We designed and evaluated a novel albuminbinder-conjugated 177 Lu-PSMA-617 derivative, 177 Lu-HTK01169, with an extended blood retention time to maximize the radiation dose delivered to prostate tumors expressing prostate-specific membrane antigen (PSMA). PSMA-617 and HTK01169 that contained N-[4-(p-iodophenyl)butanoyl]-Glu as an albuminbinding motif were synthesized using the solid-phase approach. Binding affinity to PSMA was determined by in vitro competition-binding assay. 177 Lu labeling was performed in acetate buffer (pH 4.5) at 90 °C for 15 min. SPECT/CT imaging, biodistribution, and endoradiotherapy studies were conducted in mice bearing PSMA-expressing LNCaP tumor xenografts. Radiation dosimetry was calculated using OLINDA software. Lu-PSMA-617 and Lu-HTK01169-bound PSMA with high affinity (K i values = 0.24 and 0.04 nM, respectively). SPECT imaging and biodistribution studies showed that 177 Lu-PSMA-617 and 177 Lu-HTK01169 were excreted mainly via the renal pathway. With fast blood clearance (0.68%ID/g at 1 h postinjection), the tumor uptake of 177 Lu-PSMA-617 peaked at 1 h postinjection (15.1%ID/g) and gradually decreased to 7.91%ID/g at 120 h postinjection. With extended blood retention (16.6 and 2.10%ID/g at 1 and 24 h, respectively), the tumor uptake of 177 Lu-HTK01169 peaked at 24 h postinjection (55.9%ID/g) and remained at the same level by the end of the study (120 h). Based on dosimetry calculations, 177 Lu-HTK01169 delivered an 8.3-fold higher radiation dose than 177 Lu-PSMA-617 to LNCaP tumor xenografts. For the endoradiotherapy study, the mice treated with 177 Lu-PSMA-617 (18.5 MBq) all reached humane end point (tumor volume >1000 mm 3 ) by Day 73 with a median survival of 58 days. Mice treated with 18.5, 9.3, 4.6, or 2.3 MBq of 177 Lu-HTK01169 had a median survival of >120, 103, 61, and 28 days, respectively. With greatly enhanced tumor uptake and treatment efficacy compared to 177 Lu-PSMA-617 in preclinical studies, 177 Lu-HTK01169 warrants further investigation for endoradiotherapy of prostate cancer.

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BackgroundThe aim of the study is to assess accuracy of activity quantification of 177Lu studies performed according to recommendations provided by the committee on Medical Internal Radiation Dose (MIRD) pamphlets 23 and 26. The performances of two scatter correction and three segmentation methods were compared. Additionally, the accuracy of tomographic and planar methods for determination of the camera normalization factor (CNF) was evaluated.Eight phantoms containing inserts of different sizes and shapes placed in air, water, and radioactive background were scanned using a Siemens SymbiaT SPECT/CT camera. Planar and tomographic scans with 177Lu sources were used to measure CNF. Images were reconstructed with our SPEQToR software using resolution recovery, attenuation, and two scatter correction methods (analytical photon distribution interpolated (APDI) and triple energy window (TEW)). Segmentation was performed using a fixed threshold method for both air and cold water scans. For hot water experiments three segmentation methods were compared as folows: a 40% fixed threshold, segmentation based on CT images, and our iterative adaptive dual thresholding (IADT). Quantification error, defined as the percent difference between experimental and true activities, was evaluated.ResultsQuantification error for scans in air was better for TEW scatter correction (<6%) than for APDI (<11%). This trend was reversed for scans in water (<10% for APDI and <14% for TEW). For hot water, the best results (<18% for small objects and <5% for objects >100 ml) were obtained when APDI and IADT were used for scatter correction and segmentation, respectively. Additionally, we showed that planar acquisitions with scatter correction and tomographic scans provide similar CNF values. This is an important finding because planar acquisitions are easier to perform than tomographic scans. TEW and APDI resulted in similar quantification errors with APDI showing a small advantage for objects placed in medium with non-uniform density.ConclusionsFollowing the MIRD recommendations for data acquisition and reconstruction resulted in accurate activity quantification (errors <5% for large objects). However, techniques for better organ/tumor segmentation must still be developed.Electronic supplementary materialThe online version of this article (doi:10.1186/s40658-016-0170-3) contains supplementary material, which is available to authorized users.

BackgroundCamera calibration, which translates reconstructed count map into absolute activity map, is a prerequisite procedure for quantitative SPECT imaging. Both planar and tomographic scans using different phantom geometries have been proposed for the determination of the camera calibration factor (CF). However, there is no consensus on which approach is the best. The aim of this study is to evaluate all these calibration methods, compare their performance, and propose a practical and accurate calibration method for SPECT quantitation of therapeutic radioisotopes. Twenty-one phantom experiments (Siemens Symbia SPECT/CT) and 12 Monte Carlo simulations (GATE v6.1) using three therapy isotopes (131I, 177Lu, and 188Re) have been performed. The following phantom geometries were used: (1) planar scans of point source in air (PS), (2) tomographic scans of insert(s) filled with activity placed in non-radioactive water (HS + CB), (3) tomographic scans of hot insert(s) in radioactive water (HS + WB), and (4) tomographic scans of cylinders uniformly filled with activity (HC). Tomographic data were reconstructed using OSEM with CT-based attenuation correction and triple energy window (TEW) scatter correction, and CF was determined using total counts in the reconstructed image, while for planar scans, the photopeak counts, corrected for scatter and background with TEW, were used. Additionally, for simulated data, CF obtained from primary photons only was analyzed.ResultsFor phantom experiments, CF obtained from PS and HS + WB agreed to within 6% (below 3% if experiments performed on the same day are considered). However, CF from HS + CB exceeded those from PS by 4–12%. Similar trend was found in simulation studies. Analysis of CFs from primary photons helped us to understand this discrepancy. It was due to underestimation of scatter by the TEW method, further enhanced by attenuation correction. This effect becomes less important when the source is distributed over the entire phantom volume (HS + WB and HC).ConclusionsCamera CF could be determined using planar scans of a point source, provided that the scatter and background contributions are removed, for example using the clinically available TEW method. This approach is simple and yet provides CF with sufficient accuracy (~ 5%) to be used in clinics for radiotracer quantification.

The gastrin-releasing peptide receptor (GRPR), a G protein-coupled receptor, is overexpressed in solid malignancies and particularly in prostate cancer. We synthesized a novel bombesin derivative, [68Ga]Ga-ProBOMB1, evaluated its pharmacokinetics and potential to image GRPR expression with positron emission tomography (PET), and compared it with [68Ga]Ga-NeoBOMB1. ProBOMB1 (DOTA-pABzA-DIG-d-Phe-Gln-Trp-Ala-Val-Gly-His-Leu-ψ(CH2N)-Pro-NH2) was synthesized by solid-phase peptide synthesis. The polyaminocarboxylate chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) was coupled to the N-terminal and separated from the GRPR-targeting sequence by a p-aminomethylaniline-diglycolic acid (pABzA-DIG) linker. The binding affinity to GRPR was determined using a cell-based competition assay, whereas the agonist/antagonist property was determined with a calcium efflux assay. ProBOMB1 was radiolabeled with 68GaCl3. PET imaging and biodistribution studies were performed in male immunocompromised mice bearing PC-3 prostate cancer xenografts. Blocking experiments were performed with coinjection of [d-Phe6,Leu-NHEt13,des-Met14]bombesin(6-14). Dosimetry calculations were performed with OLINDA software. ProBOMB1 and the nonradioactive Ga-ProBOMB were obtained in 1.1 and 67% yield, respectively. The Ki value of Ga-ProBOMB1 for GRPR was 3.97 ± 0.76 nM. Ga-ProBOMB1 behaved as an antagonist for GRPR. [68Ga]Ga-ProBOMB1 was obtained in 48.2 ± 10.9% decay-corrected radiochemical yield with 121 ± 46.9 GBq/μmol molar activity and >95% radiochemical purity. Imaging/biodistribution studies showed that the excretion of [68Ga]Ga-ProBOMB1 was primarily through the renal pathway. At 1 h postinjection (p.i.), PC-3 tumor xenografts were clearly delineated in PET images with excellent contrast. The tumor uptake for [68Ga]Ga-ProBOMB1 was 8.17 ± 2.57 percent injected dose per gram (% ID/g) and 9.83 ± 1.48% ID/g for [68Ga]Ga-NeoBOMB1, based on biodistribution studies at 1 h p.i. This corresponded to tumor-to-blood and tumor-to-muscle uptake ratios of 20.6 ± 6.79 and 106 ± 57.7 for [68Ga]Ga-ProBOMB1 and 8.38 ± 0.78 and 39.0 ± 12.6 for [68Ga]Ga-NeoBOMB1, respectively. Blockade with [d-Phe6,Leu-NHEt13,des-Met14]bombesin(6-14) significantly reduced the average uptake of [68Ga]Ga-ProBOMB1 in tumors by 62%. The total absorbed dose was lower for [68Ga]Ga-ProBOMB1 in all organs except for bladder compared with [68Ga]Ga-NeoBOMB1. Our data suggest that [68Ga]Ga-ProBOMB1 is an excellent radiotracer for imaging GRPR expression with PET. [68Ga]Ga-ProBOMB1 achieved a similar uptake as [68Ga]Ga-NeoBOMB1 in tumors, with enhanced contrast and lower whole-body absorbed dose.

Learning Objectives: On successful completion of this activity, participants should be able to (1) provide an introduction to machine learning, neural networks, and deep learning; (2) discuss common machine learning algorithms with illustrative examples and figures; and (3) compare machine learning algorithms and provide guidance on selection for a given application. Financial Disclosure: Sandra E. Black received in-kind funding to her institution from GE Healthcare and Avid Pharmaceuticals. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest. CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through April 2022. This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when they can be helpful in nuclear medicine. Part 2 focuses on current contributions of ML to our field, addresses future expectations and limitations, and provides a critical appraisal of what ML can and cannot do.

Due to challenges in performing routine personalized dosimetry in radiopharmaceutical therapies, interest in single-time-point (STP) dosimetry, particularly utilizing only one SPECT scan, is on the rise.Meanwhile, there are questions about reliability of STP dosimetry, with limited independent validations.In the present work, we analyze two STP dosimetry methods and evaluate dose errors for a number of radiopharmaceuticals based on effective half-life distributions. Method:We first challenged the common assumption that radiopharmaceutical effective half-lives across the population are Gaussian (normal) distributed. Then, dose accuracy was estimated based on two STP dosimetry methods for a wide range of potential scan-times post-injection (p.i.), for different radiopharmaceuticals applied to neuroendocrine tumors and prostate cancer. The accuracy and limitations of using each of the STP methods were discussed.Results: Log-normal distribution was shown as more appropriate to capture effective half-life distributions. The STP framework was shown as promising for dosimetry of 177 Lu-DOTATATE, and for kidney dosimetry of different radiopharmaceuticals (errors<30%). Meanwhile, for some radiopharmaceuticals, STP accuracy is compromised (e.g. in bone marrow and tumors for 177 Lu-PSMA therapies). Optimal SPECT scanning time for 177 Lu-DOTATATE is at ~72 h p.i., while 48 h p.i. would be better for 177 Lu-PSMA compounds. Conclusion:Our results demonstrate that simplified STP dosimetry methods may compromise the accuracy of dose estimates, with some exceptions such as for 177 Lu-DOTATATE and for kidney dosimetry in different radiopharmaceuticals. Simplified personalized dosimetry in the clinic continues to be a challenging task. Based on these results, we make suggestions and recommendations for improved personalized dosimetry using simplified imaging schemes. * The data of effective half-lives were published in the format of median and range only. For Studies 3 and 9, their corresponding values of mean and SD were then calculated based on method by Hozo et al 2005 (19). For Study 4, we had access to complete listing of effective half-lives. † The 95% CI of range was estimated assuming log-normal statistics, as described in the text. ‡ T eff of each individual ROI (organ or lesion) was available (i.e. complete listing of effective half-lives for all patients). § This overall dataset (29 patients) consisted primarily of 177 Lu-DOTATATE (22 patients) but also included some 177 Lu-DOTATOC ( 7patients).

Background: Personalization of 177 Lu-based radionuclide therapy requires implementation of dosimetry methods that are both accurate and practical enough for routine clinical use. Quantitative single-photon emission computed tomography/ computed tomography (QSPECT/CT) is the preferred scanning modality to achieve this and necessitates characterizing the response of the camera, and calibrating it, over the full range of therapeutic activities and system capacity. Various methods to determine the camera calibration factor (CF) and the deadtime constant (τ) were investigated, with the aim to design a simple and robust protocol for quantitative 177 Lu imaging. Methods: The SPECT/CT camera was equipped with a medium energy collimator. Multiple phantoms were used to reproduce various attenuation conditions: rod sources in air or water-equivalent media, as well as a Jaszczak phantom with inserts. Planar and tomographic images of a wide range of activities were acquired, with multiple energy windows for scatter correction (double or triple energy window technique) as well as count rate monitoring over a large spectrum of energy. Dead time was modelled using the paralysable model. CF and τ were deduced by curve fitting either separately in two steps (CF determined first using a subset of low-activity acquisitions, then τ determined using the full range of activity) or at once (both CF and τ determined using the full range of activity). Total or segmented activity in the SPECT field of view was computed. Finally, these methods were compared in terms of accuracy to recover the known activity, in particular when planar-derived parameters were applied to the SPECT data. Results: The SPECT camera was shown to operate as expected on a finite count rate range (up to~350 kcps over the entire energy spectrum). CF and τ from planar (sources in air) and SPECT segmented Jaszczak data yielded a very good agreement (CF < 1% and τ < 3%). Determining CF and τ from a single curve fit made deadtime-corrected images less prone to overestimating recovered activity. Using tripleenergy window scatter correction while acquiring one or more additional energy window(s) to enable wide-spectrum count rate monitoring (i.e. ranging 55-250 or 18-680 keV) yielded the most consistent results across the various geometries. The final, planar-derived calibration parameters for our system were a CF of 9.36 ± 0.01 cps/MBq and a τ of 0.550 ± 0.003 μs. Using the latter, the activity in a Jaszczak phantom could be quantified by QSPECT with an accuracy of 0.02 ± 1.10%.