The Association of Metabolic Brain MRI, Amyloid PET, and Clinical Factors: A Study of Alzheimer's Disease and Normal Controls From the Open Access Series of Imaging Studies Dataset.

BACKGROUND: Although brain activities in Alzheimer's disease (AD) might be evaluated MRI and PET, the relationships between brain temperature (BT), the index of diffusivity along the perivascular space (ALPS index), and amyloid deposition in the cerebral cortex are still unclear. PURPOSE: To investigate the relationship between metabolic imaging measurements and clinical information in patients with AD and normal controls (NCs). STUDY TYPE: Retrospective analysis of a prospective dataset. POPULATION: 58 participants (78.3 ± 6.8 years; 30 female): 29 AD patients and 29 age- and sex-matched NCs from the Open Access Series of Imaging Studies dataset. FIELD STRENGTH/SEQUENCE: 3T; T1-weighted magnetization-prepared rapid gradient-echo, diffusion tensor imaging with 64 directions, and dynamic 18 F-florbetapir PET. ASSESSMENT: Imaging metrics were compared between AD and NCs. These included BT calculated by the diffusivity of the lateral ventricles, ALPS index that reflects the glymphatic system, the mean standardized uptake value ratio (SUVR) of amyloid PET in the cerebral cortex and clinical information, such as age, sex, and MMSE. STATISTICAL TESTS: Pearson's or Spearman's correlation and multiple linear regression analyses. P values <0.05 were defined as statistically significant. RESULTS: Significant positive correlations were found between BT and ALPS index (r = 0.44 for NCs), while significant negative correlations were found between age and ALPS index (rs = -0.43 for AD and - 0.47 for NCs). The SUVR of amyloid PET was not significantly associated with BT (P = 0.81 for AD and 0.21 for NCs) or ALPS index (P = 0.10 for AD and 0.52 for NCs). In the multiple regression analysis, age was significantly associated with BT, while age, sex, and presence of AD were significantly associated with the ALPS index. DATA CONCLUSION: Impairment of the glymphatic system measured using MRI was associated with lower BT and aging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.

PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications in neuroradiology remain unexplored. This study aimed to evaluate the diagnostic performance of GPT-4 based ChatGPT in neuroradiology. METHODS: We collected 100 consecutive "Case of the Week" cases from the American Journal of Neuroradiology between October 2021 and September 2023. ChatGPT generated a diagnosis from patient's medical history and imaging findings for each case. Then the diagnostic accuracy rate was determined using the published ground truth. Each case was categorized by anatomical location (brain, spine, and head & neck), and brain cases were further divided into central nervous system (CNS) tumor and non-CNS tumor groups. Fisher's exact test was conducted to compare the accuracy rates among the three anatomical locations, as well as between the CNS tumor and non-CNS tumor groups. RESULTS: ChatGPT achieved a diagnostic accuracy rate of 50% (50/100 cases). There were no significant differences between the accuracy rates of the three anatomical locations (p = 0.89). The accuracy rate was significantly lower for the CNS tumor group compared to the non-CNS tumor group in the brain cases (16% [3/19] vs. 62% [36/58], p < 0.001). CONCLUSION: This study demonstrated the diagnostic performance of ChatGPT in neuroradiology. ChatGPT's diagnostic accuracy varied depending on disease etiologies, and its diagnostic accuracy was significantly lower in CNS tumors compared to non-CNS tumors.

Evaluation of cranial nerve involvement in chordomas and chondrosarcomas: a retrospective imaging study.

PURPOSE: Cranial nerve involvement (CNI) influences the treatment strategies and prognosis of head and neck tumors. However, its incidence in skull base chordomas and chondrosarcomas remains to be investigated. This study evaluated the imaging features of chordoma and chondrosarcoma, with a focus on the differences in CNI. METHODS: Forty-two patients (26 and 16 patients with chordomas and chondrosarcomas, respectively) treated at our institution between January 2007 and January 2023 were included in this retrospective study. Imaging features, such as the maximum diameter, tumor location (midline or off-midline), calcification, signal intensity on T2-weighted image, mean apparent diffusion coefficient (ADC) values, contrast enhancement, and CNI, were evaluated and compared using Fisher's exact test or the Mann-Whitney U-test. The odds ratio (OR) was calculated to evaluate the association between the histological type and imaging features. RESULTS: The incidence of CNI in chondrosarcomas was significantly higher than that in chordomas (63% vs. 8%, P < 0.001). An off-midline location was more common in chondrosarcomas than in chordomas (86% vs. 13%; P < 0.001). The mean ADC values of chondrosarcomas were significantly higher than those of chordomas (P < 0.001). Significant associations were identified between chondrosarcomas and CNI (OR = 20.00; P < 0.001), location (OR = 53.70; P < 0.001), and mean ADC values (OR = 1.01; P = 0.002). CONCLUSION: The incidence of CNI and off-midline location in chondrosarcomas was significantly higher than that in chordomas. CNI, tumor location, and the mean ADC can help distinguish between these entities.

Brain temperature remains stable during the day: a study of diffusion-weighted imaging thermometry in healthy individuals.

PURPOSE: To investigate the daily fluctuations in brain temperature in healthy individuals using magnetic resonance (MR) diffusion-weighted imaging (DWI) thermometry and to clarify the associations between the brain and body temperatures and sex. METHODS: Thirty-two age-matched healthy male and female volunteers (male = 16, 20-38 years) were recruited between July 2021 and January 2022. Brain MR examinations were performed in the morning and evening phases on the same day to calculate the brain temperatures using DWI thermometry. Body temperature was also measured in each MR examination. Group comparisons of body and brain temperatures between the two phases were performed using paired t-tests. A multiple linear regression model was used to predict the morning brain temperature using sex, evening brain temperature, and the interaction between sex and evening brain temperature as covariates. RESULTS: Body temperatures were significantly higher in the evening than in the morning in all participants, male group, and female group (p < 0.001, = 0001, and < 0.001, respectively). Meanwhile, no significant difference was observed between the morning and evening brain temperatures in each analysis (p = 0.23, 0.70, and 0.16, respectively). Multiple linear regression analysis showed significant associations of morning brain temperature with sex (p = 0.038), evening brain temperature (p < 0.001), and the interaction between sex and evening brain temperature (p = 0.036). CONCLUSION: Unlike body temperature, brain temperature showed no significant daily fluctuations; however, daily fluctuations in brain temperature may vary depending on sex.

Frequency and imaging features of the adjacent osseous changes of salivary gland carcinomas in the head and neck region.

PURPOSE: The association between salivary gland carcinomas and adjacent osseous changes in the head and neck region is not clear. We evaluated the frequency and imaging features of such changes and investigated the specific characteristics of salivary gland carcinomas associated with them. METHODS: A total of 118 patients with histologically proven salivary gland carcinomas were retrospectively reviewed. The imaging characteristics of osseous changes were sorted into three categories based on computed tomography images: sclerotic change, erosive change, and lytic change. The frequency of all these osseous changes and any one of them was compared between different pathologies using Fisher's exact test. Odds ratios were calculated to evaluate the association between these changes and perineural spread. RESULTS: Osseous changes were found in 21 (18%) of 118 cases. Among these, seven (6%) cases were with sclerotic, nine (8%) with erosive, and nine (8%) with lytic changes (four with mixed change). Adenoid cystic carcinoma showed a significantly higher frequency of sclerotic and erosive changes, and either osseous change, than the other salivary gland carcinomas (p < 0.001 for each). Sclerotic changes were only present in the adenoid cystic carcinomas. Perineural spread was a significant factor in showing higher osseous change frequencies (odds ratio = 3.98, p = 0.006). CONCLUSION: Among salivary gland carcinomas in the head and neck region, adenoid cystic carcinomas had a significantly higher frequency of adjacent osseous changes, especially sclerotic changes, than other salivary gland carcinomas.

Ectopic thyroid tissue at the skull base: a case report.

Ectopic thyroid tissue is a rare entity, resulting from developmental abnormality during the migration of the embryonic thyroid germ from the floor of the primitive foregut to its final pre-tracheal position. Although ectopic thyroid tissue may be located anywhere, its location at the skull base is extremely rare. We report a case of ectopic thyroid tissue at the skull base in a 19-year-old man with multimodality imaging findings.

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms.

Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images reported by radiologists to contain aneurysms were extracted from four institutions for the period from November 2006 through October 2017. The images were divided into three data sets: training data set, internal test data set, and external test data set. The algorithm was constructed by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated. Results The training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm ± 2.0 [standard deviation]) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years ± 13) and 365 were on female patients (mean age, 64 years ± 13). Test data were provided by the internal test data set (649 aneurysms [mean size, 4.1 mm ± 3.2] in 521 examinations, including 177 male patients and 344 female patients with mean age of 66 years ± 12 and 67 years ± 13, respectively) and the external test data set (80 aneurysms [mean size, 4.1 mm ± 2.1] in 67 examinations, including 19 male patients and 48 female patients with mean age of 63 years ± 12 and 68 years ± 12, respectively). The sensitivity was 91% (592 of 649) and 93% (74 of 80) for the internal and external test data sets, respectively. The algorithm improved aneurysm detection in the internal and external test data sets by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared with the initial reports. Conclusion A deep learning algorithm detected cerebral aneurysms in radiologic reports with high sensitivity and improved aneurysm detection compared with the initial reports. © RSNA, 2018 See also the editorial by Flanders in this issue.

Internal Hernias in the Era of Multidetector CT: Correlation of Imaging and Surgical Findings.

Clinical diagnosis of internal hernias is challenging because of their nonspecific signs and symptoms. Many types of internal hernias have been defined: paraduodenal, small bowel mesentery-related, greater omentum-related, lesser sac, transverse mesocolon-related, pericecal, sigmoid mesocolon-related, falciform ligament, pelvic internal, and Roux-en-Y anastomosis-related. An internal hernia is a surgical emergency that can develop into intestinal strangulation and ischemia. Accurate preoperative diagnosis is crucial for appropriate management. Multidetector computed tomography (CT), with its thin-section axial images, high-quality multiplanar reformations, and three-dimensional images, currently plays an essential role in preoperative diagnosis of internal hernias. The diagnostic approach to internal hernias at multidetector CT includes detecting an intestinal closed loop, identifying the hernia orifice, and analyzing abnormal displacement of surrounding structures and key vessels around the hernia orifice and hernia sac. At each step, multidetector CT can depict pathognomonic findings. A saclike appearance suggests an intestinal closed loop in several types of internal hernias. Convergence, engorgement, and twisting of mesenteric vessels in the hernia orifice can be seen clearly at multidetector CT, especially with use of multiplanar reformations. For definitive diagnosis of an internal hernia, analysis of displacement of anatomic landmarks around the hernia orifice is particularly important, and thin-section images provide the required information. Detailed knowledge of the anatomy, etiology, and imaging landmarks of the various hernia types is also necessary. Familiarity with the appearances of internal hernias at multidetector CT allows accurate and specific preoperative diagnosis. (©)RSNA, 2015.

Diffusion-weighted imaging thermometry in multiple sclerosis.

PURPOSE: To prospectively investigate brain temperature using MR diffusion-weighted imaging thermometry in multiple sclerosis (MS) patients and age-matched healthy controls, to examine comparisons of brain temperature between MS patients and healthy volunteers, and to examine correlations between brain temperature and disease duration and between brain temperature and Expanded Disability Status Scale (EDSS) in MS patients. MATERIALS AND METHODS: Thirteen MS patients and 13 age-matched healthy controls were examined using a 3.0 Tesla MR unit from January 2011 to February 2013. Brain temperature in each participant was measured using diffusion-weighted imaging-based MR thermometry of the lateral ventricles. Group comparisons of brain temperature between MS patients and healthy controls were performed using the Student's t-test. The determination of correlation between brain temperature in MS patients and disease duration, and between brain temperature and EDSS were performed using a Pearson's correlation coefficient test. For statistical analyses, values of P < 0.05 were considered statistically significant. RESULTS: Median brain temperature was 35.81°C (range, 35.06-37.03°C) in MS patients, and 36.29°C (range, 35.51-37.89°C) in healthy controls representing a significant difference (P = 0.020). No significant correlation of both between brain temperature and disease duration and between brain temperature and EDSS were identified (r/P = -0.382/0.198, -0.026/0.933). CONCLUSION: Brain temperature was significantly lower in MS patients than in healthy controls, probably representing decreased brain metabolism in MS patients.

Role of per-rectal portal scintigraphy in long-term follow-up of congenital portosystemic shunt.

BACKGROUND: Congenital portosystemic shunt (CPSS) has the potential to cause hepatic encephalopathy and thus needs long-term follow-up, but an effective follow-up method has not yet been established. We aimed to evaluate the importance of per-rectal portal scintigraphy (PRPS) for long-term follow-up of CPSS. METHODS: We retrospectively examined shunt severity time course in patients (median: 9.6 y, range: 5.2-16.6 y) with intrahepatic (n = 3) or extrahepatic (n = 3) CPSS by using blood tests, ultrasonography or computed tomography, and PRPS. Per-rectal portal shunt index (cutoff: 10%) was calculated by PRPS. RESULTS: PRPS demonstrated that the initial shunt index was reduced in all intrahepatic cases (from 39.7 ± 9.8% (mean ± SD) to 14.6 ± 4.7%) and all extrahepatic cases (from 46.2 ± 10.9 to 27.5 ± 12.6%) during the follow-up period. However, ultrasonography and computed tomography disclosed different shunt diameter time courses between intrahepatic and extrahepatic CPSSs. Initial shunt diameter (5.8 ± 3.5 mm) reduced to 2.0 ± 0.3 mm in intrahepatic cases, but the initial diameter (6.3 ± 0.7 mm) increased to 10.6 ± 1.0 mm in extrahepatic cases. All patients had elevated serum total bile acid or ammonia levels at initial screening, but these blood parameters were insufficient to assess shunt severity because the values fluctuate. CONCLUSION: PRPS can track changes in the shunt severity of CPSS and is more reliable than ultrasonography and computed tomography in patients with extrahepatic CPSS.

Incidence of abnormal retropharyngeal lymph nodes in sinonasal malignancies among adults.

INTRODUCTION: The aim of this study is to investigate the relationship between abnormal retropharyngeal lymph nodes (RPLNs) and sinonasal malignancies among adults. METHODS: Magnetic resonance and computed tomography images from 89 patients over 20 years old who were all histopathologically confirmed to have sinonasal malignancies from September 2001 to April 2014 were assessed retrospectively. Abnormal RPLNs were determined as those >5 mm in shortest axis or showing heterogeneous enhancement on axial images. Locations of sinonasal malignancies were categorized using the anterior border of the pterygopalatine fossa as a boundary: (a) anterior lesions, only present anterior to the boundary, or (b) posterior lesions, present or extending posterior to the boundary. Fisher's exact test was used for the analysis of the relationship between frequency of abnormal RPLNs and lesion location. RESULTS: Abnormal RPLNs were present in 13 of 89 patients (15%), including 6 of 41 squamous cell carcinomas (15%), 4 of 24 malignant lymphomas (17%), 3 of 5 olfactory neuroblastomas (60%), and 0 of 19 others (0%). Four of the 39 patients (10%) with anterior lesions showed abnormal RPLNs, compared to 9 of 50 patients (18%) with posterior lesions. No significant difference in frequency of abnormal RPLNs was apparent between anterior and posterior lesions (P = 0.37). CONCLUSIONS: In primary sinonasal malignancies among adults, the highest incidence of abnormal RPLNs was seen with olfactory neuroblastoma. The frequency of abnormal RPLNs was unaffected by the location of sinonasal malignancies among adults.

Quantitative imaging values of CT, MR, and FDG-PET to differentiate pineal parenchymal tumors and germinomas: are they useful?

INTRODUCTION: Quantitative values of CT attenuation, apparent diffusion coefficient (ADC), and standardized uptake value (SUV) were investigated for differentiation between pineal parenchymal tumors (PPTs) and germinomas. Differences in age, sex, and calcification pattern were also evaluated. METHODS: Twenty-three patients with PPTs and germinomas in 20 years were retrospectively enrolled under the approval of the institutional review board. CT attenuation, ADC, and SUV (20, 13, and 10 patients, respectively) were statistically compared between the two tumors. Differences in sex and patterns of calcification ("exploded" or "engulfed") were also examined. Mean patient ages were compared among three groups of pineoblastoma, pineal parenchymal tumor of intermediate differentiation, (PPTID) and pineocytoma and germinoma. RESULTS: None of the quantitative values of CT attenuation, ADC, and SUV showed significant differences between PPTs and germinomas (p > .05). However, there was a significant difference in age (p < .05) among the three groups of pineoblastoma (mean age ± standard deviation 7.0 ± 8.7 years), PPTID, and pineocytoma (53.7 ± 11.4 years) and germinoma (19.1 ± 8.1 years). Sex also showed significant differences between PPTs and germinomas (p = .039). Exploded pattern of calcification was found in 9 of 11 PPT patients and engulfed pattern in 7 of 9 patients with germinomas. No reverse pattern was observed, and the patterns of calcification were considered highly specific of tumor types. CONCLUSIONS: None of the quantitative imaging values could differentiate PPTs from germinomas. Age, sex, and calcification patterns were confirmed useful in differentiating these tumors to some degree.

Hepatic focal nodular hyperplasia with congenital portosystemic shunt.

Hepatic focal nodular hyperplasia (FNH) is a rare benign tumor in children. Vascular anomalies have been identified as pathological features of FNH, but the etiology remains unclear. We describe a rare case including the time course of formation of hepatic FNH in response to congenital portosystemic shunt (PSS). A 4-month-old girl was identified on newborn mass screening to have hypergalactosemia, but no inherited deficiencies in galactose-metabolizing enzymes were found. Ultrasonography and per-rectal portal scintigraphy showed intrahepatic PSS of the right lobe as a cause of the hypergalactosemia. At age 12 months, the patient had elevated hepatic enzymes and small hypoechoic hepatic lesions around the shunt. On abdominal contrast-enhanced ultrasonography spoke-wheel sign and central stellate scar were seen, which are typical features of hepatic FNH without biopsy. Congenital intrahepatic PSS should be evaluated on abdominal contrast-enhanced ultrasonography and observed over time because of its potential to develop into hepatic FNH.

Posterior reversible encephalopathy syndrome following acute pancreatitis during chemotherapy for acute monocytic leukemia.

We describe an 18-year-old man with acute leukemia who presented with posterior reversible encephalopathy syndrome (PRES) shortly after developing acute pancreatitis. On day 15 after the third consolidation course with high-dose cytarabine, treatment with broad-spectrum antibiotics was initiated for febrile neutropenia. On day 16, he developed septic shock, and subsequently, acute respiratory distress syndrome (ARDS). After adding vancomycin, micafungin and high-dose methylprednisolone (mPSL) to his treatment regimen, these manifestations subsided. On day 22, he received hemodialysis for drug-induced acute renal failure. On day 24, he developed acute pancreatitis possibly due to mPSL; the following day he had generalized seizures, and was intubated. Cerebrospinal fluid findings were normal. Brain MRI revealed hyperintense signals on FLAIR images and increased apparent diffusion coefficient values in the sub-cortical and deep white matter areas of the bilateral temporal and occipital lobes, indicative of vasogenic edema. Thus, we diagnosed PRES. Blood pressure, seizures and volume status were controlled, with MRI findings showing improvement by day 42. He was extubated on day 32 and discharged on day 49 without complications. Although little is known about PRES following acute pancreatitis, clinicians should be aware that this condition may develop.

Cerebral toxoplasmosis after umbilical cord blood transplantation diagnosed by the detection of anti-toxoplasma specific IgM antibody in cerebrospinal fluid.

Cerebral toxoplasmosis is a rare, potentially fatal, complication of hematopoietic cell transplantation. Early definitive diagnosis is very difficult and it may be associated with a poor prognosis. Herein, we describe a 60-year-old woman who developed cerebral toxoplasmosis after cord blood transplantation for myelodysplastic syndrome. During treatment with tacrolimus and methylprednisolone for relapsed grade 2 acute gut GVHD, fever and disturbance of consciousness occurred on day 210. Brain MRI showed multiple ring-enhancing nodular lesions in the thalamus, basal ganglia, brainstem, and subcortical white matter. Cerebrospinal fluid (CSF) assessment revealed elevations of both anti-to-xoplasma IgM and IgG, which were also elevated in serum, but no evidence of other infections or malignancies. Notably, the IgM level was higher in the CSF than in serum. Thus, cerebral toxoplasmosis was diagnosed. Soon after administration of oral sulfamethoxazole/trimethoprim and intravenous clindamycin in combination with short-term dexamethasone for the cerebral edema, her symptoms and signs began to improve. On day 229, both IgM and IgG titers in CSF had clearly decreased but remained essentially constant in serum. She was discharged without clinically significant neurological disorders. This case suggests that CSF specific anti-toxoplasma IgM titers might be useful for early diagnosis of cerebral toxoplasmosis after transplantation.

Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases.

PURPOSE: To compare the diagnostic performance among Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT­4 with vision (GPT-4V) based ChatGPT, and radiologists in challenging neuroradiology cases. METHODS: We collected 32 consecutive "Freiburg Neuropathology Case Conference" cases from the journal Clinical Neuroradiology between March 2016 and December 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case. Six radiologists (three radiology residents and three board-certified radiologists) independently reviewed all cases and provided diagnoses. ChatGPT and radiologists' diagnostic accuracy rates were evaluated based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. RESULTS: GPT­4 and GPT-4V-based ChatGPTs achieved accuracy rates of 22% (7/32) and 16% (5/32), respectively. Radiologists achieved the following accuracy rates: three radiology residents 28% (9/32), 31% (10/32), and 28% (9/32); and three board-certified radiologists 38% (12/32), 47% (15/32), and 44% (14/32). GPT-4-based ChatGPT's diagnostic accuracy was lower than each radiologist, although not significantly (all p > 0.07). GPT-4V-based ChatGPT's diagnostic accuracy was also lower than each radiologist and significantly lower than two board-certified radiologists (p = 0.02 and 0.03) (not significant for radiology residents and one board-certified radiologist [all p > 0.09]). CONCLUSION: While GPT-4-based ChatGPT demonstrated relatively higher diagnostic performance than GPT-4V-based ChatGPT, the diagnostic performance of GPT­4 and GPT-4V-based ChatGPTs did not reach the performance level of either radiology residents or board-certified radiologists in challenging neuroradiology cases.

Deep learning-based diffusion tensor image generation model: a proof-of-concept study.

This study created an image-to-image translation model that synthesizes diffusion tensor images (DTI) from conventional diffusion weighted images, and validated the similarities between the original and synthetic DTI. Thirty-two healthy volunteers were prospectively recruited. DTI and DWI were obtained with six and three directions of the motion probing gradient (MPG), respectively. The identical imaging plane was paired for the image-to-image translation model that synthesized one direction of the MPG from DWI. This process was repeated six times in the respective MPG directions. Regions of interest (ROIs) in the lentiform nucleus, thalamus, posterior limb of the internal capsule, posterior thalamic radiation, and splenium of the corpus callosum were created and applied to maps derived from the original and synthetic DTI. The mean values and signal-to-noise ratio (SNR) of the original and synthetic maps for each ROI were compared. The Bland-Altman plot between the original and synthetic data was evaluated. Although the test dataset showed a larger standard deviation of all values and lower SNR in the synthetic data than in the original data, the Bland-Altman plots showed each plot localizing in a similar distribution. Synthetic DTI could be generated from conventional DWI with an image-to-image translation model.

[Acute myeloid leukemia with monosomy 7 and inv(3)(q21q26.2) complicated with central diabetes insipidus].

A 20-year-old female presented with thirst, polyposia, and polyuria and was referred to our hospital because of leukocytosis and anemia. Bone marrow aspiration revealed 66.8% myeloperoxidase-positive blasts and trilineage myelodysplasia. The karyotype was 45, XX, inv(3)(q21q26.2), -7[19]. Therefore, a diagnosis of AML with inv(3)(q21q26.2) complicated by -7 was made. Moreover, hyposthenuria and a low anti-diuretic hormone (ADH) level were observed. Although cerebrospinal fluid analysis was normal, magnetic resonance imaging (MRI) revealed the absence of hyperintensity in the neurohypophysis in T1-weighted images. Therefore, she was also diagnosed with diabetes insipidus. After she was administered a desmopressin nasal spray, the volume of urine produced decreased. Following treatment with second induction therapy containing high-dose cytarabine for AML, she achieved complete remission in the bone marrow. Moreover, when the abnormality on MRI and the volume of urine were normalized, she discontinued desmopressin. Although diabetes insipidus is a rare complication of AML, the majority of AML patients who have diabetes insipidus have the abnormal karyotypes with inv(3)(q21q26.2)/t(3;3)(q21;q26.2) and monosomy 7. Further study is required to clarify the pathogenesis and develop a strategy for the treatment of this category of AML.

Correlation between Phase-difference-enhanced MR Imaging and Amyloid Positron Emission Tomography: A Study on Alzheimer's Disease Patients and Normal Controls.

PURPOSE: While amyloid-ß deposition in the cerebral cortex for Alzheimer's disease (AD) is often evaluated by amyloid positron emission tomography (PET), amyloid-ß-related iron can be detected using phase difference enhanced (PADRE) imaging; however, no study has validated the association between PADRE imaging and amyloid PET. This study investigated whether the degree of hypointense areas on PADRE imaging correlated with the uptake of amyloid PET. METHODS: PADRE imaging and amyloid PET were performed in 8 patients with AD and 10 age-matched normal controls. ROIs in the cuneus, precuneus, superior frontal gyrus (SFG), and superior temporal gyrus (STG) were automatically segmented. The degree of hypointense areas on PADRE imaging in each ROI was evaluated using 4-point scaling of visual assessment or volumetric semiquantitative assessment (the percentage of hypointense volume within each ROI). The mean standardized uptake value ratio (SUVR) of amyloid PET in each ROI was also calculated. The Spearman's correlation coefficient between the 4-point scale of PADRE imaging and SUVR of amyloid PET or between the semiquantitative hypointense volume percentage and SUVR in each ROI was evaluated. RESULTS: In the precuneus, a significant positive correlation was identified between the 4-point scale of PADRE imaging and SUVR of amyloid PET (Rs = 0.5; P = 0.034) in all subjects. In the cuneus, a significant positive correlation was identified between the semiquantitative volume percentage of PADRE imaging and SUVR of amyloid PET (Rs = 0.55; P = 0.02) in all subjects. CONCLUSION: Amyloid-ß-enhancing PADRE imaging can be used to predict the SUVR of amyloid PET, especially in the cuneus and precuneus, and may have the potential to be used for diagnosing AD by detecting amyloid deposition.

Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study.

BACKGROUND: Chest radiography is a common and widely available examination. Although cardiovascular structures-such as cardiac shadows and vessels-are visible on chest radiographs, the ability of these radiographs to estimate cardiac function and valvular disease is poorly understood. Using datasets from multiple institutions, we aimed to develop and validate a deep-learning model to simultaneously detect valvular disease and cardiac functions from chest radiographs. METHODS: In this model development and validation study, we trained, validated, and externally tested a deep learning-based model to classify left ventricular ejection fraction, tricuspid regurgitant velocity, mitral regurgitation, aortic stenosis, aortic regurgitation, mitral stenosis, tricuspid regurgitation, pulmonary regurgitation, and inferior vena cava dilation from chest radiographs. The chest radiographs and associated echocardiograms were collected from four institutions between April 1, 2013, and Dec 31, 2021: we used data from three sites (Osaka Metropolitan University Hospital, Osaka, Japan; Habikino Medical Center, Habikino, Japan; and Morimoto Hospital, Osaka, Japan) for training, validation, and internal testing, and data from one site (Kashiwara Municipal Hospital, Kashiwara, Japan) for external testing. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. FINDINGS: We included 22 551 radiographs associated with 22 551 echocardiograms obtained from 16 946 patients. The external test dataset featured 3311 radiographs from 2617 patients with a mean age of 72 years [SD 15], of whom 49·8% were male and 50·2% were female. The AUCs, accuracy, sensitivity, and specificity for this dataset were 0·92 (95% CI 0·90-0·95), 86% (85-87), 82% (75-87), and 86% (85-88) for classifying the left ventricular ejection fraction at a 40% cutoff, 0·85 (0·83-0·87), 75% (73-76), 83% (80-87), and 73% (71-75) for classifying the tricuspid regurgitant velocity at a 2·8 m/s cutoff, 0·89 (0·86-0·92), 85% (84-86), 82% (76-87), and 85% (84-86) for classifying mitral regurgitation at the none-mild versus moderate-severe cutoff, 0·83 (0·78-0·88), 73% (71-74), 79% (69-87), and 72% (71-74) for classifying aortic stenosis, 0·83 (0·79-0·87), 68% (67-70), 88% (81-92), and 67% (66-69) for classifying aortic regurgitation, 0·86 (0·67-1·00), 90% (89-91), 83% (36-100), and 90% (89-91) for classifying mitral stenosis, 0·92 (0·89-0·94), 83% (82-85), 87% (83-91), and 83% (82-84) for classifying tricuspid regurgitation, 0·86 (0·82-0·90), 69% (68-71), 91% (84-95), and 68% (67-70) for classifying pulmonary regurgitation, and 0·85 (0·81-0·89), 86% (85-88), 73% (65-81), and 87% (86-88) for classifying inferior vena cava dilation. INTERPRETATION: The deep learning-based model can accurately classify cardiac functions and valvular heart diseases using information from digital chest radiographs. This model can classify values typically obtained from echocardiography in a fraction of the time, with low system requirements and the potential to be continuously available in areas where echocardiography specialists are scarce or absent. FUNDING: None.