaluated with simulated images of a cylindric phantom and simulated bone scan images. The mean squared error between filtered and true images was used as difference metric, and the coefficient of variation was used to estimate noise reduction. The CNNs were compared with gaussian and median filters. A clinical evaluation was performed in which the ability to detect metastases for CNN- and gaussian-filtered bone scans with half the number of counts was compared with standard bone scans. Results: The best CNN reduced the coefficient of variation by, on average, 92%, and the best standard filter reduced the coefficient of variation by 88%. The best CNN gave a mean squared error that was on average 68% and 20% better than the best standard filters, for the cylindric and bone scan images, respectively. The best CNNs for the cylindric phantom and bone scans were the dedicated CNNs. No significant differences in the ability to detect metastases were found between standard, CNN-, and gaussian-filtered bone scans. Conclusion: Noise can be removed efficiently regardless of noise level with little or no resolution loss. The CNN filter enables reducing the scanning time by half and still obtaining good accuracy for bone metastasis assessment.

Evaluation of 18F-FDG uptake in lung parenchyma compensating for tissue fraction: Comparison between non-enhanced low dose CT and intravenous contrast-enhanced diagnostic CT.

AIM: To determine how the presence of intravenous (IV) contrast-enhanced CT influences SUV measurements corrected for both attenuation and tissue fraction. MATERIAL AND METHODS: Eighteen patients with different malignancies, free from lung disorders, lung cancer or metastasis, were prospectively recruited when referred for staging with combined 18F-FDG-PET/CT examination. A non-enhanced low-dose CT over the chest was immediately followed by a whole-body IV contrast-enhanced diagnostic CT and finally the PET acquisition. PET data were reconstructed with attenuation correction based on the two CT data sets. The lungs were segmented in the CT images and lung density was measured. Uptake of 18F-FDG in lung parenchyma was recorded using both non-enhanced and IV contrast-enhanced CT as well as with and without compensation for lung aeration. A comparison of SUV values of corrected and uncorrected PET images was performed. RESULTS: There was no significant difference between low dose PET/CT and IV contrast-enhanced PET/CT when removing the impact of air fraction (p = 0.093 for the right lung and p = 0.085 for the left lung). When tissue fraction was not corrected for, there was a significant difference between low dose PET/CT and IV contrast enhanced PET/CT used for attenuation correction (p = 0.006 for the right lung and p = 0.015 for the left lung). CONCLUSION: There was only a marginal effect on the assessement of SUV in the lung tissue when using IV contrast enhanced CT for attenuation correction when the air fraction was accounted for.

Comparison of conventional and Si-photomultiplier-based PET systems for image quality and diagnostic performance.

BACKGROUND: A new generation of positron emission tomography with computed tomography (PET-CT) was recently introduced using silicon (Si) photomultiplier (PM)-based technology. Our aim was to compare the image quality and diagnostic performance of a SiPM-based PET-CT (Discovery MI; GE Healthcare, Milwaukee, WI, USA) with a time-of-flight PET-CT scanner with a conventional PM detector (Gemini TF; Philips Healthcare, Cleveland, OH, USA), including reconstruction algorithms per vendor's recommendations. METHODS: Imaging of the National Electrical Manufacturers Association IEC body phantom and 16 patients was carried out using 1.5 min/bed for the Discovery MI PET-CT and 2 min/bed for the Gemini TF PET-CT. Images were analysed for recovery coefficients for the phantom, signal-to-noise ratio in the liver, standardized uptake values (SUV) in lesions, number of lesions and metabolic TNM classifications in patients. RESULTS: In phantom, the correct (> 90%) activity level was measured for spheres ≥17 mm for Discovery MI, whereas the Gemini TF reached a correct measured activity level for the 37-mm sphere. In patient studies, metabolic TNM classification was worse using images obtained from the Discovery MI compared those obtained from the Gemini TF in 4 of 15 patients. A trend toward more malignant, inflammatory and unclear lesions was found using images acquired with the Discovery MI compared with the Gemini TF, but this was not statistically significant. Lesion-to-blood-pool SUV ratios were significantly higher in images from the Discovery MI compared with the Gemini TF for lesions smaller than 1 cm (p < 0.001), but this was not the case for larger lesions (p = 0.053). The signal-to-noise ratio in the liver was similar between platforms (p = 0.52). Also, shorter acquisition times were possible using the Discovery MI, with preserved signal-to-noise ratio in the liver. CONCLUSIONS: Image quality was better with Discovery MI compared to conventional Gemini TF. Although no gold standard was available, the results indicate that the new PET-CT generation will provide potentially better diagnostic performance.

Measurement of airway inflammation in current smokers by positron emission tomography.

BACKGROUND: Accumulation of activated neutrophilic leucocytes is known to increase uptake of 18 F-fluorodeoxyglucose (18 F-FDG) into lung tissue. Available evidence suggests that smokers and subjects with chronic obstructive pulmonary disease (COPD) have neutrophilic inflammation in peripheral airways. The aim of this study was to examine whether current smokers have higher lung tissue uptake of 18 F-FDG than never-smokers when correcting for air fraction of the lungs. METHODS: We prospectively recruited 33 current smokers and 33 never-smokers among subjects referred for diagnosis or staging of cancer, other than lung cancer, with combined positron emission tomography/computed tomography (PET/CT) with 18 F-FDG. Subjects with focal 18 F-FDG uptake or focal CT abnormalities in the lungs were excluded. The lungs were segmented in the CT image, and lung density measured. 18 F-FDG uptake was measured in the corresponding volume and corrected for air fraction. RESULTS: Lung uptake of 18 F-FDG, corrected for air fraction, was 12·5 and 8 per cent higher in the right and left lungs, respectively, in current smokers than in never-smokers (P<0·05). Conclusion Abnormal lung tissue uptake of 18 F-FDG may be masked by reduced lung density if the uptake is not related to air fraction. Increased uptake of 18 F-FDG in lung tissue in current smokers relative to never-smokers may reflect inflammation in peripheral airways. Measurements of 18 F-FDG uptake in the lung tissue may be useful for animal and human studies of airways disease in COPD and the relation between airway and systemic inflammation.

Impact of acquisition time and penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm on a Si-photomultiplier-based PET-CT system for 18F-FDG.

BACKGROUND: Block-sequential regularized expectation maximization (BSREM), commercially Q. Clear (GE Healthcare, Milwaukee, WI, USA), is a reconstruction algorithm that allows for a fully convergent iterative reconstruction leading to higher image contrast compared to conventional reconstruction algorithms, while also limiting noise. The noise penalization factor ß controls the trade-off between noise level and resolution and can be adjusted by the user. The aim was to evaluate the influence of different ß values for different activity time products (ATs = administered activity × acquisition time) in whole-body 18F-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) regarding quantitative data, interpretation, and quality assessment of the images. Twenty-five patients with known or suspected malignancies, referred for clinical 18F-FDG PET-CT examinations acquired on a silicon photomultiplier PET-CT scanner, were included. The data were reconstructed using BSREM with ß values of 100-700 and ATs of 4-16 MBq/kg × min/bed (acquisition times of 1, 1.5, 2, 3, and 4 min/bed). Noise level, lesion SUVmax, and lesion SUVpeak were calculated. Image quality and lesion detectability were assessed by four nuclear medicine physicians for acquisition times of 1.0 and 1.5 min/bed position. RESULTS: The noise level decreased with increasing ß values and ATs. Lesion SUVmax varied considerably between different ß values and ATs, whereas SUVpeak was more stable. For an AT of 6 (in our case 1.5 min/bed), the best image quality was obtained with a ß of 600 and the best lesion detectability with a ß of 500. AT of 4 generated poor-quality images and false positive uptakes due to noise. CONCLUSIONS: For oncologic whole-body 18F-FDG examinations on a SiPM-based PET-CT, we propose using an AT of 6 (i.e., 4 MBq/kg and 1.5 min/bed) reconstructed with BSREM using a ß value of 500-600 in order to ensure image quality and lesion detection rate as well as a high patient throughput. We do not recommend using AT < 6 since the risk of false positive uptakes due to noise increases.

The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations.

BACKGROUND: The Deauville score (DS) is a clinical tool, based on the comparison between lesion and reference organ uptake of 18F-fluorodeoxyglucose (FDG), used to stratify patients with lymphoma into categories reflecting their disease status. With a plethora of positron emission tomography with computed tomography (PET-CT) hard- and software algorithms, standard uptake value (SUV) in lesions and reference organs may differ which affects DS classification and therefore medical treatment. The EANM Research Ltd. (EARL) harmonization program from the European Association of Nuclear Medicine (EANM) partly mitigates this issue, but local preferences are common in clinical practice. We have investigated the discordance in DS calculated from patients with lymphoma referred for 18F-FDG PET-CT reconstructed with three different algorithms: the newly introduced block-sequential regularization expectation-maximization algorithm commercially sold as Q. Clear (QC, GE Healthcare, Milwaukee, WI, USA), compliant with the newly proposed updated EARL recommendations, and two settings compliant with the current EARL recommendations (EARLlower and EARLupper, representing the lower and upper limit of the EARL recommendations). METHODS: Fifty-two patients with non-Hodgkin and Hodgkin lymphoma were included (18 females and 34 males). Segmentation of mediastinal blood pool and liver were semi-automatically performed, whereas segmentation of lesions was done manually. From these segmentations, SUVmax and SUVpeak were obtained and DS calculated. RESULTS: There was a significant difference in DS between the QC algorithm and EARLlower/EARLupper (p < 0.0001 for both) but not between EARLlower and EARLupper (p = 0.102) when SUVmax was used. For SUVpeak, there was a significant difference between QC and EARLlower (p = 0.001), but not for QC vs EARLupper (p = 0.071) or EARLlower vs EARLupper (p = 0.102). Five non-responders (DS 4-5) for QC were classified as responders (DS 1-3) when EARLlower/EARLupper was used, both when SUVmax and SUVpeak were investigated. CONCLUSION: Using the proposed updated EARL recommendations compared with the current recommendations will significantly change DS classification. In select cases, the discordance would affect the choice of medical treatment. Specifically, the current EARL recommendations were more often prone to classify patients as responders.

Impact of penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm for 18F-fluorocholine PET-CT regarding image quality and interpretation.

BACKGROUND: Recently, the block-sequential regularized expectation maximization (BSREM) reconstruction algorithm was commercially introduced (Q.Clear, GE Healthcare, Milwaukee, WI, USA). However, the combination of noise-penalizing factor (ß), acquisition time, and administered activity for optimal image quality has not been established for 18F-fluorocholine (FCH). The aim was to compare image quality and diagnostic performance of different reconstruction protocols for patients with prostate cancer being examined with 18F-FCH on a silicon photomultiplier-based PET-CT. Thirteen patients were included, injected with 4 MBq/kg, and images were acquired after 1 h. Images were reconstructed with frame durations of 1.0, 1.5, and 2.0 min using ß of 150, 200, 300, 400, 500, and 550. An ordered subset expectation maximization (OSEM) reconstruction with a frame duration of 2.0 min was used for comparison. Images were quantitatively analyzed regarding standardized uptake values (SUV) in metastatic lymph nodes, local background, and muscle to obtain contrast-to-noise ratios (CNR) as well as the noise level in muscle. Images were analyzed regarding image quality and number of metastatic lymph nodes by two nuclear medicine physicians. RESULTS: The highest median CNR was found for BSREM with a ß of 300 and a frame duration of 2.0 min. The OSEM reconstruction had the lowest median CNR. Both the noise level and lesion SUVmax decreased with increasing ß. For a frame duration of 1.5 min, the median quality score was highest for ß 400-500, and for a frame duration of 2.0 min the score was highest for ß 300-500. There was no statistically significant difference in the number of suspected lymph node metastases between the different image series for one of the physicians, and for the other physician the number of lymph nodes differed only for one combination of image series. CONCLUSIONS: To achieve acceptable image quality at 4 MBq/kg 18F-FCH, we propose using a ß of 400-550 with a frame duration of 1.5 min. The lower ß should be used if a high CNR is desired and the higher if a low noise level is important.

Evaluation of changes in Bone Scan Index at different acquisition time-points in bone scintigraphy.

Bone Scan Index (BSI) is a validated imaging biomarker to objectively assess tumour burden in bone in patients with prostate cancer, and can be used to monitor treatment response. It is not known if BSI is significantly altered when images are acquired at a time difference of 1 h. The aim of this study was to investigate if automatic calculation of BSI is affected when images are acquired 1 hour apart, after approximately 3 and 4 h. We prospectively studied patients with prostate cancer who were referred for bone scintigraphy according to clinical routine. The patients performed a whole-body bone scan at approximately 3 h after injection of radiolabelled bisphosphonate and a second 1 h after the first. BSI values for each bone scintigraphy were obtained using EXINI boneBSI software. A total of 25 patients were included. Median BSI for the first acquisition was 0·05 (range 0-11·93) and for the second acquisition 0·21 (range 0-13·06). There was a statistically significant increase in BSI at the second image acquisition compared to the first (P<0·001). In seven of 25 patients (28%) and in seven of 13 patients with BSI > 0 (54%), a clinically significant increase (>0·3) was observed. The time between injection and scanning should be fixed when changes in BSI are important, for example when monitoring therapeutic efficacy.