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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. [ABSTRACT FROM AUTHOR]

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: 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: 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).)

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: 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).)

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: 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: 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: 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.

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.

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.