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Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of healthcare. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a roadmap for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technological revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy and workflow efficiency, as well as emerging challenges and critical responsibilities are discussed. Establishing and maintaining leadership in AI requires a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, referring providers, among other stakeholders, while protecting our patients and society. This strategic plan is prepared by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging (SNMMI).

Previous studies have demonstrated the feasibility of absolute quantification of dynamic 123I-metaiodobenzylguanidine (123I-MIBG) SPECT imaging in humans. This work reports a simplified quantification method for dynamic 123I-MIBG SPECT using practical protocols with shortened acquisition time and voxel-by-voxel parametric imaging. Methods: Twelve healthy human volunteers underwent five 15-min dynamic SPECT scans at 0, 15, 90, 120, and 180 min after 123I-MIBG injection. List-mode SPECT data were binned into 29 frames and reconstructed with corrections for attenuation, scatter, and decay. Population-based blood-to-plasma correction and metabolite correction were applied to the image-derived input function. Likelihood estimation in graphical analysis (LEGA) was used as a simplified model to obtain volume of distribution (V T) values, which were compared with those obtained with the reversible 2-tissue (2T) compartment model. Three simplified protocols were evaluated with 2T and LEGA using a 30-min scan started simultaneously with tracer injection plus a 15-min scan at 90, 120, or 180 min after injection. Voxel-by-voxel LEGA fitting was applied to the aligned dynamic images using both the full protocol (five 15-min scans) and the simplified protocols. Results: Correlation analysis (y = 0.955x + 0.547, R 2 = 0.997) and Bland-Altman plot (mean difference, -0.8 mL/cm3; 95% limits of agreement, [-2.5, 1.0] mL/cm3; normal V T range, 29.0 ± 12.4 mL/cm3) showed that LEGA can be used as a simplified model of 2T for 123I-MIBG. High-quality V T parametric images could be obtained with LEGA. Region-of-interest (ROI) modeling and parametric imaging results were in excellent agreement as determined by correlation analysis (y = 0.999x - 1.026, R 2 = 0.982) and Bland-Altman plot (mean difference, -1.0 mL/cm3; 95% limits of agreement, [-4.2, 2.1] mL/cm3). V T correlated reasonably well between all simplified protocols and the full protocol with LEGA but not with 2T. The V T results were more reliable when there was a longer interval between the 2 acquisitions in the simplified protocols. Conclusion: For ROI-based kinetic modeling and parametric imaging, reliable quantification of dynamic 123I-MIBG SPECT can be achieved with LEGA using a simplified protocol of a 30-min scan starting with tracer injection plus a 15-min scan no earlier than 180 min after injection.

Respiratory motion degrades the detection and quantification capabilities of PET/CT imaging. Moreover, mismatch between a fast helical CT image and a time-averaged PET image due to respiratory motion results in additional attenuation correction artifacts and inaccurate localization. Current motion compensation approaches typically have 3 limitations: the mismatch among respiration-gated PET images and the CT attenuation correction (CTAC) map can introduce artifacts in the gated PET reconstructions that can subsequently affect the accuracy of the motion estimation; sinogram-based correction approaches do not correct for intragate motion due to intracycle and intercycle breathing variations; and the mismatch between the PET motion compensation reference gate and the CT image can cause an additional CT-mismatch artifact. In this study, we established a motion correction framework to address these limitations. Methods: In the proposed framework, the combined emission–transmission reconstruction algorithm was used for phase-matched gated PET reconstructions to facilitate the motion model building. An event-by-event nonrigid respiratory motion compensation method with correlations between internal organ motion and external respiratory signals was used to correct both intracycle and intercycle breathing variations. The PET reference gate was automatically determined by a newly proposed CT-matching algorithm. We applied the new framework to 13 human datasets with 3 different radiotracers and 323 lesions and compared its performance with CTAC and non–attenuation correction (NAC) approaches. Validation using 4-dimensional CT was performed for one lung cancer dataset. Results: For the 10 (18)F-FDG studies, the proposed method outperformed (P < 0.006) both the CTAC and the NAC methods in terms of region-of-interest–based SUV(mean), SUV(max), and SUV ratio improvements over no motion correction (SUV(mean): 19.9% vs. 14.0% vs. 13.2%; SUV(max): 15.5% vs. 10.8% vs. 10.6%; SUV ratio: 24.1% vs. 17.6% vs. 16.2%, for the proposed, CTAC, and NAC methods, respectively). The proposed method increased SUV ratios over no motion correction for 94.4% of lesions, compared with 84.8% and 86.4% using the CTAC and NAC methods, respectively. For the 2 (18)F-fluoropropyl-(+)-dihydrotetrabenazine studies, the proposed method reduced the CT-mismatch artifacts in the lower lung where the CTAC approach failed and maintained the quantification accuracy of bone marrow where the NAC approach failed. For the (18)F-FMISO study, the proposed method outperformed both the CTAC and the NAC methods in terms of motion estimation accuracy at 2 lung lesion locations. Conclusion: The proposed PET/CT respiratory event-by-event motion-correction framework with motion information derived from matched attenuation-corrected PET data provides image quality superior to that of the CTAC and NAC methods for multiple tracers.

Conventional 2-dimensional planar imaging of 123I-metaiodobenzylguanidine (123I-mIBG) is not fully quantitative. To develop a more accurate quantitative imaging approach, we investigated dynamic SPECT imaging with kinetic modeling in healthy humans to obtain the myocardial volume of distribution (VT) for 123I-mIBG. Methods: Twelve healthy humans underwent 5 serial 15-min SPECT scans at 0, 15, 90, 120, and 180 min after bolus injection of 123I-mIBG on a hybrid cadmium zinc telluride SPECT/CT system. Serial venous blood samples were obtained for radioactivity measurement and radiometabolite analysis. List-mode data of all the scans were binned into frames and reconstructed with attenuation and scatter corrections. Myocardial and blood-pool volumes of interest were drawn on the reconstructed images to derive the myocardial time–activity curve and input function. A population-based blood-to-plasma ratio (BPR) curve was generated. Both the population-based metabolite correction (PBMC) and the individual metabolite correction (IMC) curves were generated for comparison. VT values were obtained from different compartment models, using different input functions with and without metabolite and BPR corrections. Results: The BPR curve reached the peak value of 2.1 at 13 min after injection. Parent fraction was approximately 58% ± 13% at 15 min and stabilized at approximately 40% ± 5% by 180 min after injection. Two radiometabolite species were observed. When the reversible 2-tissue-compartment fit was used, the mean VT value was 29.0 ± 12.4 mL/cm3 with BPR correction and PBMC, a 188% ± 32% increase compared with that without corrections. There was significant difference in VT with BPR correction (P = 2.3e-04) as well as with PBMC (P = 1.6e-05). The mean difference in VT between PBMC and IMC was −3% ± 8%, which was insignificant (P = 0.39). The intersubject coefficients of variation after PBMC (43%) and IMC (42%) were similar. Conclusion: The myocardial VT of 123I-mIBG was established in healthy humans for the first time. Accurate kinetic modeling of 123I-mIBG requires both BPR and metabolite corrections. Population-based BPR correction and metabolite correction curves were developed, allowing more convenient absolute quantification of dynamic 123I-mIBG SPECT images.

Existing respiratory motion-correction methods are applied only to static PET imaging. We have previously developed an event-by-event respiratory motion-correction method with correlations between internal organ motion and external respiratory signals (INTEX). This method is uniquely appropriate for dynamic imaging because it corrects motion for each time point. In this study, we applied INTEX to human dynamic PET studies with various tracers and investigated the impact on kinetic parameter estimation. Methods: The use of 3 tracers—a myocardial perfusion tracer, 82Rb (n = 7); a pancreatic β-cell tracer, 18F-FP(+)DTBZ (n = 4); and a tumor hypoxia tracer, 18F-fluoromisonidazole (18F-FMISO) (n = 1)—was investigated in a study of 12 human subjects. Both rest and stress studies were performed for 82Rb. The Anzai belt system was used to record respiratory motion. Three-dimensional internal organ motion in high temporal resolution was calculated by INTEX to guide event-by-event respiratory motion correction of target organs in each dynamic frame. Time–activity curves of regions of interest drawn based on end-expiration PET images were obtained. For 82Rb studies, K1 was obtained with a 1-tissue model using a left-ventricle input function. Rest–stress myocardial blood flow (MBF) and coronary flow reserve (CFR) were determined. For 18F-FP(+)DTBZ studies, the total volume of distribution was estimated with arterial input functions using the multilinear analysis 1 method. For the 18F-FMISO study, the net uptake rate Ki was obtained with a 2-tissue irreversible model using a left-ventricle input function. All parameters were compared with the values derived without motion correction. Results: With INTEX, K1 and MBF increased by 10% ± 12% and 15% ± 19%, respectively, for 82Rb stress studies. CFR increased by 19% ± 21%. For studies with motion amplitudes greater than 8 mm (n = 3), K1, MBF, and CFR increased by 20% ± 12%, 30% ± 20%, and 34% ± 23%, respectively. For 82Rb rest studies, INTEX had minimal effect on parameter estimation. The total volume of distribution of 18F-FP(+)DTBZ and Ki of 18F-FMISO increased by 17% ± 6% and 20%, respectively. Conclusion: Respiratory motion can have a substantial impact on dynamic PET in the thorax and abdomen. The INTEX method using continuous external motion data substantially changed parameters in kinetic modeling. More accurate estimation is expected with INTEX.