Correlations Between Single-Photon Emission Computed Tomography Parameters of Salivary Glands and Dry Eye Tests in Patients With Sjögren's Syndrome

ObjectiveTo evaluate the correlations between Single-Photon Emission Computed Tomography (SPECT) parameters of salivary glands and dry eye parameters in patients with Sjögren's syndrome (SS).MethodsA total of 28 patients with SS participated in this prospective study. Dry eye assessments include tear film break-up time (TBUT), corneal fluorescein staining scoring (CFS), Schirmer's I test (SIT) examination and SPECT of salivary gland. The following quantitative parameters were derived from SPECT imaging for salivary glands: Uptake index (UI), the time needed to achieve the minimum counts after Vit C stimulation (Ts), and excretion fraction (EF). The relation between the aforementioned parameters and TBUT, CFS and SIT were analyzed with SPSS 22.0 software.ResultsAll the 28 eyes of the 28 subjects were examined. The mean SIT was 6.04 ± 4.64 mm/5 min (0–18 mm/5 min); the mean CFS was 3.07 ± 2.65 (0–10) and the mean BUT was 2.11 ± 1.97 s (0–9 s). The mean EF value was 0.52 ± 0.12 (0.26–0.75) in parotid glands and 0.45 ± 0.10 (0.30–0.67) in submandibular glands, respectively. The mean UI value was 9.33 ± 1.68 (6.03–13.20) in parotid glands and 9.92 ± 1.48 (7.08–12.60) in submandibular glands, respectively. The mean Ts (min) was 5.32 ± 3.01 (2.00–12.00) in parotid glands and 11.09 ± 7.40 (2.00- 29.00 min) in submandibular glands, respectively. It was found that EF positively correlates with SIT in patients with SS (r = 0.499 and 0.426 in parotid glands and submandibular glands, with P < 0.05), while no significant correlation was found between the UI, Ts and CFS, TBUT (P > 0.05).ConclusionsThe EF was positively correlated with SIT in patients with SS, it could reflex the dysfunction of salivary glands in SS patients. So, EF may be a valuable parameter for the diagnosis of SS patients with lacrimal gland secretion dysfunction.

Influence of brain atrophy using semiquantitative analysis in [123I]FP-CIT single-photon emission computed tomography by a Monte Carlo simulation study

The specific binding ratio (SBR) is an objective indicator of N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-[123I] iodophenyl) nortropane ([123I]FP-CIT) single-photon emission computed tomography (SPECT) that could be used for the diagnosis of Parkinson’s disease and Lewy body dementia. One of the issues of the SBR analysis is that the setting position of the volume of interest (VOI) may contain cerebral ventricles and cerebral grooves. These areas may become prominent during the brain atrophy analysis; however, this phenomenon has not been evaluated enough. This study thus used Monte Carlo simulations to examine the effect of brain atrophy on the SBR analysis. The brain atrophy model (BAM) used to simulate the three stages of brain atrophy was made using a morphological operation. Brain atrophy levels were defined in the descending order from 1 to 3, with Level 3 indicating to the most severe damage. Projection data were created based on BAM, and the SPECT reconstruction was performed. The ratio of the striatal to background region accumulation was set to a rate of 8:1, 6:1, and 4:1. The striatal and the reference VOI mean value were decreased as brain atrophy progressed. Additionally, the Bolt’s analysis methods revealed that the reference VOI value was more affected by brain atrophy than the striatal VOI value. Finally, the calculated SBR value was overestimated as brain atrophy progressed, and a similar trend was observed when the ratios of the striatal to background region accumulation were changed. This study thus suggests that the SBR can be overestimated in cases of advanced brain atrophy.

Single-Photon Emission Computed Tomography/Computed Tomography Image-Based Radiomics for Discriminating Vertebral Bone Metastases From Benign Bone Lesions in Patients With Tumors

Purpose: The purpose of this study was to investigate the feasibility of Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) image-based radiomics in differentiating bone metastases from benign bone lesions in patients with tumors.Methods: A total of 192 lesions from 132 patients (134 in the training group, 58 in the validation group) diagnosed with vertebral bone metastases or benign bone lesions were enrolled. All images were evaluated and diagnosed independently by two physicians with more than 20 years of diagnostic experience for qualitative classification, the images were imported into MaZda software in Bitmap (BMP) format for feature extraction. All radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation algorithms after the process of normalization and correlation analysis. Based on these selected features, two models were established: The CT model and SPECT model (radiomics features were derived from CT and SPECT images, respectively). In addition, a combination model (ComModel) combined CT and SPECT features was developed in order to better evaluate the predictive performance of radiomics models. Subsequently, the diagnostic performance between each model was separately evaluated by a confusion matrix.Results: There were 12, 13, and 18 features contained within the CT, SPECT, and ComModel, respectively. The constructed radiomics models based on SPECT/CT images to discriminate between bone metastases and benign bone lesions not only had high diagnostic efficacy in the training group (AUC of 0.894, 0.914, 0.951 for CT model, SPECT model, and ComModel, respectively), but also performed well in the validation group (AUC; 0.844, 0.871, 0.926). The AUC value of the human experts was 0.849 and 0.839 in the training and validation groups, respectively. Furthermore, both SPECT model and ComModel show higher classification performance than human experts in the training group (P = 0.021 and P = 0.001, respectively) and the validation group (P = 0.037 and P = 0.007, respectively). All models showed better diagnostic accuracy than human experts in the training group and the validation group.Conclusion: Radiomics derived from SPECT/CT images could effectively discriminate between bone metastases and benign bone lesions. This technique may be a new non-invasive way to help prevent unnecessary delays in diagnosis and a potential contribution in disease staging and treatment planning.

Transient Global Cerebral Hypoperfusion as a Characteristic Cerebral Hemodynamic Pattern in the Acute Stage after Combined Revascularization Surgery for Pediatric Moyamoya Disease: N-Isopropyl-P-[123I] Iodoamphetamine Single-Photon Emission Computed Tomography Study

<b><i>Introduction:</i></b> Surgical revascularization prevents cerebral ischemic attack by improving cerebral blood flow (CBF) in both adult and pediatric patients with moyamoya disease (MMD). Uneven hemodynamic changes, including local cerebral hyperperfusion and remote ischemia, can cause delayed intracerebral hemorrhage and perioperative infarctions in adult MMD patients, but the characteristic hemodynamic pattern among pediatric MMD patients after revascularization surgery is poorly understood. <b><i>Methods:</i></b> This study included 16 consecutive pediatric MMD patients (age, 6–16 years; mean age, 11.3) undergoing superficial temporal artery-middle cerebral artery anastomosis combined with encephalo-duro-myo-synangiosis on 21 affected hemispheres. Perioperative management was conducted by aspirin administration and strict blood pressure control (110–130 mm Hg). We prospectively performed N-isopropyl-p-[¹²³I] iodoamphetamine single-photon emission computed tomography on postoperative days (POD) 1 and 7 and analyzed the temporal changes in perioperative hemodynamics. <b><i>Results:</i></b> Four patients (19.0%, 4/21) exhibited immediate CBF improvement from POD 1, which was classified as “immediate redistribution pattern.” In contrast, 9 (42.9%, 9/21) demonstrated transient hemispheric global hypoperfusion at POD 1 and subsequent CBF improvement at POD 7, which was defined as “transient hypoperfusion pattern.” Although 8 patients, including 4 with “transient hypoperfusion pattern” (44.4, 4/9), developed mild transient neurological deterioration in the acute stage, it resolved in all 21 patients, and there were no permanent neurological deficits. <b><i>Discussion/Conclusions:</i></b> This study revealed that the “transient hypoperfusion pattern” after revascularization surgery is relatively common among pediatric MMD patients, and its outcome is favorable under strict perioperative management.

Low-Parameter Small Convolutional Neural Network Applied to Functional Medical Imaging of Tc-99m Trodat-1 Brain Single-Photon Emission Computed Tomography for Parkinson’s Disease

Parkinson’s disease (PD), a progressive disease that affects movement, is related to dopaminergic neuron degeneration. Tc-99m Trodat-1 brain (TRODAT) single-photon emission computed tomography (SPECT) aids the functional imaging of dopamine transporters and is used for dopaminergic neuron enumeration. Herein, we employed a convolutional neural network to facilitate PD diagnosis through TRODAT SPECT, which is simpler than models such as VGG16 and ResNet50. We retrospectively collected the data of 3188 patients (age range 20–107 years) who underwent TRODAT SPECT between June 2011 and December 2019. We developed a set of functional imaging multiclassification deep learning algorithms suitable for TRODAT SPECT on the basis of the annotations of medical experts. We then applied our self-proposed model and compared its results with those of four other models, including deep and machine learning models. TRODAT SPECT included three images collected from each patient: one presenting the maximum absorption of the metabolic function of the striatum and two adjacent images. An expert physician determined that our model’s accuracy, precision, recall, and F1-score were 0.98, 0.98, 0.98, and 0.98, respectively. Our TRODAT SPECT model provides an objective, more standardized classification correlating to the severity of PD-related diseases, thereby facilitating clinical diagnosis and preventing observer bias.

Single-Photon Emission Computed Tomography Image-Assisted Diagnosis of Thyroid Diseases under Convolutional Network Neural Algorithm

The aim of this study was to investigate the value of single-photon emission computed tomography (SPECT) based on the convolutional neural network (CNN) algorithm in thyroid diseases. Thirty-five patients with thyroid disease from the hospital were selected as the observation group, and another 35 healthy volunteers were selected as the control group. The constructed model of SPECT based on the CNN algorithm was compared with the backpropagation neural network (BPNN) algorithm, which was then applied to the SPECT of 35 patients with thyroid disease. It turned out that as the number of iterations increased, the parameter training of CNN was gradually sufficient, the network model was continuously optimized, and the accuracy gradually increased. From the data results, the Dice value of the proposed CNN algorithm was higher than that of the BPNN algorithm and the segmentation effect was relatively good. The visual index of the thyroid/neck of the observation group (2.68 ± 1.32) was remarkably inferior to that of the control group (12.347.54) ( P < 0.05 ). The visual index of the thyroid/submandibular gland in the observation group (1.02 ± 0.41) was remarkably inferior to that of the control group (8.89 ± 4.86) ( P < 0.05 ). The visual index of the thyroid/parotid gland in the observation group (1.04 ± 0.58) was remarkably inferior to that of the control group (8.53 ± 4.25) ( P < 0.05 ). In addition, 99mTcO4-SPECT had a sensitivity of 95.2%, a specificity of 90.3%, and an accuracy of 91.5% in the diagnosis of thyroid diseases. The area under the curve of the receiver operating characteristic curve for 99mTcO4-SPECT diagnosis of thyroid disease is 0.958, and the 95% confidence interval is 0.834∼1. In summary, the SPECT based on the CNN algorithm proposed in this study has a good segmentation effect and can accurately locate the anatomical information of thyroid diseases, which can replace the traditional diagnostic methods for the diagnosis of thyroid diseases.