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Background: Accurate attenuation correction (AC) is vital in nuclear medicine, particularly for quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. This study aimed to establish a CT-free quantification technology in kidney SPECT imaging using deep learning to generate synthetic attenuation maps (μ-maps) from SPECT data, thereby reducing radiation exposure and eliminating the need for CT scans. Results: A dataset of 1000 Tc-99m DTPA SPECT/CT scans was analyzed for training (n = 800), validation (n = 100), and testing (n = 100) using a modified 3D U-Net for deep learning. The study investigated the use of primary emission and scattering SPECT data, normalization methods, loss function optimization, and up-sampling techniques for optimal μ-map generation. The problem of checkerboard artifacts, unique to μ-map generation from SPECT signals, and the effects of iodine contrast media were evaluated. The addition of scattering SPECT to primary emission SPECT imaging, logarithmic maximum normalization, the combination of absolute difference loss (L1) and three times the absolute gradient difference loss (3 × LGDL), and the nearest-neighbor interpolation significantly enhanced AI performance in μ-map generation (p < 0.00001). Checkerboard artifacts were effectively eliminated using the nearest-neighbor interpolation technique. The developed AI algorithm produced μ-maps neutral to the presence of iodine contrast and showed negligible contrast effects on quantitative SPECT measurement, such as glomerular filtration rate (GFR). The potential reduction in radiation exposure by transitioning to AI-based CT-free SPECT imaging ranges from 45.3 to 78.8%. Conclusion: The study successfully developed and optimized a deep learning algorithm for generating synthetic μ-maps in kidney SPECT images, demonstrating the potential to transition from conventional SPECT/CT to CT-free SPECT imaging for GFR measurement. This advancement represents a significant step towards enhancing patient safety and efficiency in nuclear medicine. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) requires computed tomography (CT)-based attenuation correction and manual thyroid segmentation on CT for %thyroid uptake measurements. Here, we aimed to develop a deep-learning-based CT-free quantitative thyroid SPECT that can generate an attenuation map (μ-map) and automatically segment the thyroid. Methods: Quantitative thyroid SPECT/CT data (n = 650) were retrospectively analyzed. Typical 3D U-Nets were used for the μ-map generation and automatic thyroid segmentation. Primary emission and scattering SPECTs were inputted to generate a μ-map, and the original μ-map from CT was labeled (268 and 30 for training and validation, respectively). The generated μ-map and primary emission SPECT were inputted for the automatic thyroid segmentation, and the manual thyroid segmentation was labeled (280 and 36 for training and validation, respectively). Other thyroid SPECT/CT (n = 36) and salivary SPECT/CT (n = 29) were employed for verification. Results: The synthetic μ-map demonstrated a strong correlation (R2 = 0.972) and minimum error (mean square error = 0.936 × 10−4, %normalized mean absolute error = 0.999%) of attenuation coefficients when compared to the ground truth (n = 30). Compared to manual segmentation, the automatic thyroid segmentation was excellent with a Dice similarity coefficient of 0.767, minimal thyroid volume difference of − 0.72 mL, and a short 95% Hausdorff distance of 9.416 mm (n = 36). Additionally, %thyroid uptake by synthetic μ-map and automatic thyroid segmentation (CT-free SPECT) was similar to that by the original μ-map and manual thyroid segmentation (SPECT/CT) (3.772 ± 5.735% vs. 3.682 ± 5.516%, p = 0.1090) (n = 36). Furthermore, the synthetic μ-map generation and automatic thyroid segmentation were successfully performed in the salivary SPECT/CT using the deep-learning algorithms trained by thyroid SPECT/CT (n = 29). Conclusion: CT-free quantitative SPECT for automatic evaluation of %thyroid uptake can be realized by deep-learning. Key points: Question 1: Can CT-free attenuation correction be realized for SPECT? Pertinent findings: The first deep-learning algorithm produced μ-map similar to CT-derived μ-map. Implications for patient care: Quantitative SPECT can be performed without CT. Therefore, patients can be protected from redundant radiation exposure of CT. Question 2: Can the thyroid be segmented without high-resolution images like CT? Pertinent findings: The second deep-learning algorithm successfully generated the thyroid segmentation map using low-resolution images such as the generated μ-map and SPECT. Implications for patient care: The thyroid segmentation process was dramatically reduced from 40–60 min to < 1 min, facilitating rapid patient care. Question 3: Can quantitative SPECT/CT be possible without CT? Pertinent findings: The two deep-learning algorithms deprived the quantitative thyroid SPECT/CT of CT. Implications for patient care: Repetitive CT acquisitions may be excluded in multiple SPECT/CT-based nuclear imaging studies, such as dosimetry. [ABSTRACT FROM AUTHOR]

Purpose: 68 Ga-DOTA-TOC uptake in the pancreas uncinate process is often found due to physiologic expression of somatostatin receptors (SSTR). We investigated the association of physiologic 68 Ga-DOTA-TOC uptake in the pancreas uncinate process with blood glucose level. Methods: 68 Ga-DOTA-TOC PET scans acquired from 44 patients (male:female = 20:24, age = 50.8 ± 14.8y [mean ± SD]) were retrospectively analyzed. The blood glucose level (BGL) was examined before 68 Ga-DOTA-TOC injection. Patients diagnosed with diabetes mellitus and patients with BGL over 200 mg/dl were excluded. 68 Ga-DOTA-TOC uptake was measured by the maximum standardized uptake values (SUVmax). Additionally, SSTR-positive volume (SV) in the pancreas uncinate process was measured with two different thresholds: by SUV cutoff of 2.5 (SV2.5) and 40% of SUVmax (SV40%). These measurements on 68 Ga-DOTA-TOC PET were correlated with BGL. Results: The mean of SUVmax of the pancreas uncinate process was 6.51 ± 2.04. SV2.5 was 17.81 ± 7.14 cm3, and SV40% was 18.20 ± 8.83 cm3. A significant negative correlation was found between SUVmax of the pancreas uncinate process and BGL (r = -0.37, p = 0.01). The ratio between SUVmax of the pancreas uncinate process and SUVmean of the pancreas body also showed a significance negative correlation with BGL (r = -0.40, p = 0.01). SV2.5 (r = 0.27, p = 0.07) and SV40% (r = -0.151, p = 0.32) were not significantly correlated with BGL. Conclusion: Physiologic 68 Ga-DOTA-TOC uptake in the pancreas uncinate process was negatively correlated with BGL. Our results suggested that glycemia could affect physiologic uptake of 68 Ga-DOTA-TOC. [ABSTRACT FROM AUTHOR]

Background: The current diagnostic criteria for temporomandibular disorders (TMD) do not require imaging for the diagnosis of degenerative joint disease (DJD) of the temporomandibular joint (TMJ) condyle, and there is a lack of data investigating the effectiveness of imaging modalities in predicting long‐term TMJ DJD prognosis. Objectives: To verify the association between initial bone scintigraphy results and long‐term DJD bone changes occurring in the TMJ condyle on cone beam computed tomography (CBCT). Methods: Initial bone scintigraphy, panoramic radiography and CBCT results were analysed in relation to long‐term (12 months) TMJ DJD bone change on CBCTs in 55 TMD patients (110 joints). Clinical and radiographic indices were statistically analysed among three groups (improved, no change, and worsened) based on long‐term TMJ DJD prognosis calculated by destructive change index (DCI). Results: Neither the uptake ratio nor visual assessment results from initial bone scintigraphy showed a significant difference according to long‐term condylar bone change groups. The cut‐off value of bone scintigraphy uptake ratio was 2.53 for long‐term worsening of TMJ DJD. Worsening of TMJ DJD was significantly associated with the diagnosis based on panoramic radiography (p =.011) and CBCT (p <.001). Initial DCI (β = −.291, p =.046) had a significant association with long‐term worsening of TMJ DJD. Conclusion: Initial bone scintigraphy results did not show sufficiently close associations with long‐term TMJ DJD prognosis. This should be considered in the selection process of imaging modalities for TMJ DJD patients. Future studies are needed to develop prognostic indices that comprise both clinical and imaging contents for improved predictive ability. [ABSTRACT FROM AUTHOR]

Quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) using Tc-99m pertechnetate aids in evaluating salivary gland function. However, gland segmentation and quantitation of gland uptake is challenging. We develop a salivary gland SPECT/CT with automated segmentation using a deep convolutional neural network (CNN). The protocol comprises SPECT/CT at 20 min, sialagogue stimulation, and SPECT at 40 min post-injection of Tc-99m pertechnetate (555 MBq). The 40-min SPECT was reconstructed using the 20-min CT after misregistration correction. Manual salivary gland segmentation for %injected dose (%ID) by human experts proved highly reproducible, but took 15 min per scan. An automatic salivary segmentation method was developed using a modified 3D U-Net for end-to-end learning from the human experts (n = 333). The automatic segmentation performed comparably with human experts in voxel-wise comparison (mean Dice similarity coefficient of 0.81 for parotid and 0.79 for submandibular, respectively) and gland %ID correlation (R2 = 0.93 parotid, R2 = 0.95 submandibular) with an operating time less than 1 min. The algorithm generated results that were comparable to the reference data. In conclusion, with the aid of a CNN, we developed a quantitative salivary gland SPECT/CT protocol feasible for clinical applications. The method saves analysis time and manual effort while reducing patients' radiation exposure. [ABSTRACT FROM AUTHOR]

Purpose: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning–based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading. Methods: A total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide). Results: Inter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system). Conclusion: Our results highlight the impact of deep learning–based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading. [ABSTRACT FROM AUTHOR]

Introduction: Patients presenting with tears of the triangular fibrocartilage complex (TFCC) can have ulnar positive variance, for which the clinical relevance to concomitant ulnar impaction syndrome (UIS) may be unclear. We hypothesized that maximum standardized uptake value (SUVmax), a semiquantitative single-photon-emission computed tomography/computed tomography (SPECT/CT) value, would distinguish between the traumatic TFCC tear and degenerative TFCC tear associated with the UIS. This study aimed to compare SUVmax between patients with TFCC tear only and patients with TFCC tear and UIS. Methods: A total of 26 patients presenting with TFCC tears on magnetic resonance imaging (MRI) underwent semiquantitative SPECT/CT examinations. The diagnosis of concomitant UIS was made based on positive ulnar impaction tests and MRI findings. We compared the SUVmax between patients with and without concomitant UIS. We also calculated the cutoff value for the diagnosis of UIS using receiver operating characteristic curve analysis. Results: Of 26 patients, 14 had concomitant UIS, and 12 had TFCC tears only. The SUVmax was significantly higher in patients with concomitant UIS than in those without UIS (p = 0.048). With a SUVmax cutoff value of 4.09 for UIS, sensitivity of 67% and specificity of 82% were obtained. Conclusions: In the semiquantitative SPECT/CT examinations of patients with TFCC tears, those with concomitant UIS had a higher SUVmax than those without UIS. Semiquantitative SPECT/CT can be helpful in confirming concomitant UIS in patients with TFCC tears. [ABSTRACT FROM AUTHOR]

Purpose: Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers. Methods: Amyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method. Results: The mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PET and a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively. Conclusion: We suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers. [ABSTRACT FROM AUTHOR]

Extragonadal teratoma originating from the retroperitoneum represents less than 5% of all teratomas and accounts for less than 10% of all pediatric retroperitoneal neoplasms. To date, there has been no report of teratoma managed with LT. This study reports an infant aged 3 months with retroperitoneal immature teratoma involving the hepatic hilum, refractory to chemotherapy and treated with LT. The patient was referred to our hospital for management of a growing abdominal mass. Histopathology of a fine needle biopsy of the lesion suggested the possibility of a hepatoblastoma with teratoid features. Cisplatin-based chemotherapy was initiated, but rapid growth of the tumor encasing the hepatic artery proper was detected, even after two cycles of chemotherapy. A split LT was carried out, and pathological examination of the explanted liver revealed the involvement of numerous neuroepithelial components, confirming the diagnosis of a Norris grade 3 immature teratoma. The patient recovered well and was discharged on day 19 post-LT. As on date, on postoperative day 240, he has completed seven cycles of a 12-cycle vinblastine and doxorubicin-based adjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study is to identify the safety and feasibility of laparoscopic caudate lobectomy.Background: Caudate lobectomy has been considered as technically difficult because of the deep location of the caudate lobe and its proximity to great vessels. Due to the technical difficulty, laparoscopic caudate lobectomy was not feasible in patients with malignancy in the caudate lobe.Methods: Six consecutive patients with caudate hepatic malignancy received laparoscopic caudate lobectomy at Samsung Medical Center from September 2007 to May 2014. Demographic data, intraoperative parameters, and postoperative outcomes were assessed.Results: All procedures for these 6 patients with caudate malignancy were completed with totally laparoscopic technique. Only 1 patient who had hepatocellular carcinoma in the Spiegel lobe underwent partial caudate lobectomy, and others underwent complete caudate lobectomy. The mean tumor size was 2.65 cm (range 0.9-5.1 cm). The mean operative time was 382 minutes (range 168-615 minutes) and none required transfusion. The mean duration of hospital stay was 8 days (range 6-13 days). There was no perioperative complication and patient mortality in this series. The resected margins of the specimens were tumor free (R0 resections, range 0.1-1.2 cm). The mean follow-up period was 56 months (range 12.9-93.7 months). No patient died during the follow-up period.Conclusions: Our experience demonstrated that laparoscopic caudate lobectomy is safe and feasible in selected patients with malignancy in the caudate lobe. [ABSTRACT FROM AUTHOR]

Background: Despite increases in the performance of pure laparoscopic living donor hepatectomy, variations in the bile duct or portal vein have been regarded as relative contraindications to this technique [1-3]. This report describes a donor with separate right posterior and right anterior hepatic ducts and portal veins who underwent pure laparoscopic living donor right hemihepatectomy, integrated with 3D laparoscopy and indocyanine green (ICG) near-infrared fluorescence cholangiography [1, 4, 5].Methods: A 50-year-old man offered to donate part of his liver to his older brother, who required a transplant for hepatitis B-associated liver cirrhosis and hepatocellular carcinoma. Donor height was 178.0 cm, body weight was 82.7 kg, and body mass index was 26.1 kg/m2. Preoperative computed tomography and magnetic resonance cholangiopancreatography showed that the donor had separate right posterior and right anterior hepatic ducts and portal veins. The entire procedure was performed under 3D laparoscopic view. Following intravenous injections of 0.05 mg/kg ICG, ICG near-infrared fluorescence camera was used to demarcate the exact transection line and determine the optimal bile duct division point.Results: The total operation time was 443 min; the donor required no transfusions and experienced no intraoperative complications. The graft weighed 1146 g with a graft-to-recipient weight ratio of 1.88%. The optimal bile duct division point was identified using ICG fluorescence cholangiography, and the bile duct was divided with good patency without any stricture. The right anterior and posterior portal veins were transected with endostaplers without any torsion. The patient was discharged on postoperative day 8, with no complications.Conclusion: Using a 3D view and ICG fluorescence cholangiography, pure 3D laparoscopic living donor right hemihepatectomy is feasible in a donor with separate right posterior and right anterior hepatic ducts and portal veins. [ABSTRACT FROM AUTHOR]