Nuclear Medicine and Molecular Imaging最新文献

Peptide receptor radionuclide therapy (PRRT) has become an established treatment for patients with inoperable and/or metastatic, well-differentiated neuroendocrine tumors with overexpression of somatostatin receptor type 2 (SSTR-2). The post-therapy ¹⁷⁷Lu-DOTATATE whole-body scan not only assesses the biodistribution of the lesions seen on pre-therapy ⁶⁸ Ga-SSTR PET/CT scan but also provides a quick assessment of disease status and dosimetry during treatment. Like any other radionuclide scan, the whole-body ¹⁷⁷Lu-DOTATATE scan may also show abnormal radiotracer uptake, which may require further imaging to establish its exact etiology. Though radiotracer emboli mimicking focal pulmonary lesions have been described with ¹⁸F-FDG and ⁶⁸ Ga-DOTANOC PET/CT scans, similar artifacts with post-therapy ¹⁷⁷Lu-DOTATATE scans have not been described. Herein, we report two cases of hot emboli in the post-therapy ¹⁷⁷Lu-DOTATATE scans.

Aspergillus infection is relatively rare disease, and we present a case of orbital aspergillus infection who presented with right orbital pain and swelling. Right orbital lesion was identified on CT, MRI, and PET-CT imaging followed by confirmation of aspergillus on histopathological examination. We demonstrate that Tc-99 m ubiquicidin scan can yield positive results in aspergillosis too, enabling its differentiation from non-infective pathologies.

The diagnosis of patients with fever of unknown origin (FUO) in pediatric heart transplantation is a challenging medical problem. The physician should differentiate between rejections, infections, malignancy, adrenal insufficiency, and drug fever. Immunosuppressive therapy in these patients exposes them to a high risk of developing a post-transplantation fungal infection. In this case, we discuss the diagnostic contribution of the ^99mTc-UBI scan and ¹⁸F-FDG PET scan for diagnosis of fungal infection causing FUO in these patients.

Purpose: We evaluated the residual vascular and adipose tissue inflammation in patients with chronic coronary artery disease (CAD) using positron emission tomography (PET).

Methods: Our study population consisted of 98 patients with known CAD and 94 control subjects who had undergone ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET due to non-cardiac reasons. Aortic root and vena cava superior ¹⁸F-FDG uptake were measured to obtain the aortic root target-to-background ratio (TBR). In addition, adipose tissue PET measurements were done in pericoronary, epicardial, subcutaneous, and thoracic adipose tissue. Adipose tissue TBR was calculated using the left atrium as a reference region. Data are presented as mean ± standard deviation or as median (interquartile range).

Results: The aortic root TBR was higher in CAD patients compared to control subjects, 1.68 (1.55-1.81) vs. 1.53 (1.43-1.64), p < 0.001. Subcutaneous adipose tissue uptake was elevated in CAD patients 0.30 (0.24-0.35) vs. 0.27 (0.23-0.31), p < 0.001. Metabolic activity of CAD patients and control subjects was comparable in the pericoronary (0.81 ± 0.18 vs. 0.80 ± 0.16, p = 0.59), epicardial (0.53 ± 0.21 vs. 0.51 ± 0.18, p = 0.38) and thoracic (0.31 ± 0.12 vs. 0.28 ± 0.12, p = 0.21) adipose tissue regions. Aortic root or adipose tissue ¹⁸F-FDG uptake was not associated with the common CAD risk factors, coronary calcium score, or aortic calcium score (p value > 0.05).

Conclusion: Patients with a chronic CAD had a higher aortic root and subcutaneous adipose tissue ¹⁸F-FDG uptake compared to control patients, which suggests residual inflammatory risk.

Introduction: Current treatment approach aims to achieve greater efficacy with fewer side effects, by targeted cancer therapy as much as possible. Radionuclide therapy is a modality that uses cancer theranostics and is increasingly applied for various cancers as a targeted therapy. YouTube is a preferred tool for obtaining medical information from the internet. This study aims to determine the content quality, level of interaction and usefulness as education material of radionuclide therapy YouTube videos and to reveal the impact of the COVID-19 process on these parameters.

Materials and methods: The keywords were searched on YouTube on August 25, 2018, and May 10, 2021. After removing duplicate and excluded videos, all remaining videos were scored and coded.

Results: Majority of the videos were useful educational material. Most of them were high quality. Popularity markers were unrelated to quality level. After COVID, the power index of videos with high JAMA scores increased. The COVID-19 pandemic did not have a negative effect on video features; the quality of the content increased even more after the pandemic.

Conclusion: Radionuclide therapy YouTube videos have high-quality content and provide useful education material. The popularity is independent of the content quality. During the pandemic, video quality and usefulness characteristics did not change, while the visibility is increased. We consider YouTube to be an appropriate educational material for patients and healthcare professionals to gain basic knowledge of radionuclide therapy. The Covıd-19 pandemic highlighted the power of radionuclide therapy YouTube videos as an educational material.

Purpose: Delayed images may not be acquired due to severe pain, drowsiness, or worsening vital signs while waiting after blood pool imaging in three-phase bone scintigraphy. If the hyperemia in the blood pool image contains information from which increased uptake on the delayed images can be inferred, the generative adversarial network (GAN) can generate the increased uptake from the hyperemia. We attempted to apply pix2pix, a type of conditional GAN, to transform hyperemia into increased bone uptake.

Methods: We enrolled 1464 patients who underwent three-phase bone scintigraphy for inflammatory arthritis, osteomyelitis, complex regional pain syndrome (CRPS), cellulitis, and recent bone injury. Blood pool images were acquired 10 min after intravenous injection of Tc-99 m hydroxymethylene diphosphonate, and delayed bone images were obtained after 3 h. The model was based on the open-source code of the pix2pix model with perceptual loss. Increased uptake in the delayed images generated by the model was evaluated using lesion-based analysis by a nuclear radiologist in areas consistent with hyperemia in the blood pool images.

Results: The model showed sensitivities of 77.8% and 87.5% for inflammatory arthritis and CRPS, respectively. In osteomyelitis and cellulitis, their sensitivities of about 44% were observed. However, in cases of recent bone injury, the sensitivity was only 6.3% in areas consistent with focal hyperemia.

Conclusion: The model based on pix2pix generated increased uptake in delayed images matching the hyperemia in the blood pool image in inflammatory arthritis and CRPS.

For more anatomically precise quantitation of mouse brain PET, spatial normalization (SN) of PET onto MR template and subsequent template volumes-of-interest (VOIs)-based analysis are commonly used. Although this leads to dependency on the corresponding MR and the process of SN, routine preclinical/clinical PET images cannot always afford corresponding MR and relevant VOIs. To resolve this issue, we propose a deep learning (DL)-based individual-brain-specific VOIs (i.e., cortex, hippocampus, striatum, thalamus, and cerebellum) directly generated from PET images using the inverse-spatial-normalization (iSN)-based VOI labels and deep convolutional neural network model (deep CNN). Our technique was applied to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and ¹⁸F FDG PET scans before and after the administration of human immunoglobin or antibody-based treatments. To train the CNN, PET images were used as inputs and MR iSN-based target VOIs as labels. Our devised methods achieved decent performance in terms of not only VOI agreements (i.e., Dice similarity coefficient) but also the correlation of mean counts and SUVR, and CNN-based VOIs was highly accordant with ground-truth (the corresponding MR and MR template-based VOIs). Moreover, the performance metrics were comparable to that of VOI generated by MR-based deep CNN. In conclusion, we established a novel quantitative analysis method both MR-less and SN-less fashion to generate individual brain space VOIs using MR template-based VOIs for PET image quantification.

Supplementary information: The online version contains supplementary material available at 10.1007/s13139-022-00772-4.

Purpose: Classification of focal skeleton/bone marrow uptake (BMU) can be challenging. The aim is to investigate whether an artificial intelligence-based method (AI), which highlights suspicious focal BMU, increases interobserver agreement among a group of physicians from different hospitals classifying Hodgkin's lymphoma (HL) patients staged with [¹⁸F]FDG PET/CT.

Methods: Forty-eight patients staged with [¹⁸F]FDG PET/CT at Sahlgenska University Hospital between 2017 and 2018 were reviewed twice, 6 months apart, regarding focal BMU. During the second time review, the 10 physicians also had access to AI-based advice regarding focal BMU.

Results: Each physician's classifications were pairwise compared with the classifications made by all the other physicians, resulting in 45 unique pairs of comparisons both without and with AI advice. The agreement between the physicians increased significantly when AI advice was available, which was measured as an increase in mean Kappa values from 0.51 (range 0.25-0.80) without AI advice to 0.61 (range 0.19-0.94) with AI advice (p = 0.005). The majority of the physicians agreed with the AI-based method in 40 (83%) of the 48 cases.

Conclusion: An AI-based method significantly increases interobserver agreement among physicians working at different hospitals by highlighting suspicious focal BMU in HL patients staged with [¹⁸F]FDG PET/CT.

A major opportunity in nuclear cardiology is the many significant artificial intelligence (AI) applications that have recently been reported. These developments include using deep learning (DL) for reducing the needed injected dose and acquisition time in perfusion acquisitions also due to DL improvements in image reconstruction and filtering, SPECT attenuation correction using DL without need for transmission images, DL and machine learning (ML) use for feature extraction to define myocardial left ventricular (LV) borders for functional measurements and improved detection of the LV valve plane and AI, ML, and DL implementations for MPI diagnosis, prognosis, and structured reporting. Although some have, most of these applications have yet to make it to widespread commercial distribution due to the recency of their developments, most reported in 2020. We must be prepared both technically and socio-economically to fully benefit from these and a tsunami of other AI applications that are coming.

Purpose: Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [¹⁸F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the performance of lung cancer segmentation using [¹⁸F]FDG PET/CT.

Methods: The whole-body [¹⁸F]FDG PET/CT scan data of 887 patients with lung cancer were retrospectively used for network training and evaluation. The ground-truth tumor volume of interest was drawn using the LifeX software. The dataset was randomly partitioned into training, validation, and test sets. Among the 887 PET/CT and VOI datasets, 730 were used to train the proposed models, 81 were used as the validation set, and the remaining 76 were used to evaluate the model. In Stage 1, the global U-net receives 3D PET/CT volume as input and extracts the preliminary tumor area, generating a 3D binary volume as output. In Stage 2, the regional U-net receives eight consecutive PET/CT slices around the slice selected by the Global U-net in Stage 1 and generates a 2D binary image as the output.

Results: The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net.

Conclusion: The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [¹⁸F]FDG PET/CT.