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Purpose: Large language models (LLMs) are rapidly advancing and demonstrating high performance in understanding textual information, suggesting potential applications in interpreting patient histories and documented imaging findings. As LLMs continue to improve, their diagnostic abilities are expected to be enhanced further. However, there is a lack of comprehensive comparisons between LLMs from different manufacturers. In this study, we aimed to test the diagnostic performance of the three latest major LLMs (GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro) using Radiology Diagnosis Please Cases, a monthly diagnostic quiz series for radiology experts., Materials and Methods: Clinical history and imaging findings, provided textually by the case submitters, were extracted from 324 quiz questions originating from Radiology Diagnosis Please cases published between 1998 and 2023. The top three differential diagnoses were generated by GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, using their respective application programming interfaces. A comparative analysis of diagnostic performance among these three LLMs was conducted using Cochrane's Q and post hoc McNemar's tests., Results: The respective diagnostic accuracies of GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro for primary diagnosis were 41.0%, 54.0%, and 33.9%, which further improved to 49.4%, 62.0%, and 41.0%, when considering the accuracy of any of the top three differential diagnoses. Significant differences in the diagnostic performance were observed among all pairs of models., Conclusion: Claude 3 Opus outperformed GPT-4o and Gemini 1.5 Pro in solving radiology quiz cases. These models appear capable of assisting radiologists when supplied with accurate evaluations and worded descriptions of imaging findings., (© 2024. The Author(s).)

Purpose: Many large radiographic datasets of lung nodules are available, but the small and hard-to-detect nodules are rarely validated by computed tomography. Such difficult nodules are crucial for training nodule detection methods. This lack of difficult nodules for training can be addressed by artificial nodule synthesis algorithms, which can create artificially embedded nodules. This study aimed to develop and evaluate a novel cost function for training networks to detect such lesions. Embedding artificial lesions in healthy medical images is effective when positive cases are insufficient for network training. Although this approach provides both positive (lesion-embedded) images and the corresponding negative (lesion-free) images, no known methods effectively use these pairs for training. This paper presents a novel cost function for segmentation-based detection networks when positive-negative pairs are available., Methods: Based on the classic U-Net, new terms were added to the original Dice loss for reducing false positives and the contrastive learning of diseased regions in the image pairs. The experimental network was trained and evaluated, respectively, on 131,072 fully synthesized pairs of images simulating lung cancer and real chest X-ray images from the Japanese Society of Radiological Technology dataset., Results: The proposed method outperformed RetinaNet and a single-shot multibox detector. The sensitivities were 0.688 and 0.507 when the number of false positives per image was 0.2, respectively, with and without fine-tuning under the leave-one-case-out setting., Conclusion: To our knowledge, this is the first study in which a method for detecting pulmonary nodules in chest X-ray images was evaluated on a real clinical dataset after being trained on fully synthesized images. The synthesized dataset is available at https://zenodo.org/records/10648433 ., (© 2024. The Author(s).)

Purpose: Transarterial radioembolization (TARE) is effective for unresectable hepatocellular carcinoma; however, it awaits approval in Japan. This study aimed to simulate the cost-effectiveness of TARE over chemoembolization when TARE is approved in Japan and identify the requirements for cost-effectiveness., Materials and Methods: A Markov model was constructed to analyze the costs and effectiveness associated with TARE and transarterial chemoembolization with drug-eluting beads (DEB-TACE) for 2-month cycles over 5 years. In the primary analysis, the intention-to-treat survival data were used to calculate transition probabilities, whereas the ancillary analysis assessed the per-protocol survival data. DEB-TACE costs were calculated using the Japanese nationwide claims Diagnosis Procedure Combination database between April 2018 and March 2022, whereas TARE costs were estimated using database and international sources. The incremental cost-effectiveness ratio (ICER) was determined based on the payer's perspective and compared with the Japanese willingness-to-pay threshold of 5 million Japanese yen (JPY) (31,250 USD) per quality-adjusted life years (QALY)., Results: From the claims database, 6,986 patients with hepatocellular carcinoma who received DEB-TACE were identified. In the primary analysis, the ICER was 5,173,591 JPY (32,334 USD)/QALY, surpassing the Japanese willingness-to-pay threshold. However, the ancillary analysis showed a lower ICER of 4,156,533 JPY (25,978 USD)/QALY, falling below the threshold. The one-way deterministic sensitivity analysis identified progression-free survival associated with TARE and DEB-TACE, DEB-TACE costs, and radioactive microsphere reimbursement price as key ICER influencers. The primary analysis suggested that setting the reimbursement price of radioactive microspheres below 1.399 million JPY (8,744 USD), approximately 2.8% lower than the price in the United Kingdom, would place the ICER below the Japanese willingness-to-pay threshold., Conclusions: Under specific conditions, TARE can be a more cost-effective treatment than DEB-TACE. If the reimbursement price of radioactive microspheres is set approximately 2.8% lower than that in the United Kingdom, TARE could be cost-effective compared with DEB-TACE., (© 2024. The Author(s).)

Purpose: To compare computed tomography (CT) pulmonary angiography and unenhanced CT to determine the effect of rapid iodine contrast agent infusion on tracheal diameter and lung volume., Material and Methods: This retrospective study included 101 patients who underwent CT pulmonary angiography and unenhanced CT, for which the time interval between them was within 365 days. CT pulmonary angiography was scanned 20 s after starting the contrast agent injection at the end-inspiratory level. Commercial software, which was developed based on deep learning technique, was used to segment the lung, and its volume was automatically evaluated. The tracheal diameter at the thoracic inlet level was also measured. Then, the ratios for the CT pulmonary angiography to unenhanced CT of the tracheal diameter (TD PAU ) and both lung volumes (BLV PAU ) were calculated., Results: Tracheal diameter and both lung volumes were significantly smaller in CT pulmonary angiography (17.2 ± 2.6 mm and 3668 ± 1068 ml, respectively) than those in unenhanced CT (17.7 ± 2.5 mm and 3887 ± 1086 ml, respectively) (p < 0.001 for both). A statistically significant correlation was found between TD PAU and BLV PAU with a correlation coefficient of 0.451 (95% confidence interval, 0.280-0.594) (p < 0.001). No factor showed a significant association with TD PAU . The type of contrast agent had a significant association for BLV PAU (p = 0.042)., Conclusions: Rapid infusion of iodine contrast agent reduced the tracheal diameter and both lung volumes in CT pulmonary angiography, which was scanned at end-inspiratory level, compared with those in unenhanced CT., (© 2024. The Author(s).)

Purpose: The diagnostic performance of large language artificial intelligence (AI) models when utilizing radiological images has yet to be investigated. We employed Claude 3 Opus (released on March 4, 2024) and Claude 3.5 Sonnet (released on June 21, 2024) to investigate their diagnostic performances in response to the Radiology's Diagnosis Please quiz questions., Materials and Methods: In this study, the AI models were tasked with listing the primary diagnosis and two differential diagnoses for 322 quiz questions from Radiology's "Diagnosis Please" cases, which included cases 1 to 322, published from 1998 to 2023. The analyses were performed under the following conditions: (1) Condition 1: submitter-provided clinical history (text) alone. (2) Condition 2: submitter-provided clinical history and imaging findings (text). (3) Condition 3: clinical history (text) and key images (PNG file). We applied McNemar's test to evaluate differences in the correct response rates for the overall accuracy under Conditions 1, 2, and 3 for each model and between the models., Results: The correct diagnosis rates were 58/322 (18.0%) and 69/322 (21.4%), 201/322 (62.4%) and 209/322 (64.9%), and 80/322 (24.8%) and 97/322 (30.1%) for Conditions 1, 2, and 3 for Claude 3 Opus and Claude 3.5 Sonnet, respectively. The models provided the correct answer as a differential diagnosis in up to 26/322 (8.1%) for Opus and 23/322 (7.1%) for Sonnet. Statistically significant differences were observed in the correct response rates among all combinations of Conditions 1, 2, and 3 for each model (p < 0.01). Claude 3.5 Sonnet outperformed in all conditions, but a statistically significant difference was observed only in the comparison for Condition 3 (30.1% vs. 24.8%, p = 0.028)., Conclusion: Two AI models demonstrated a significantly improved diagnostic performance when inputting both key images and clinical history. The models' ability to identify important differential diagnoses under these conditions was also confirmed., (© 2024. The Author(s).)

Purpose: The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience., Methods: We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists., Results: The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist., Conclusions: Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used., (© 2024. The Author(s).)

Purpose: To assess the performance of GPT-4 Turbo with Vision (GPT-4TV), OpenAI's latest multimodal large language model, by comparing its ability to process both text and image inputs with that of the text-only GPT-4 Turbo (GPT-4 T) in the context of the Japan Diagnostic Radiology Board Examination (JDRBE)., Materials and Methods: The dataset comprised questions from JDRBE 2021 and 2023. A total of six board-certified diagnostic radiologists discussed the questions and provided ground-truth answers by consulting relevant literature as necessary. The following questions were excluded: those lacking associated images, those with no unanimous agreement on answers, and those including images rejected by the OpenAI application programming interface. The inputs for GPT-4TV included both text and images, whereas those for GPT-4 T were entirely text. Both models were deployed on the dataset, and their performance was compared using McNemar's exact test. The radiological credibility of the responses was assessed by two diagnostic radiologists through the assignment of legitimacy scores on a five-point Likert scale. These scores were subsequently used to compare model performance using Wilcoxon's signed-rank test., Results: The dataset comprised 139 questions. GPT-4TV correctly answered 62 questions (45%), whereas GPT-4 T correctly answered 57 questions (41%). A statistical analysis found no significant performance difference between the two models (P = 0.44). The GPT-4TV responses received significantly lower legitimacy scores from both radiologists than the GPT-4 T responses., Conclusion: No significant enhancement in accuracy was observed when using GPT-4TV with image input compared with that of using text-only GPT-4 T for JDRBE questions., (© 2024. The Author(s).)

Purpose: Standardized uptake values (SUVs) derived from 18 F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography are a crucial parameter for identifying tumors or abnormalities in an organ. Moreover, exploring ways to improve the identification of tumors or abnormalities using a statistical measurement tool is important in clinical research. Therefore, we developed a fully automatic method to create a personally normalized Z-score map of the liver SUV., Methods: The normalized Z-score map for each patient was created using the SUV mean and standard deviation estimated from blood-test-derived variables, such as alanine aminotransferase and aspartate aminotransferase, as well as other demographic information. This was performed using the least absolute shrinkage and selection operator (LASSO)-based estimation formula. We also used receiver operating characteristic (ROC) to analyze the results of people with and without hepatic tumors and compared them to the ROC curve of normal SUV., Results: A total of 7757 people were selected for this study. Of these, 7744 were healthy, while 13 had abnormalities. The area under the ROC curve results indicated that the anomaly detection approach (0.91) outperformed only the maximum SUV (0.89). To build the LASSO regression, sets of covariates, including sex, weight, body mass index, blood glucose level, triglyceride, total cholesterol, γ-glutamyl transpeptidase, total protein, creatinine, insulin, albumin, and cholinesterase, were used to determine the SUV mean, whereas weight was used to determine the SUV standard deviation., Conclusion: The Z-score normalizes the mean and standard deviation. It is effective in ROC curve analysis and increases the clarity of the abnormality. This normalization is a key technique for effective measurement of maximum glucose consumption by tumors in the liver., (© 2024. The Author(s).)

Postmortem computed tomography (CT) is an essential tool for investigating the causes of death. Postmortem CT has characteristic imaging findings and should not be interpreted in the same manner as clinical antemortem images. In analyzing the cause of death in in-hospital death cases using postmortem images, it is crucial to understand early postmortem and post-resuscitation changes. In addition, it is essential to understand the limitations of diagnosing the cause of death or significant pathology relating to death on non-contrast-enhanced postmortem CT. In Japan, there has also been social demand to establish a system for postmortem imaging at the time of death. To facilitate such a system, clinical radiologists should be prepared to interpret postmortem images and assess the cause of death. This review article provides comprehensive information regarding unenhanced postmortem CT for in-hospital death cases in daily clinical practice in Japan., (© 2023. The Author(s).)

Hypophysitis is an inflammatory disease affecting the pituitary gland. Hypophysitis can be classified into multiple types depending on the mechanisms (primary or secondary), histology (lymphocytic, granulomatous, xanthomatous, plasmacytic/IgG4 related, necrotizing, or mixed), and anatomy (adenohypophysitis, infundibulo-neurohypophysitis, or panhypophysitis). An appropriate diagnosis is vital for managing these potentially life-threatening conditions. However, physiological morphological alterations, remnants, and neoplastic and non-neoplastic lesions may masquerade as hypophysitis, both clinically and radiologically. Neuroimaging, as well as imaging findings of other sites of the body, plays a pivotal role in diagnosis. In this article, we will review the types of hypophysitis and summarize clinical and imaging features of both hypophysitis and its mimickers., (© 2023. The Author(s).)

Purpose: Biliary adenofibroma is a solid microcystic epithelial neoplasm in the liver, comprising microcystic and tubuloacinar glandular tissues lined by a non-mucin secreting biliary epithelium and supported by a fibrous stroma. It is an extremely rare benign tumor with potential for malignant transformation. Herein, we report the case of a 64-year-old woman diagnosed with intrahepatic cholangiocarcinoma arising from biliary adenofibroma., Methods: Imaging studies revealed a tumor of 50 mm diameter, consisting of two components in S1 of the liver. The ventral portion of the tumor showed an ill-defined mass with early peripheral and gradual centripetal enhancement invading to the middle hepatic vein on computed tomography (CT), diffusion restriction on magnetic resonance images, and high fluorine-18-2-deoxy-D-glucose (FDG) uptake on positron emission tomography, like conventional intrahepatic cholangiocarcinoma. The dorsal portion showed a well-defined and low-attenuated mass with heterogeneous early enhancement and partial wash-out on CT, marked hyperintensity on heavily T2-weighted images, and low FDG uptake. The patient subsequently underwent extended left hepatectomy., Results: Pathologically, the former was diagnosed as cholangiocarcinoma and the latter as biliary adenofibroma. We discuss the radiological-pathological correlation of the tumor with a literature review., Conclusion: Preoperative diagnosis of biliary adenofibroma is extremely challenging; however, clinically, it is crucial not to miss the presence of malignant findings., (© 2023. The Author(s).)

Purpose: The aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip prostheses and compare it with DLR and hybrid iterative reconstruction (IR) with SEMAR (IR-S)., Materials and Methods: This retrospective study included 26 patients (mean age 68.6 ± 16.6 years, with 9 males and 17 females) with metal hip prostheses who underwent a CT examination including the pelvis. Axial pelvic CT images were reconstructed using DLR-S, DLR, and IR-S. In one-by-one qualitative analyses, two radiologists evaluated the degree of metal artifacts, noise, and pelvic structure depiction. In side-by-side qualitative analyses (DLR-S vs. IR-S), the two radiologists evaluated metal artifacts and overall quality. By placing regions of interest on the bladder and psoas muscle, the standard deviations of their CT attenuation were recorded, and the artifact index was calculated based on them. Results were compared between DLR-S vs. DLR and DLR vs. IR-S using the Wilcoxon signed-rank test., Results: In one-by-one qualitative analyses, metal artifacts and structure depiction in DLR-S were significantly better than those in DLR; however, between DLR-S and IR-S, significant differences were noted only for reader 1. Image noise in DLR-S was rated as significantly reduced compared with that in IR-S by both readers. In side-by-side analyses, both readers rated that the DLR-S images are significantly better than IR-S images regarding overall image quality and metal artifacts. The median (interquartile range) of the artifact index for DLR-S was 10.1 (4.4-16.0) and was significantly better than those for DLR (23.1, 6.5-36.1) and IR-S (11.4, 7.8-17.9)., Conclusion: DLR-S provided better pelvic CT images in patients with metal hip prostheses than IR-S and DLR., (© 2023. The Author(s).)

Purpose: This study aimed to compare the hepatocellular carcinoma (HCC) detection performance, interobserver agreement for Liver Imaging Reporting and Data System (LI-RADS) categories, and image quality between deep learning reconstruction (DLR) and conventional hybrid iterative reconstruction (Hybrid IR) in CT., Methods: This retrospective study included patients who underwent abdominal dynamic contrast-enhanced CT between October 2021 and March 2022. Arterial, portal, and delayed phase images were reconstructed using DLR and Hybrid IR. Two blinded readers independently read the image sets with detecting HCCs, scoring LI-RADS, and evaluating image quality., Results: A total of 26 patients with HCC (mean age, 73 years ± 12.3) and 23 patients without HCC (mean age, 66 years ± 14.7) were included. The figures of merit (FOM) for the jackknife alternative free-response receiver operating characteristic analysis in detecting HCC averaged for the readers were 0.925 (reader 1, 0.937; reader 2, 0.913) in DLR and 0.878 (reader 1, 0.904; reader 2, 0.851) in Hybrid IR, and the FOM in DLR were significantly higher than that in Hybrid IR (p = 0.038). The interobserver agreement (Cohen's weighted kappa statistics) for LI-RADS categories was moderate for DLR (0.595; 95% CI, 0.585-0.605) and significantly superior to Hybrid IR (0.568; 95% CI, 0.553-0.582). According to both readers, DLR was significantly superior to Hybrid IR in terms of image quality (p ≤ 0.021)., Conclusion: DLR improved HCC detection, interobserver agreement for LI-RADS categories, and image quality in evaluations of HCC compared to Hybrid IR in abdominal dynamic contrast-enhanced CT., (© 2023. The Author(s).)

Triplet repeat diseases (TRDs) refer to a group of diseases caused by three nucleotide repeats elongated beyond a pathologic threshold. TRDs are divided into the following four groups depending on the pathomechanisms, although the pathomechanisms of several diseases remain unelucidated: polyglutamine disorders, caused by a pathologic repeat expansion of CAG (coding the amino acid glutamine) located within the exon; loss-of-function repeat disorders, characterized by the common feature of a loss of function of the gene within which they occur; RNA gain-of-function disorders, involving the production of a toxic RNA species; and polyalanine disorders, caused by a pathologic repeat expansion of GCN (coding the amino acid alanine) located within the exon. Many of these TRDs manifest through neurologic symptoms; moreover, neuroimaging, especially brain magnetic resonance imaging, plays a pivotal role in the detection of abnormalities, differentiation, and management of TRDs. In this article, we reviewed the clinical and neuroimaging features of TRDs. An early diagnosis of TRDs through clinical and imaging approaches is important and may contribute to appropriate medical intervention for patients and their families., (© 2022. The Author(s).)

Purpose: To explore the CT findings and pneumonnia progression pattern of the Alpha and Delta variants of SARS-CoV-2 by comparing them with the pre-existing wild type., Method: In this retrospective comparative study, a total of 392 patients with COVID-19 were included: 118 patients with wild type (70 men, 56.8 ± 20.7 years), 137 with Alpha variant (93 men, 49.4 ± 17.0 years), and 137 with Delta variant (94 men, 45.4 ± 12.4). Chest CT evaluation included opacities and repairing changes as well as lesion distribution and laterality. Chest CT severity score was also calculated. These parameters were statistically compared across the variants., Results: Ground glass opacity (GGO) with consolidation and repairing changes were more frequent in the order of Delta variant, Alpha variant, and wild type throughout the disease course. Delta variant showed GGO with consolidation more conspicuously than did the other two on days 1-4 (vs. wild type, Bonferroni corrected p = 0.01; vs. Alpha variant, Bonferroni corrected p = 0.003) and days 5-8 (vs. wild type, Bonferroni corrected p < 0.001; vs. Alpha variant, Bonferroni corrected-p = 0.003). Total lung CT severity scores of Delta variant were higher than those of wild type on days 1-4 and 5-8 (Bonferroni corrected p = 0.01 and Bonferroni corrected p = 0.005, respectively) and that of Alpha variant on days 1-4 (Bonferroni corrected p = 0.002). There was no difference in the CT findings between wild type and Alpha variant., Conclusions: Pneumonia progression of Delta variant may be more rapid and severe in the early stage than in the other two., (© 2022. The Author(s).)

Purpose: To develop an anomaly detection system in PET/CT with the tracer 18 F-fluorodeoxyglucose (FDG) that requires only normal PET/CT images for training and can detect abnormal FDG uptake at any location in the chest region., Materials and Methods: We trained our model based on a Bayesian deep learning framework using 1878 PET/CT scans with no abnormal findings. Our model learns the distribution of standard uptake values in these normal training images and detects out-of-normal uptake regions. We evaluated this model using 34 scans showing focal abnormal FDG uptake in the chest region. This evaluation dataset includes 28 pulmonary and 17 extrapulmonary abnormal FDG uptake foci. We performed per-voxel and per-slice receiver operating characteristic (ROC) analyses and per-lesion free-response receiver operating characteristic analysis., Results: Our model showed an area under the ROC curve of 0.992 on discriminating abnormal voxels and 0.852 on abnormal slices. Our model detected 41 of 45 (91.1%) of the abnormal FDG uptake foci with 12.8 false positives per scan (FPs/scan), which include 26 of 28 pulmonary and 15 of 17 extrapulmonary abnormalities. The sensitivity at 3.0 FPs/scan was 82.2% (37/45)., Conclusion: Our model trained only with normal PET/CT images successfully detected both pulmonary and extrapulmonary abnormal FDG uptake in the chest region., (© 2022. The Author(s).)

Purpose: Schwannomas in and around the porta hepatis (porta hepatic schwannomas) are rare benign tumors easily misdiagnosed as other pathologies, including malignancies. We aimed to evaluate their imaging features on ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and 18 F-fluorodeoxyglucose-positron emission tomography/CT (FDG-PET/CT)., Methods: We performed a multi-institutional retrospective study by reviewing the clinical and imaging findings of pathologically proven eight porta hepatic schwannomas (mean age, 55 years; range, 38-80 years; one male and seven females). Preoperative imaging included three ultrasonography, eight CT, eight MRI, and two FDG-PET/CT., Results: All patients were asymptomatic. The mean tumor size was 61.9 mm (range, 30-180 mm), and all tumors demonstrated well-defined lesions on ultrasonography and their solid components showed soft tissue attenuation on non-contrast CT. MRI showed two distinct components in all cases: the component with T1-weighted hypointensities and T2-weighted hyperintensities with poor enhancement (suggestive of Antoni B histology); the component with T2-weighted hypointensities with gradually increasing enhancement (suggestive of Antoni A histology), resulting in a heterogeneous pattern on post-contrast CT or MRI (8/8, 100%). The separated deviation of surrounding bile ducts and vessels without obstruction allowed the recognition of extrahepatic localization and their benign nature. A ginger root-like morphology (2/8, 25%) seemed to be suggestive of extension along the Glisson's sheath, although this finding was not seen frequently., Conclusion: Recognizing imaging features such as extrahepatic location, benign nature with internal structures suggestive of Antoni A/B histology, and characteristic tumor extension may provide key diagnostic clues for porta hepatic schwannomas., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: The purpose of this study was to evaluate whether deep learning reconstruction (DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T., Materials and Methods: In this retrospective study, MRA images of 40 patients (21 males and 19 females; mean age, 65.8 ± 13.2 years) were reconstructed with and without the DLR technique (DLR image and non-DLR image, respectively). Quantitative image analysis was performed by placing regions of interest on the basilar artery and cerebrospinal fluid in the prepontine cistern. We calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for analyses of the basilar artery. Two experienced radiologists evaluated the depiction of structures (the right internal carotid artery, right ophthalmic artery, basilar artery, and right superior cerebellar artery), artifacts, subjective noise and overall image quality in a qualitative image analysis. Scores were compared in the quantitative and qualitative image analyses between the DLR and non-DLR images using Wilcoxon signed-rank tests., Results: The SNR and CNR for the basilar artery were significantly higher for the DLR images than for the non-DLR images (p < 0.001). Qualitative image analysis scores (p < 0.003 and p < 0.005 for readers 1 and 2, respectively), excluding those for artifacts (p = 0.072-0.565), were also significantly higher for the DLR images than for the non-DLR images., Conclusion: DLR enables the production of higher quality 1.5 T intracranial MRA images with improved visualization of arteries., (© 2021. The Author(s).)

Purpose: Radiologists interpret many medical images and clinical practice demands timely interpretation, resulting in a heavy workload. To reduce the workload, here we formulate and validate a method that can handle different types of medical image and can detect virtually all types of lesion in a medical image. For the first time, we show that two flow-based deep generative (FDG) models can predict the logarithm posterior probability in a semi-supervised approach., Methods: We adopt two FDG models in conjunction with Bayes' theorem to predict the logarithm posterior probability that a medical image is normal. We trained one of the FDG models with normal images and the other FDG model with normal and non-normal images., Results: We validated the method using two types of medical image: chest X-ray images (CXRs) and brain computed tomography images (BCTs). The area under the receiver operating characteristic curve for pneumonia-like opacities in CXRs was 0.839 on average, and for infarction in BCTs was 0.904., Conclusion: We formulated a method of predicting the logarithm posterior probability using two FDG models. We validated that the method can detect abnormal findings in CXRs and BCTs with both an acceptable performance for testing and a comparatively light workload for training., (© 2021. CARS.)

Coronavirus disease 2019 (COVID-19) pandemic has posed a major public health crisis all over the world. The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. In the early stage of the pandemic, the performance of chest imaging for COVID-19 has widely been debated especially in the context of comparison to real-time reverse transcription polymerase chain reaction. Current evidence is against the use of chest imaging for routine screening of COVID-19 contrary to the initial expectations. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes. In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed., (© 2021. The Author(s).)

Purpose: The three-dimensional (3D) voxel labeling of lesions requires significant radiologists' effort in the development of computer-aided detection software. To reduce the time required for the 3D voxel labeling, we aimed to develop a generalized semiautomatic segmentation method based on deep learning via a data augmentation-based domain generalization framework. In this study, we investigated whether a generalized semiautomatic segmentation model trained using two types of lesion can segment previously unseen types of lesion., Methods: We targeted lung nodules in chest CT images, liver lesions in hepatobiliary-phase images of Gd-EOB-DTPA-enhanced MR imaging, and brain metastases in contrast-enhanced MR images. For each lesion, the 32 × 32 × 32 isotropic volume of interest (VOI) around the center of gravity of the lesion was extracted. The VOI was input into a 3D U-Net model to define the label of the lesion. For each type of target lesion, we compared five types of data augmentation and two types of input data., Results: For all considered target lesions, the highest dice coefficients among the training patterns were obtained when using a combination of the existing data augmentation-based domain generalization framework and random monochrome inversion and when using the resized VOI as the input image. The dice coefficients were 0.639 ± 0.124 for the lung nodules, 0.660 ± 0.137 for the liver lesions, and 0.727 ± 0.115 for the brain metastases., Conclusions: Our generalized semiautomatic segmentation model could label unseen three types of lesion with different contrasts from the surroundings. In addition, the resized VOI as the input image enables the adaptation to the various sizes of lesions even when the size distribution differed between the training set and the test set., (© 2021. CARS.)

Purpose: The performance of computer-aided detection (CAD) software depends on the quality and quantity of the dataset used for machine learning. If the data characteristics in development and practical use are different, the performance of CAD software degrades. In this study, we investigated changes in detection performance due to differences in training data for cerebral aneurysm detection software in head magnetic resonance angiography images., Materials and Methods: We utilized three types of CAD software for cerebral aneurysm detection in MRA images, which were based on 3D local intensity structure analysis, graph-based features, and convolutional neural network. For each type of CAD software, we compared three types of training pattern, which were two types of training using single-site data and one type of training using multisite data. We also carried out internal and external evaluations., Results: In training using single-site data, the performance of CAD software largely and unpredictably fluctuated when the training dataset was changed. Training using multisite data did not show the lowest performance among the three training patterns for any CAD software and dataset., Conclusion: The training of cerebral aneurysm detection software using data collected from multiple sites is desirable to ensure the stable performance of the software., (© 2021. Japan Radiological Society.)

Purpose: Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) has high diagnostic accuracy in the detection of liver lesions. There is a demand for computer-aided detection/diagnosis software for Gd-EOB-DTPA-enhanced MRI. We propose a deep learning-based method using one three-dimensional fully convolutional residual network (3D FC-ResNet) for liver segmentation and another 3D FC-ResNet for simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI., Methods: We prepared a five-phase (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary phases) series as the input image sets and labeled focal liver lesion (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scars) images as the output image sets. We used 100 cases to train our model, 42 cases to determine the hyperparameters of our model, and 42 cases to evaluate our model. We evaluated our model by free-response receiver operating characteristic curve analysis and using a confusion matrix., Results: Our model simultaneously detected and classified focal liver lesions. In the test cases, the detection accuracy for whole focal liver lesions had a true-positive ratio of 0.6 at an average of 25 false positives per case. The classification accuracy was 0.790., Conclusion: We proposed the simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI using multichannel 3D FC-ResNet. Our results indicated simultaneous detection and classification are possible using a single network. It is necessary to further improve detection sensitivity to help radiologists., (© 2021. CARS.)

Purpose: The clinical usefulness of computer-aided detection of cerebral aneurysms has been investigated using different methods to present lesion candidates, but suboptimal methods may have limited its usefulness. We compared three presentation methods to determine which can benefit radiologists the most by enabling them to detect more aneurysms., Materials and Methods: We conducted a multireader multicase observer performance study involving six radiologists and using 470 lesion candidates output by a computer-aided detection program, and compared the following three different presentation methods using the receiver operating characteristic analysis: (1) a lesion candidate is encircled on axial slices, (2) a lesion candidate is overlaid on a volume-rendered image, and (3) combination of (1) and (2). The response time was also compared., Results: As compared with axial slices, radiologists showed significantly better detection performance when presented with volume-rendered images. There was no significant difference in response time between the two methods. The combined method was associated with a significantly longer response time, but had no added merit in terms of diagnostic accuracy., Conclusion: Even with the aid of computer-aided detection, radiologists overlook many aneurysms if the presentation method is not optimal. Overlaying colored lesion candidates on volume-rendered images can help them detect more aneurysms.

Objectives: To evaluate the relationship between abnormal findings on abdomino-pelvic CT and adverse events in oncologic patients treated with lenvatinib, and their relationship with treatment planning., Methods: This single institutional retrospective study included 58 patients with unresectable hepatocellular carcinoma or unresectable thyroid carcinoma (mean age ± standard deviation 69.6 ± 10.0 years; range 39-84 years; 48 men) who underwent CT between October 2016 and July 2020. Two radiologists who were blinded to clinical information including the presence or absence of diarrhea evaluated the imaging findings, including the presence/absence of enteritis in each intestinal segment. Gastrointestinal adverse events (diarrhea, decreased appetite, nausea, and vomiting) and other drug-induced adverse events requiring treatment or follow-up during lenvatinib treatment were also investigated. The frequency of these adverse events was compared between the patients with and without enteritis using Fisher's exact test or the Mann-Whitney U test., Results: Enteritis was found on CT in the majority (33/58 [56.9%]) of the patients, and most of them (25/33 [75.8%]) showed duodenojejunitis. The frequency of gastrointestinal adverse events (28/33 [84.8%] vs. 13/25 [56.0%], p = 0.009), diarrhea (20/33 [60.6%] vs. 3/25 [12.0%], p < 0.001), and drug interruptions (25/33 [75.8%] vs. 10/25 [40.0%], p = 0.008) and the number of other adverse events (3.9 ± 1.7 vs. 2.3 ± 1.3, p < 0.001) were significantly higher in the patients with enteritis on CT than in those without., Conclusions: Lenvatinib-induced enteritis frequently involved the duodenum and jejunum and was related to a significantly higher frequency of treatment interruptions and gastrointestinal adverse events.

Purpose: Immune checkpoint inhibitors promote the antitumor activity of T cells; however, there is a risk of side effects. The aim of this study was to characterize the computed tomography (CT) findings of one such side effect, anti-programmed cell death-1 antibody-related enterocolitis (αPD-1-EC)., Methods: This single-institution retrospective study included 21 patients with αPD-1-EC who underwent CT between January 2015 and April 2020. Two board-certified radiologists independently evaluated the CT findings, including the pattern of intestinal wall enhancement, maximum bowel wall thickness, maximum appendiceal diameter, and involvement of enterocolitis in each intestinal segment. Symptoms and their severity were also investigated., Results: Pancolitis and skip lesions involving both the rectosigmoid colon and the cecum were found in 9 patients each (42.9%). The rectum was the most frequently involved lesion (18/21, 85.7%), and appendiceal involvement was found in 11 patients (52.4%). The most frequent wall enhancement pattern was the gray pattern (i.e., mild homogeneous enhancement of the thickened bowel wall). The mean maximum diameter of the involved appendix was 9.6 ± 4.5 mm (range 4.5-18 mm). Frequent symptoms included diarrhea (21/21), fever (8/21), and abdominal pain (7/21). Other concomitant immune-related adverse events were found in 6 patients., Conclusions: Pancolitis, skip lesions, and appendiceal involvement were frequent in patients with αPD-1-EC. When combining these characteristic findings with other clinical findings, such as low-grade diarrhea, other concomitant immune-related adverse events, and anti-PD-1 therapy administration, CT may be a useful diagnostic tool for αPD-1-EC.

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.0 years). All these images were labeled as "Normal," "No Opacity/Not Normal," or "Opacity" by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images., (© 2021. The Author(s).)

Objective: To investigate whether a SPIO-labeling technique could enable MR visualization of the treatment margin after X-irradiation at a single dose of 30 Gy., Materials and Methods: Fifteen rats bearing N1-S1 hepatoma in either the left (group 1) or right (group 2) liver lobe were examined. Four hours after systemic SPIO administration, the left lobe was selectively irradiated at 30 Gy. Liver T2* maps were acquired 7 days later using a 9.4 T scanner. The livers were excised and examined histologically., Results: The irradiated area showed T2*-weighted hypointensity with significantly shorter T2* values than those in the non-irradiated area (p < 0.001). Tumors in group 1 completely disappeared, whereas tumors in group 2 had grown outside the T2*-weighted hypointensity by up to ~ 2.3 times that at baseline. Group 1 showed significantly higher probability of tumor regression than group 2 (p = 0.048). Histologically, iron deposition was heavier in irradiated areas than in non-irradiated areas., Discussion: Even at a single dose of 30 Gy, which is a slightly higher dose than can be used clinically in stereotactic body radiotherapy, MR visualization of the treatment margin was achieved, because tumors showed significant growth outside the T2*-hypointense areas. In contrast, tumors disappeared inside the T2*-hypointense areas.

Purpose: To build a novel, open-source, purely web-based platform system to address problems in the development and clinical use of computer-assisted detection/diagnosis (CAD) software. The new platform system will replace the existing system for the development and validation of CAD software, Clinical Infrastructure for Radiologic Computation of United Solutions (CIRCUS)., Methods: In our new system, the two top-level applications visible to users are the web-based image database (CIRCUS DB; database) and the Docker plug-in-based CAD execution platform (CIRCUS CS; clinical server). These applications are built on top of a shared application programming interface server, a three-dimensional image viewer component, and an image repository., Results: We successfully installed our new system into a Linux server at two clinical sites. A total of 1954 cases were registered in CIRCUS DB. We have been utilizing CIRCUS CS with four Docker-based CAD plug-ins., Conclusions: We have successfully built a new version of the CIRCUS system. Our platform was successfully implemented at two clinical sites, and we plan to publish it as an open-source software project.

Purpose: A novel image feature set named histogram of triangular paths in graph (HoTPiG) is presented. The purpose of this study is to evaluate the feasibility of the proposed HoTPiG feature set through two clinical computer-aided detection tasks: nodule detection in lung CT images and aneurysm detection in head MR angiography images., Methods: The HoTPiG feature set is calculated from an undirected graph structure derived from a binarized volume. The features are derived from a 3-D histogram in which each bin represents a triplet of shortest path distances between the target node and all possible node pairs near the target node. First, the vessel structure is extracted from CT/MR volumes. Then, a graph structure is extracted using an 18-neighbor rule. Using this graph, a HoTPiG feature vector is calculated at every foreground voxel. After explicit feature mapping with an exponential-χ 2 kernel, each voxel is judged by a linear support vector machine classifier. The proposed method was evaluated using 300 CT and 300 MR datasets., Results: The proposed method successfully detected lung nodules and cerebral aneurysms. The sensitivity was about 80% when the number of false positives was three per case for both applications., Conclusions: The HoTPiG image feature set was presented, and its high general versatility was shown through two medical lesion detection applications.

Purpose: Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) tends to show higher diagnostic accuracy than other modalities. There is a demand for computer-assisted detection (CAD) software for Gd-EOB-DTPA-enhanced MRI. Segmentation with high accuracy is important for CAD software. We propose a liver segmentation method for Gd-EOB-DTPA-enhanced MRI that is based on a four-dimensional (4D) fully convolutional residual network (FC-ResNet). The aims of this study are to determine the best combination of an input image and output image in our proposed method and to compare our proposed method with the previous rule-based segmentation method., Methods: We prepared a five-phase image set and a hepatobiliary phase image set as the input image sets to determine the best input image set. We also prepared a labeled liver image and labeled liver and labeled body trunk images as the output image sets to determine the best output image set. In addition, we optimized the hyperparameters of our proposed model. We used 30 cases to train our model, 10 cases to determine the hyperparameters of our model, and 20 cases to evaluate our model., Results: Our network with the five-phase image set and the output image set of labeled liver and labeled body trunk images showed the highest accuracy. Our proposed method showed higher accuracy than the previous rule-based segmentation method. The Dice coefficient of the liver region was 0.944 ± 0.018., Conclusion: Our proposed 4D FC-ResNet showed satisfactory performance for liver segmentation as preprocessing in CAD software.

Purpose: Elucidating the association between pancreatic ductal anomalies and chronic asymptomatic pancreatic hyperenzymemia using magnetic resonance cholangiopancreatography., Methods: We conducted a single-center, retrospective, case-control study. The healthy community group comprised 554 subjects who participated in a paid, whole-body health checkup program. The patient group comprised 14 subjects with idiopathic pancreatic hyperamylasemia or hyperlipasemia. All subjects underwent magnetic resonance cholangiopancreatography. The clinical features and incidence rates of pancreatic ductal anomalies were then compared between the groups., Results: Compared to the healthy community group, the patient group was significantly more likely to be ≥ age 65 (71.4% of patient group vs. 22.1% of healthy community group), have a history of diabetes mellitus (21.4% vs. 5.4%) or hypertension (35.7% vs. 11.4%), and to have pancreas divisum (21.4% vs. 2.7%), meandering main pancreatic duct (21.4% vs. 4.1%), Wirsungocele (14.3% vs. 1.1%), or dilated main pancreatic duct (14.3% vs. 2.3%). Multivariate analysis found that age ≥ 65 (odds ratio 8.76), presence of pancreas divisum (odds ratio 13.2), meandering main pancreatic duct (odds ratio 8.95), and Wirsungocele (odds ratio 17.6) were independent factors significantly associated with chronic asymptomatic pancreatic hyperenzymemia., Conclusions: Pancreas divisum, meandering main pancreatic duct, and Wirsungocele were independently associated with chronic asymptomatic pancreatic hyperenzymemia.

Purpose: For the development of computer-assisted detection (CAD) software using voxel-based classification, gold standards defined by pixel-by-pixel painting, called painted gold standards, are desirable. However, for radiologists who define gold standards, a simplified method of definition is desirable. One of the simplest methods of defining gold standards is a spherical region, called a spherical gold standard. In this study, we investigated whether spherical gold standards can be used as an alternative to painted gold standards for computerized detection using voxel-based classification., Materials and Methods: The spherical gold standards were determined by the center of gravity and the maximum diameter. We compared two types of gold standard, painted gold standards and spherical gold standards, by two types of CAD software using voxel-based classification., Results: The time required to paint the area of one lesion was 4.7-6.5 times longer than the time required to define a spherical gold standard. For the same performance of the CAD software, the number of training cases required for the spherical gold standard was 1.6-7.6 times that for the painted gold standards., Conclusion: Spherical gold standards can be used as an alternative to painted gold standards for the computerized detection of lesions with simple shapes.

Purpose: Compared with the conventional pulsed gradient spin-echo (PGSE) sequence, diffusion-weighted imaging (DWI) with the oscillating gradient spin-echo (OGSE) sequence can shorten the diffusion time by changing the frequency. The purpose was to investigate whether n-alkanes are suitable as isotropic phantoms for estimating the diffusion coefficient with the OGSE sequence., Materials and Methods: We investigated changes in the apparent diffusion coefficient (ADC) due to differences in the viscosities of nine n-alkane phantoms with different numbers of carbon atoms from C 8 H 18 to C 16 H 34 using OGSE and PGSE sequences at 21 °C. Effective diffusion times of 4.3, 5.1, 6.5, 9.3, 20, 40, and 60 ms were used. The T 2 relaxation times of each n-alkane phantom were measured using quantitative synthetic magnetic resonance imaging (MRI). Circular regions of interest were placed manually within the alkane phantoms on ADC and T 2 maps., Results: In each alkane phantom, changes in mean ADC values were almost constant with changes in diffusion times. Viscosities and ADC values showed inverse proportionality, as expected theoretically., Conclusion: The ADC values of alkanes do not depend on diffusion times. The n-alkanes can be useful phantoms for assessing the accuracy of clinical protocols of DWI with the OGSE sequence.

Purpose: The aim of the present study is to investigate a possible role of a single dose of (-)-epigallocatechin gallate (EGCG), the major catechin in green tea, for the pharmacokinetic interaction between green tea and nadolol in humans., Methods: In a randomized three-phase crossover study, 13 healthy volunteers received single doses of 30 mg nadolol orally with water (control), or an aqueous solution of EGCG-concentrated green tea extract (GTE) at low or high dose. Plasma concentrations and urinary excretion of nadolol were determined up to 48 h. In addition, blood pressure and pulse rate were monitored. In vitro transport kinetic experiments were performed using human embryonic kidney 293 cells stably expressing organic anion transporting polypeptide (OATP)1A2 to evaluate the inhibitory effect of EGCG on OATP1A2-mediated substrate transport., Results: Single coadministration of low and high dose GTE significantly reduced the plasma concentrations of nadolol. The geometric mean ratios with 90% CI for area under the plasma concentration-time curves from 0 to infinity of nadolol were 0.72 (0.56-0.87) for the low and 0.60 (0.51-0.69) for the high dose. There were no significant differences in T max , elimination half-life, and renal clearance between GTE and water phases. No significant changes were observed for blood pressure and pulse rate between phases. EGCG competitively inhibited OATP1A2-mediated uptake of sulphobromophthalein and nadolol with K i values of 21.6 and 19.4 μM, respectively., Conclusions: EGCG is suggested to be a key contributor to the interaction of green tea with nadolol. Moreover, even a single coadministration of green tea may significantly affect nadolol pharmacokinetics.

Purpose: The objective of this study is to assess the effects of green tea and its major catechin component, (-)-epigallocatechin gallate (EGCG), on CYP2C9-mediated substrate metabolism in vitro, and the pharmacokinetics of fluvastatin in healthy volunteers., Methods: The metabolism of diclofenac and fluvastatin in human recombinant CYP2C9 was investigated in the presence of EGCG. In a randomized three-phase crossover study, 11 healthy volunteers ingested a single 20-mg dose of fluvastatin with green tea extract (GTE), containing 150 mg of EGCG, along with water (300 mL), brewed green tea (300 mL), or water (300 mL) after overnight fasting. Plasma concentrations of fluvastatin and EGCG were measured by ultra-performance liquid chromatography with fluorescence detection and a single mass spectrometer., Results: EGCG inhibited diclofenac 4'-hydroxylation and fluvastatin degradation with IC 50 of 2.23 and 48.04 μM, respectively. Brewed green tea used in the clinical study also dose-dependently inhibited the metabolism of diclofenac and fluvastatin in vitro. However, no significant effects of GTE and brewed green tea were observed in plasma concentrations of fluvastatin. The geometric mean ratios with 90% CI for area under the plasma concentration-time curve (AUC 0-∞ ) of fluvastatin were 0.993 (0.963-1.024, vs. brewed green tea) and 0.977 (0.935-1.020, vs. GTE)., Conclusions: Although in vitro studies indicated that EGCG and brewed green tea produce significant inhibitory effects on CYP2C9 activity, the concomitant administration of green tea and fluvastatin in healthy volunteers did not influence the pharmacokinetics of fluvastatin.

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Purpose: Parkinson's disease (PD) is caused by a selective degeneration of dopamine neurons. The relationship between dopamine transporter (DAT) density and gray matter volume has been unclear. Here we investigated the voxelwise correlation between gray matter volume and DAT binding measured by 123 I-N-ω-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl)nortropane ( 123 I-FP-CIT) single-photon emission computed tomography (SPECT; DaTscan™ imaging) in PD., Materials and Methods: Thirty-one male patients with PD were examined with MRI and DaTscan. To measure nigrostriatal dopaminergic degeneration in PD, the specific binding ratio (SBR) of the striatum was obtained by DaTscan. Voxel-based morphometry (VBM) of 3D T1-weighted images was used to evaluate the relationships between the regional gray matter volume and the SBR in the striatum., Results: There were significant positive correlations between the SBR and the gray matter volume in the right pulvinar and posterior middle temporal gyrus and a trend level in the left pulvinar, all of which are associated with the second visual pathway., Conclusion: The nigrostriatal dopaminergic degeneration might affect the secondary visual pathway, leading to visual dysfunctions in PD.

We report a case of kaposiform hemangioendothelioma (KHE) occurring at the sigmoid colon in an 8-year-old male. He had experienced lower abdominal pain and fever for several days. Contrast-enhanced CT images revealed a well-enhanced mass involving several large and small cystic parts with thin, enhanced walls at the sigmoid colon. The diagnosis was made histopathologically after surgical resection; the cystic parts corresponded to dilated lymphatic vessels. KHE is a rare, locally aggressive, vascular tumor that usually occurs in the soft tissues of the extremities. However, a case occurring in the bowel is extremely rare, and usually appears as bowel wall thickening. This is a unique case of KHE appearing as a mass-shaped hypervascular lesion in the bowel. Dilated lymphatic vessels in KHE may appear as a cystic part of the lesion.

Granular cell tumor of the neurohypophysis is a rare disease entity. To our knowledge, this is the first report concerning a granular cell tumor of the neurohypophysis associated with optic tract edema. A 55-year-old man underwent brain magnetic resonance imaging (MRI) for a medical check-up, and a suprasellar tumor was detected. Brain computed tomography (CT) demonstrated a well delineated, homogenous, slightly hyperdense suprasellar tumor. MRI detected a lobular tumor that was isointense on T1-weighted images, hypointense on T2-weighted images, and showed homogeneous enhancement after administration of a gadopentetate dimeglumine. T2-weighted images and fluid-attenuated inversion recovery (FLAIR) images demonstrated a hyperintense region in the optic tract. Subtotal tumor resection was performed, and histological examination confirmed the diagnosis of granular cell tumor. Postoperative MRI showed that the tumor volume was reduced and optic tract edema diminished compared with the preoperative findings. We also review the literature focusing on radiographic findings, and compare the effectiveness of MRI and CT for diagnosing granular cell tumor of the neurohypophysis.

The aims of this study were to (1) investigate the repeatability of measured volumes using the atlas-based method in each area of the brain, and (2) validate our hypothesis that the repeatability of the measured volumes with the atlas-based method was improved by using smoothed images. T1-weighted magnetic resonance images were obtained in five healthy subjects using the 1.5-T scanner. We used Statistical Parametric Mapping 5 and WFU PickAtlas software (theory of the Talairach brain atlas). Volumes inside region-of-interest (ROI) were measured in ten sets (five subjects × right and left) on six ROIs, respectively. One set comprises five images (one subject × five 3D-T1WIs). The percentage change was defined as [100 × (measured volume-mean volume in each set)/mean volume in each set)]. As a result, the average percentage changes using non-smoothed image on each ROI were as follows: gray matter, 0.482%; white matter, 0.375%; cerebrospinal fluid images, 0.731%; hippocampus, 0.864%; orbital gyrus, 1.692%; cerebellum posterior lobe, 0.854%. Using smoothed images with large FWHM resulted in improved repeatability on orbital gyrus. This is the first report of repeatability in each brain structure and improved repeatability with smoothed images using the atlas-based method.

Purpose: We compared different acquisition and reconstruction methods in phantom and human studies in the clinical setting to validate our hypothesis that optimizing the k-space acquisition and reconstruction method could decrease motion artifacts., Materials and Methods: Diffusion tensor images of a water phantom were obtained with three table displacement magnitudes: 1 mm, 2 mm, and 3 mm. Images were reconstructed using homodyne and zero-fill reconstruction. Overscanning in 8- and 16-k(y) lines was tested. We performed visual assessment of the artifacts using reconstructed coronal images and analyzed them with Wilcoxon signed-ranks test both for phantom and human studies. Also, fractional anisotropy (FA) changes between acquisition methods were compared., Results: Artifacts due to smaller displacement (1 and 2 mm) were significantly reduced in 16-k(y) overscan with zero filling. The Wilcoxon signed-ranks test showed significant differences (P < 0.031 for reconstruction methods and P < 0.016 for overscanning methods). FA changes were statistically significant (P < 0.037; Student's t-test). The Wilcoxon signed-ranks test showed significant reductions (P < 0.005) in the human study., Conclusion: Motion-induced artifacts can be reduced by optimizing acquisition and reconstruction methods. The techniques described in this study offer an effective method for robust estimation of diffusion tensor in the presence of motion-related artifactual data points.