Brain perfusion SPECT in dementia: what radiologists should know.

The findings of brain perfusion single-photon emission computed tomography (SPECT), which detects abnormalities often before changes manifest in morphological imaging, mainly reflect neurodegeneration and contribute to dementia evaluation. A major shift is about to occur in dementia practice to the approach of diagnosing based on biomarkers and treating with disease-modifying drugs. Accordingly, brain perfusion SPECT will be required to serve as a biomarker of neurodegeneration. Hypoperfusion in Alzheimer's disease (AD) is typically seen in the posterior cingulate cortex and precuneus early in the disease, followed by the temporoparietal cortices. On the other hand, atypical presentations of AD such as the posterior variant, logopenic variant, frontal variant, and corticobasal syndrome exhibit hypoperfusion in areas related to symptoms. Additionally, hypoperfusion especially in the precuneus and parietal association cortex can serve as a predictor of progression from mild cognitive impairment to AD. In dementia with Lewy bodies (DLB), the differentiating feature is the presence of hypoperfusion in the occipital lobes in addition to that observed in AD. Hypoperfusion of the occipital lobe is not a remarkable finding, as it is assumed to reflect functional loss due to impairment of the cholinergic and dopaminergic systems rather than degeneration per se. Moreover, the cingulate island sign reflects the degree of AD pathology comorbid in DLB. Frontotemporal dementia is characterized by regional hypoperfusion according to the three clinical types, and the background pathology is diverse. Idiopathic normal pressure hydrocephalus shows apparent hypoperfusion around the Sylvian fissure and corpus callosum and apparent hyperperfusion in high-convexity areas. The cortex or striatum with diffusion restriction on magnetic resonance imaging in prion diseases reflects spongiform degeneration and brain perfusion SPECT reveals hypoperfusion in the same areas. Brain perfusion SPECT findings in dementia should be carefully interpreted considering background pathology.

Deep learning model with collage images for the segmentation of dedicated breast positron emission tomography images.

BACKGROUND: Dedicated breast positron emission tomography (dbPET) has high contrast and resolution optimized for detecting small breast cancers, leading to its noisy characteristics. This study evaluated the application of deep learning to the automatic segmentation of abnormal uptakes on dbPET to facilitate the assessment of lesions. To address data scarcity in model training, we used collage images composed of cropped abnormal uptakes and normal breasts for data augmentation. METHODS: This retrospective study included 1598 examinations between April 2015 and August 2020. A U-Net-based model with an uptake shape classification head was trained using either the original or augmented dataset comprising collage images. The Dice score, which measures the pixel-wise agreement between a prediction and its ground truth, of the models was compared using the Wilcoxon signed-rank test. Moreover, the classification accuracies were evaluated. RESULTS: After applying the exclusion criteria, 662 breasts were included; among these, 217 breasts had abnormal uptakes (mean age: 58 ± 14 years). Abnormal uptakes on the cranio-caudal and mediolateral maximum intensity projection images of 217 breasts were annotated and labeled as focus, mass, or non-mass. The inclusion of collage images into the original dataset yielded a Dice score of 0.884 and classification accuracy of 91.5%. Improvement in the Dice score was observed across all subgroups, and the score of images without breast cancer improved significantly from 0.750 to 0.834 (effect size: 0.76, P = 0.02). CONCLUSIONS: Deep learning can be applied for the automatic segmentation of dbPET, and collage images can improve model performance.

Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.

This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1−5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts.

Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography.

PURPOSE: To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic performance of radiologists. MATERIALS AND METHODS: We retrospectively gathered 300 images of normal and 328 images of axillary lymph nodes with breast cancer metastases for training. A DL model using the CNN architecture Xception was developed to analyze test data of 50 normal and 50 metastatic lymph nodes. A board-certified radiologist with 12 years' experience. (Reader 1) and two residents with 3- and 1-year experience (Readers 2, 3), respectively, scored these test data with and without the assistance of the DL system for the possibility of metastasis. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS: Our DL model had a sensitivity of 94%, a specificity of 88%, and an AUC of 0.966. The AUC of the DL model was not significantly different from that of Reader 1 (0.969; p = 0.881) and higher than that of Reader 2 (0.913; p = 0.101) and Reader 3 (0.810; p < 0.001). With the DL support, the AUCs of Readers 2 and 3 increased to 0.960 and 0.937, respectively, which were comparable to those of Reader 1 (p = 0.138 and 0.700, respectively). CONCLUSION: Our DL model demonstrated great diagnostic performance for differentiating benign from malignant axillary lymph nodes on breast ultrasound and for potentially providing effective diagnostic support to residents.

Deep learning for image classification in dedicated breast positron emission tomography (dbPET).

OBJECTIVE: This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images. METHODS: Of the 1598 women who underwent dbPET examination between April 2015 and August 2020, a total of 618 breasts on 309 examinations for 284 women who were diagnosed with BC or non-BC were analyzed in this retrospective study. The Xception-based DL model was trained to predict BC or non-BC using dbPET images from 458 breasts of 109 BCs and 349 non-BCs, which consisted of mediallateral and craniocaudal maximum intensity projection images, respectively. It was tested using dbPET images from 160 breasts of 43 BC and 117 non-BC. Two expert radiologists and two radiology residents also interpreted them. Sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were calculated. RESULTS: Our DL model had a sensitivity and specificity of 93% and 93%, respectively, while radiologists had a sensitivity and specificity of 77-89% and 79-100%, respectively. Diagnostic performance of our model (AUC = 0.937) tended to be superior to that of residents (AUC = 0.876 and 0.868, p = 0.073 and 0.073), although not significantly different. Moreover, no significant differences were found between the model and experts (AUC = 0.983 and 0.941, p = 0.095 and 0.907). CONCLUSIONS: Our DL model could be applied to dbPET and achieve the same diagnostic ability as that of experts.

Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer.

Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80-98% and a specificity of 80%, 88%, and 76-92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872-0.967, p = 0.036-0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future.

Xanthogranulomatous Pancreatitis Accompanied by Xanthogranulomatous Cholecystitis: A Case Report and Literature Review.

ABSTRACT: Xanthogranulomatous pancreatitis (XGP) is extremely rare, with only 31 cases reported in the English literature to date. We reviewed previously reported 17 articles about XGP and report an additional case of XGP. This is the first case of XGP with xanthogranulomatous cholecystitis accompanied by intraductal papillary mucinous carcinoma (IPMC) in an 80-year-old woman. She was referred to our hospital with jaundice and general malaise and was found to have a cystic mass at the pancreatic head and a solid mass at the pancreatic tail, with dilation of both the main pancreatic duct and biliary tract noted on computed tomography. Diagnosis of IPMC at the pancreatic head with neuroendocrine tumor at the pancreatic tail was made, and the patient underwent subtotal stomach-preserving pancreatoduodenectomy with enucleation of the mass at the tail. Pathological examination revealed IPMC with xanthogranulomatous changes around the IPMC and at the pancreatic tail and gallbladder. Xanthogranulomatous pancreatitis could be induced by inflammatory reaction due to obstruction of the pancreatic duct and biliary tract by mucin produced in the IPMC. It is sometimes difficult to preoperatively differentiate XGP from malignant pancreatic tumors, such as pancreatic carcinoma or neuroendocrine tumor, using imaging studies.