Generation of synthetic TSPO PET maps from structural MRI images

Background: Individual skull models of bone density and geometry are important when planning the expected transcranial ultrasound acoustic field and estimating mechanical and thermal safety in low-intensity transcranial ultrasound stimulation (TUS) studies. Computed tomography (CT) images have typically been used to estimate skull acoustic properties. However, obtaining CT images in research participants may be prohibitive due to exposure to ionising radiation and limited access to CT scanners within research groups. Objective: We present a validated open-source tool for researchers to obtain individual skull estimates from T1-weighted MR images, for use in acoustic simulations. We refined a previously trained and validated 3D convolutional neural network (CNN) to generate 100 keV pseudo-CTs. The network was pretrained on 110 individuals and refined and tested on a database of 37 healthy control individuals. We compared simulations based on reference CTs to simulations based on our pseudo-CTs and binary skull masks, a common alternative in the absence of CT. Compared with reference CTs, our CNN produced pseudo-CTs with a mean absolute error of 109.8 ± 13.0 HU across the whole head and 319.3 ± 31.9 HU in the skull. In acoustic simulations, the focal pressure was statistically equivalent for simulations based on reference CT and pseudo-CT (0.48 ± 0.04 MPa and 0.50 ± 0.04 MPa respectively) but not for binary skull masks (0.28 ± 0.05 MPa). We show that our network can produce pseudo-CT comparable to reference CTs in healthy individuals, and that these can be used in acoustic simulations.

How far is arterial spin labeling MRI from a clinical reality? Insights from arterial spin labeling comparative studies in Alzheimer's disease and other neurological disorders.

Organ segmentation using 18F-FDG PET images alone has not been extensively explored. Segmentation based methods based on deep learning (DL) have traditionally relied on CT or MRI images, which are vulnerable to alignment issues and artifacts. This study aimed to develop a DL approach for segmenting the entire liver based solely on 18F-FDG PET images. We analyzed data from 120 patients who were assessed using 18F-FDG PET. A three-dimensional (3D) U-Net model from nnUNet and preprocessed PET images served as DL and input images, respectively, for the model. The model was trained with 5-fold cross-validation on data from 100 patients, and segmentation accuracy was evaluated on an independent test set of 20 patients. Accuracy was assessed using Intersection over Union (IoU), Dice coefficient, and liver volume. Image quality was evaluated using mean (SUVmean) and maximum (SUVmax) standardized uptake value and signal-to-noise ratio (SNR). The model achieved an average IoU of 0.89 and an average Dice coefficient of 0.94 based on test data from 20 patients, indicating high segmentation accuracy. No significant discrepancies in image quality metrics were identified compared with ground truth. Liver regions were accurately extracted from 18F-FDG PET images which allowed rapid and stable evaluation of liver uptake in individual patients without the need for CT or MRI assessments.

Effect of Comorbid Knee and Hip Osteoarthritis on Longitudinal Clinical and Health Care Use Outcomes in Older Adults With New Visits for Back Pain

Registration of functional PET and MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. But, the methods published are non-automatic or rigid body transformation. In this paper, we present a method that mapping PET image onto the MR image with automatic non-rigid transformation. The method is largely composed of two parts. First part is the segmentation and extracting the features of both MR and PET by using FCM and morphological methods. And, second part is the non-rigid mapping of PET image onto the PET template and MR image onto MR template. The templates are made from 20 PET images and 20 MR images each other. And, the MR template is registered with PET template. In non-rigid mapping, we use Bayesian framework in which statistical information on the imaging process is combined with prior information on expected template deformations to make inferences about the parameters of the deformation field. The method newly defines intensity similarity between the deforming scan and the target brain. Intensity similarity combined with prior information is used to generate deformation field. We applied our algorithm to PET and T1-weighted MR images from many patients. The registered images were validated by physicians. And we got the satisfactory results.

A 66-year-old male patient who is hepatitis C RNA positive, without cirrhosis, has been found to have a mass on ultrasound examination of the liver. Physical examination is unremarkable. The hemoglobin, white cell count and platelet counts are normal. The alkaline phosphatase is mildly elevated to 212 U/L(normal <115), the AST 112 U/L, and the ALT 128 U/L. The CA 19-9 is 82 U/mL (normal < 55 U/mL) and the alpha fetoprotein 12 ng /mL. A CT scan of the abdomen shows a vague mass in segment V of the liver without definite arterial enhancement. An MRI of the abdomen is carried out with contrast but the mass is still indeterminate on T1 and T2 imaging.

Recent reports have shown the potential for deep learning (DL) models to automatically segment of Couinaud liver segments and future liver remnant (FLR) for liver resections. However, these studies have mainly focused on the development of the models. Existing reports lack adequate validation of these models in diverse liver conditions and thorough evaluation using clinical cases. This study thus aimed to develop and perform a spatial external validation of a DL model for the automated segmentation of Couinaud liver segments and FLR using computed tomography (CT) in various liver conditions and to apply the model prior to major hepatectomy. This retrospective study developed a 3-dimensional (3D) U-Net model for the automated segmentation of Couinaud liver segments and FLR on contrast-enhanced portovenous phase (PVP) CT scans. Images were obtained from 170 patients from January 2018 to March 2019. First, radiologists annotated the Couinaud segmentations. Then, a 3D U-Net model was trained in Peking University First Hospital (n=170) and tested in Peking University Shenzhen Hospital (n=178) in cases with various liver conditions (n=146) and in candidates for major hepatectomy (n=32). The segmentation accuracy was evaluated using the dice similarity coefficient (DSC). Quantitative volumetry to evaluate the resectability was compared between manual and automated segmentation. The DSC in the test data sets 1 and 2 for segments I to VIII was 0.93±0.01, 0.94±0.01, 0.93±0.01, 0.93±0.01, 0.94±0.00, 0.95±0.00, 0.95±0.00, and 0.95±0.00, respectively. The mean automated FLR and FLR% assessments were 493.51±284.77 mL and 38.53%±19.38%, respectively. The mean manual FLR and FLR% assessments were 500.92±284.38 mL and 38.35%±19.14%, respectively, in test data sets 1 and 2. For test data set 1, when automated segmentation of the FLR% was used, 106, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively; however, when manual segmentation of the FLR% was used, 107, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively. For test data set 2, all cases were categorized as candidates for major hepatectomy when automated and manual segmentation of the FLR% was used. No significant differences in FLR assessment (P=0.50; U=185,545), FLR% assessment (P=0.82; U=188,337), or the indications for major hepatectomy were noted between automated and manual segmentation (McNemar test statistic 0.00; P>0.99). The DL model could be used to fully automate the segmentation of Couinaud liver segments and FLR with CT prior to major hepatectomy in an accurate and clinically practicable manner.

TSPO PET imaging may hold promise as a single-step diagnostic work-up for clinical immunopsychiatry. This review paper on the clinical applicability of TSPO PET for primary psychiatric disorders discusses if and why TSPO PET imaging might become the first clinical immunopsychiatry biomarker and the investment prerequisites and scientific advancements needed to accommodate this transition from bench to bedside. We conducted a systematic search of the literature to identify clinical studies of TSPO PET imaging in patients with primary psychiatric disorders. We included both original case-control studies as well as longitudinal cohort studies of patients with a primary psychiatric diagnosis. Thirty-one original studies met our inclusion criteria. In the field of immunopsychiatry, TSPO PET has until now mostly been studied in schizophrenia and related psychotic disorders, and to a lesser extent in mood disorders and neurodevelopmental disorders. Quantitative TSPO PET appears most promising as a predictive biomarker for the transdiagnostic identification of subgroups or disease stages that could benefit from immunological treatments, or as a prognostic biomarker forecasting patients' illness course. Current scanning protocols are still too unreliable, impractical and invasive for clinical use in symptomatic psychiatric patients. TSPO PET imaging in its present form does not yet offer a sufficiently attractive cost-benefit ratio to become a clinical immunopsychiatry biomarker. Its translation to psychiatric clinical practice will depend on the prioritising of longitudinal research and the establishment of a uniform protocol rendering clinically meaningful TSPO uptake quantification at the shortest possible scan duration without arterial cannulation.

Rapid Response to Treatment of Multiple Myeloma Detected with FDG PET Scanning in Multiple Myeloma - Early Results from Total Therapy III.

Deep learning (DL) represents a promising technique for image restoration. We explored its ability to restore ultra-low-count [18F]FDG PET studies of the brain in subjects with dementia and in healthy subjects to allow for reduced scan durations or administered activities without compromising diagnostic performance. Methods: Various DL models using the content aware image restoration approach of CSBDeep toolbox (3D U-nets) were trained with subvolumes of 1,000 random subjects. On the basis of 10-min list-mode PET data after injection of 208 ± 10 MBq of [18F]FDG, we reconstructed reduced scan durations of 2 min, 1 min, 30 s, 20 s, and 10 s. The resulting models were applied to [18F]FDG PET scans of subjects with Alzheimer disease (n = 15), frontotemporal dementia (n = 14), and healthy controls (n = 13). We explored the effect of reduced scan times on individual regional measures in diagnostically relevant regions and on voxel-based group contrasts. Three independent readers rated all datasets with regard to assessability, diagnosis, and diagnostic confidence. Results: Individual mean regional [18F]FDG uptake remained largely unchanged. The SD strongly increased with shorter scan duration without application of DL (mean increase ≤ 48%), whereas it slightly decreased with DL (≥-7%). In group contrasts, the number of significant voxels strongly decreased with shorter scan time without DL (≥-41%), which was partially offset by DL (≥-27%). On visual reads, the fraction of assessable images steeply fell to only 4% (10-s scan) for scan durations below 2 min without DL, whereas every single image restored with DL was assessable. The diagnostic confidence continuously declined with shorter scan durations without DL, whereas diagnostic confidence only negligibly changed with DL (intermediate-to-high confidence ratings: 0%-54% vs. 80%-84%; 83% for the 10-min scan). The diagnostic accuracy of PET reads dropped from 90% to 4% without and remained high with DL (90%-93%; 90% for the 10-min scan). Conclusion: Our study demonstrates the compelling performance of DL to restore cerebral [18F]FDG PET datasets with ultra-low-count statistics for quantitative regional, voxel-based group, and clinical visual analyses. Consequently, DL enables a dramatic reduction of scan durations or administered activities (e.g., 10-min scan with 3.5 MBq, equivalent to ∼60 µSv) for [18F]FDG PET in patients with dementia and possibly other indications.

PurposeDespite the revealed role of immunological dysfunctions in the development and progression of Alzheimer’s disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-β (Aβ) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aβ PET imaging in clinical AD cohort.MethodsWe analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aβ ([18F]AV-45) within the prospective Alzheimer’s Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aβ PET imaging.ResultsTSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aβ deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =—0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aβ deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =—54.01, P = 0.003).ConclusionBy integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aβ deposition and cognitive dysfunction.

The purpose of this study was to determine the sensitivity, specificity, and clinical utility of 18F 2-fluoro-2-deoxy-D-glucose (FDG) total-body positron emission tomography (PET) scanning for the detection of metastases in patients with malignant melanoma. Recent preliminary reports suggest that PET using FDG may be more sensitive and specific for detection of metastatic melanoma than standard radiologic imaging studies using computed tomography (CT). PET technology is showing utility in the detection of metastatic tumors from multiple primary sites including breast, lung, lymphoma, and melanoma. However, little information is available concerning the general utility, sensitivity, and specificity of PET scanning of patients with metastatic melanoma. One hundred three PET scans done on 76 nonrandomized patients having AJCC stage II to IV melanoma were prospectively evaluated. Patients were derived from two groups. Group 1 (63 patients) had PET, CT (chest and abdomen), and magnetic resonance imaging (MRI; brain) scans as a part of staging requirements for immunotherapy protocols. Group 2 (13 nonprotocol patients) had PET, CT, and MRI scans as in group 1, but for clinical evaluation only. PET scans were done using 12 to 20 mCi of FDG given intravenously. Results of PET scans were compared to CT scans and biopsy or cytology results. PET scanning for the detection of melanoma metastases had a sensitivity of 94.2% and a specificity of 83.3% compared to 55.3% and 84.4%, respectively, for CT scanning. Factors that produced false-positive PET scans were papillary carcinoma of the thyroid (1), bronchogenic carcinoma (1), inflamed epidermal cyst (1), Warthin's tumor of the parotid gland (1), surgical wound inflammation (2), leiomyoma of the uterus (1), suture granuloma (1), and endometriosis (1). The four false-negative scans were thought to be due to smaller (<0.3 to 0.5 cm) and diffuse areas of melanoma without a mass effect. PET scanning is extremely sensitive (94.2%) and very specific (83.3%) for identifying metastatic melanoma, particularly in soft tissues, lymph nodes, and the liver. A number of second primary or metastatic tumors and an inflammatory response can also be localized by PET. This observation mandates a close clinical correlation with positive PET and emphasizes the importance of establishing a tissue diagnosis. False-negative scans in the presence of metastases are rare (4% of scans). Metastases < or =5 mm in diameter may not image well. PET is superior to CT in detecting melanoma metastases and has a role as a primary strategy in the staging of melanoma.

Purpose: Background: Colonic polyps are infrequently identified as an incidental finding on F-18 fluorodeoxyglucose positron emission tomography (PET) scan. We present 2 patients with large colonic polyps detected during whole-body PET for evaluation of lung nodules. Cases: An 82 year old female had a PET scan revealing focal uptake which correlated with a thickened loop of bowel. The standardized uptake value (SUV) in this area was as high as 6.5g/mL. Her previous colonoscopy 5 years prior was normal. Colonoscopy 3 months after the scan revealed a 40 mm sessile, carpet-like polyp in the transverse colon. Pathology reported a tubular adenoma with high grade dysplasia without invasion. During three sessions over 9 months, endoscopic mucosal resection (EMR) was performed using saline and epinephrine (1:10,000 mixture) for a lift-technique with piecemeal resection and argon plasma coagulation to treat the remaining tissue. Follow-up PET scan after 12 months revealed no metabolic activity in the colon. Repeat colonoscopy 5 months after the third resection demonstrated no lesions at the previous site, and multiple biopsies showed normal colonic mucosa. A 79 year old male had a PET scan showing focal uptake seen in the distal transverse colon with an SUV of 12.6 g/mL. Colonoscopy one month later revealed a 30 mm sessile, carpet-like polyp. Pathology reported a tubulovillous adenoma. EMR was performed with a combination of saline, epinephrine (1:10,000 mixture) and methylene blue for a lift-technique using a hot snare. The lesion was removed using a piecemeal resection. Argon plasma coagulation was used on the remaining tissue. PET scan for lung cancer 4 months later reported no metabolic activity in the colon. The patient declined colonoscopy after the normal PET. Discussion: PET is being increasingly used for surveillance after cancer treatment. This modality can incidentally detect pre-malignant lesions in the colon as well, with a sensitivity and specificity of nearly 75% and 80%, respectively. Accuracy is improved with larger size and higher grade of dysplasia of the lesion. It is a poor colorectal cancer screening tool, however, due to its limitations such as high cost and radiation exposure. EMR during colonoscopy can remove large, sessile polyps, preventing progression to malignancy. It is a desirable modality in patients with multiple comorbidities who are poor surgical candidates. These cases demonstrate the ability of colonoscopy with EMR to eradicate lesions identified by focal colonic uptake on PET.

Back pain in primary care: predictors of high health-care costs

Several recent studies have demonstrated the low yield of anatomically based computed tomography scans in evaluating Stage III (American Joint Committee on Cancer) patients with malignant melanoma. The purpose of this study was to investigate the efficacy and clinical utility of functionally based positron emission tomography (PET) scans in the same patient population. A prospective evaluation of 106 whole body PET scans obtained after injection of 2-fluorine-18, 2-fluoro-2-deoxy-D-glucose (FDG) was performed in 95 patients with clinically evident Stage III lymph node and/or in-transit melanoma. Areas of abnormality on FDG PET scanning were identified visually as foci of increased metabolic activity compared with background, and all positive foci were assessed pathologically. In this patient population, there were 234 areas that were evaluated pathologically of which 165 were confirmed histologically to be melanoma. PET scanning identified 144 of the 165 areas of melanoma for a sensitivity of 87.3%. The 21 areas of melanoma that were missed included 10 microscopic foci, 9 foci less than 1 cm, and 2 foci greater than 1 cm. There were 39 areas of increased PET activity that were not associated with malignancy for a 78.6% predictive value of a positive test. Of the 39 false-positive areas (false-positive rate of 56.5%), 13 could be attributed to recent surgery, 3 to arthritis, 3 to infection, 2 to superficial phlebitis, 1 to a benign skin nevus, and 1 to a colonic polyp. Pathologic evaluation of the remaining false-positive areas failed to reveal a definitive etiology for their increased activity on PET scan. With the application of pertinent clinical information, the predictive value of a positive PET scan could be improved to 90. 6%. The specificity of PET scanning in this study was only 43.5% because very few prophylactic lymph node dissections were performed. Thirty-six of the total 183 abnormal areas (19.7%) on PET scanning proved to be unsuspected areas of metastatic disease. These findings led to a change in the planned clinical management in patients after 16 of the 106 PET scans (15.1%). FDG PET scanning can be helpful in managing patients with Stage III melanoma in whom further surgery is contemplated. Although false-positive areas are not uncommon, PET scans did change the management of patients 15% of the time. PET's inability to identify microscopic disease suggests that it is of limited use in evaluating patients with Stage I-II disease.