Assessment for Carotid Atherosclerotic Plaque Using Vessel Wall Magnetic Resonance Imaging: A Multireader ROC Study to Determine Optimal Sequence for Detecting Vessel Wall Calcification.

INTRODUCTION: We aimed to compare conventional vessel wall MR imaging techniques and quantitative susceptibility mapping (QSM) to determine the optimal sequence for detecting carotid artery calcification. METHODS: Twenty-two patients who underwent carotid vessel wall MR imaging and neck CT were enrolled. Four slices of 6-mm sections from the bilateral internal carotid bifurcation were subdivided into 4 segments according to clock position (0-3, 3-6, 6-9, and 9-12) and assessed for calcification. Two blinded radiologists independently reviewed a total of 704 segments and scored the likelihood of calcification using a 5-point scale on spin-echo imaging, FLASH, and QSM. The observer performance for detecting calcification was evaluated by a multireader, multiple-case receiver operating characteristic study. Weighted κ statistics were calculated to assess interobserver agreement. RESULTS: QSM had a mean area under the receiver operating characteristic curve of 0.85, which was significantly higher than that of any other sequence (p < 0.01) and showed substantial interreader agreement (κ = 0.68). A segment with a score of 3-5 was defined as positive, and a segment with a score of 1-2 was defined as negative; the sensitivity and specificity of QSM were 0.75 and 0.87, respectively. CONCLUSION: QSM was the most reliable MR sequence for the detection of plaque calcification.

Significance of Low Signal in Intracranial Vertebral Artery Wall Observed on Susceptibility-Weighted Angiography.

PURPOSE: We aimed to investigate the clinical significance of the low signal in the intracranial vertebral artery wall observed on susceptibility-weighted angiography. MATERIALS AND METHODS: We retrospectively reviewed susceptibility-weighted angiographies from 200 consecutive patients with acute ischemic stroke in the posterior circulation territory. The presence of eccentric or concentric low signals in the vertebral artery wall was examined and evaluated. The etiology of the low signal was also investigated as much as possible by referring to computed tomography and T1-weighted imaging (T1WI). We also compared its frequency in each stroke subtype. RESULTS: A low signal was observed in 128/200 patients (64%). The low signals (58%) corresponded to vessel wall calcification in 74 of 128 patients and to vessel wall thickening showing intermediate to low (n = 8) or high (n = 16) signals on T1WI in 24 (19%) patients. The low signal did not have vessel wall thickening or calcification in 1 patient, and the cause of the low signal could not be verified in 29 patients. According to stroke subtypes, a low signal was observed in 14/14 (100%) vertebral artery dissections, all of which corresponded to intramural hematoma. A low signal was observed in 51/65 (78%) atherothromboses, which were significantly more frequent than cardioembolism (34/66; 52%) and small-artery disease (18/39; 46%) (p < 0.01). In atherothrombosis, calcification was the most common cause of low signal (n = 32; 63%). CONCLUSION: Low signals on susceptibility-weighted angiography were frequently observed in vertebral artery dissection and atherothrombosis, reflecting intramural hematoma in all of the former and predominantly calcification in the latter.

Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck.

OBJECTIVES: To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). MATERIALS AND METHODS: We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS: Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001). DISCUSSION: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.

Quantitative Susceptibility Mapping: Basic Methods and Clinical Applications.

Quantitative susceptibility mapping (QSM), one of the advanced MRI techniques for evaluating magnetic susceptibility, offers precise quantitative measurements of spatial distributions of magnetic susceptibility. Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and is a substance-specific value. Recently, QSM has been widely used to estimate various levels of substances in the brain, including iron, hemosiderin, and deoxyhemoglobin (paramagnetism), as well as calcification (diamagnetism). By visualizing iron distribution in the brain, it is possible to identify anatomic structures that are not evident on conventional images and to evaluate various neurodegenerative diseases. It has been challenging to apply QSM in areas outside the brain because of motion artifacts from respiration and heartbeats, as well as the presence of fat, which has a different frequency to the proton. In this review, the authors provide a brief overview of the theoretical background and analyze methods of converting MRI phase images to QSM. Moreover, we provide an overview of the current clinical applications of QSM. Online supplemental material is available for this article. ©RSNA, 2022.

Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck.

PURPOSE: To assess the utility of deep learning (DL)-based image reconstruction with the combination of compressed sensing (CS) denoising cycle by comparing images reconstructed by conventional CS-based method without DL in fat-suppressed (Fs)-contrast enhanced (CE) three-dimensional (3D) T1-weighted images (T1WIs) of the head and neck. MATERIALS AND METHODS: We retrospectively analyzed the cases of 39 patients who had undergone head and neck Fs-CE 3D T1WI applying reconstructions based on conventional CS and CS augmented by DL, respectively. In the qualitative assessment, we evaluated overall image quality, visualization of anatomical structures, degree of artifacts, lesion conspicuity, and lesion edge sharpness based on a five-point system. In the quantitative assessment, we calculated the signal-to-noise ratios (SNRs) of the lesion and the posterior neck muscle and the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS: For all items of the qualitative analysis, significantly higher scores were awarded to images with DL-based reconstruction (p < 0.001). In the quantitative analysis, DL-based reconstruction resulted in significantly higher values for both the SNR of lesions (p < 0.001) and posterior neck muscles (p < 0.001). Significantly higher CNRs were also observed in images with DL-based reconstruction (p < 0.001). CONCLUSION: DL-based image reconstruction integrating into the CS-based denoising cycle offered superior image quality compared to the conventional CS method. This technique will be useful for the assessment of patients with head and neck disease.

Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities.

PURPOSE: To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter hyperintensity evaluation. MATERIALS AND METHODS: We examined 30 patients with white matter hyperintensities, obtaining fully sampled FLAIR images (standard FLAIR, std-FLAIR). We created accelerated FLAIR (acc-FLAIR) images using one-third of the fully sampled data and applied deep learning to generate DLR-FLAIR images. Three neuroradiologists assessed the quality (amount of noise and gray/white matter contrast) in all three image types. The reproducibility of hyperintensities was evaluated by comparing a subset of 100 hyperintensities in acc-FLAIR and DLR-FLAIR images with those in the std-FLAIR images. Quantitatively, similarities and errors of the entire image and the focused regions on white matter hyperintensities in acc-FLAIR and DLR-FLAIR images were measured against std-FLAIR images using structural similarity index measure (SSIM), regional SSIM, normalized root mean square error (NRMSE), and regional NRMSE values. RESULTS: All three neuroradiologists evaluated DLR-FLAIR as having significantly less noise and higher image quality scores compared with std-FLAIR and acc-FLAIR (p < 0.001). All three neuroradiologists assigned significantly higher frontal lobe gray/white matter visibility scores for DLR-FLAIR than for acc-FLAIR (p < 0.001); two neuroradiologists attributed significantly higher scores for DLR-FLAIR than for std-FLAIR (p < 0.05). Regarding white matter hyperintensities, all three neuroradiologists significantly preferred DLR-FLAIR (p < 0.0001). DLR-FLAIR exhibited higher similarity to std-FLAIR in terms of visibility of the hyperintensities, with 97% of the hyperintensities rated as nearly identical or equivalent. Quantitatively, DLR-FLAIR demonstrated significantly higher SSIM and regional SSIM values than acc-FLAIR, with significantly lower NRMSE and regional NRMSE values (p < 0.0001). CONCLUSIONS: DLR-FLAIR can reduce scan time and generate images of similar quality to std-FLAIR in patients with white matter hyperintensities. Therefore, DLR-FLAIR may serve as an effective method in traditional magnetic resonance imaging protocols.

Acute onset of constrictive pericarditis due to acute myelomonocytic leukemia: A case and literature review.

We herein present a fatal case of constrictive pericarditis (CP) due to acute myelomonocytic leukemia (AMML) in a patient who initially complained of an acute onset of chest pain two days after COVID-19 vaccination. An autopsy revealed pericardial infiltration of leukemic cells. CP is rarely associated with leukemia and only 14 cases have been reported in the literature. The etiology of CP in previous reports included leukemic infiltration, graft-versus-host disease, drug-induced, post-radiation, autoimmune, and otherwise unidentified. This case indicates that leukemic infiltration can cause CP and that clinicians should include leukemia in the differential diagnosis of CP.

Prediction of amyloid positron emission tomography positivity using multiple regression analysis of quantitative susceptibility mapping.

PURPOSE: To develop a method for predicting amyloid positron emission tomography (PET) positivity based on multiple regression analysis of quantitative susceptibility mapping (QSM). MATERIALS AND METHODS: This prospective study included 39 patients with suspected dementia from four centers. QSM images were obtained through a 3-T, three-dimensional radiofrequency-spoiled gradient-echo sequence with multiple echoes. The cortical standard uptake value ratio (SUVR) was obtained using amyloid PET with 18F-flutemetamol, and susceptibility in the brain regions was obtained using QSM. A multiple regression model to predict cortical SUVR was constructed based on susceptibilities in multiple brain regions, with the constraint that cortical SUVR and susceptibility were positively correlated. The discrimination performance of the Aß-positive and Aß-negative cohorts was evaluated based on the predicted SUVR using the area under the receiver operating characteristic curve (AUC) and Mann-Whitney U test. RESULTS: The correlation coefficients between true and predicted SUVR were increased by incorporating the constraint, and the AUC to discriminate between the Aß-positive and Aß-negative cohorts reached to 0.79 (p < 0.01). CONCLUSION: These preliminary results suggest that a QSM-based multiple regression model can predict amyloid PET positivity with fair accuracy.

Fatal case of subdural empyema caused by Campylobacter rectus and Slackia exigua.

We report a fatal subdural empyema caused by Campylobacter rectus in a 66-year-old female who developed acute onset of confusion, dysarthria, and paresis in her left extremities. A CT scan showed hypodensity in a crescentic formation with a mild mid-line shift. She had a bruise on her forehead caused by a fall several days before admission, which initially raised subdural hematoma (SDH) diagnosis, and a burr hole procedure was planned. However, her condition deteriorated on the admission night, and she died before dawn. An autopsy revealed that she had subdural empyema (SDE) caused by Campylobacter rectus and Slackia exigua. Both microorganisms are oral microorganisms that rarely cause extra-oral infection. In our case, head trauma caused a skull bone fracture, and sinus infection might have expanded to the subdural space causing SDE. CT/MRI findings were not typical for either SDH or SDE. Early recognition of subdural empyema and prompt initiation of treatment with antibiotics and surgical drainage is essential for cases of SDE. We present our case and a review of four reported cases.

Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor.

BACKGROUND: In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep learning technique for the diagnosis of orbital invasion, using computed tomography (CT) images. METHODS: A total of 168 lesions with malignant nasal or sinonasal tumors were divided into a training dataset (n = 119) and a test dataset (n = 49). The final diagnosis (invasion-positive or -negative) was determined by experienced radiologists who carefully reviewed all of the CT images. In a CNN-based deep learning analysis, a slice of the square target region that included the orbital bone wall was extracted and fed into a deep-learning training session to create a diagnostic model using transfer learning with the Visual Geometry Group 16 (VGG16) model. The test dataset was subsequently tested in CNN-based diagnostic models and by two other radiologists who were not specialized in head and neck radiology. At approx. 2 months after the first reading session, two radiologists again reviewed all of the images in the test dataset, referring to the diagnoses provided by the trained CNN-based diagnostic model. RESULTS: The diagnostic accuracy was 0.92 by the CNN-based diagnostic models, whereas the diagnostic accuracies by the two radiologists at the first reading session were 0.49 and 0.45, respectively. In the second reading session by two radiologists (diagnosing with the assistance by the CNN-based diagnostic model), marked elevations of the diagnostic accuracy were observed (0.94 and 1.00, respectively). CONCLUSION: The CNN-based deep learning technique can be a useful support tool in assessing the presence of orbital invasion on CT images, especially for non-specialized radiologists.

FDG-PET vs. chemical shift MR imaging in differentiating intertrabecular metastasis from hematopoietic bone marrow hyperplasia.

PURPOSE: To evaluate the utility of SUVmax on FDG-PET and chemical shift imaging (CSI) on MRI in the differentiation of intertrabecular metastasis (ITM) from hematopoietic bone marrow hyperplasia (HBMH). PATIENTS AND METHODS: We retrospectively evaluated 54 indeterminate focal bone marrow lesions in 44 patients detected on FDG-PET. The lesions were assigned to the metastasis group (M group, 29 lesions of 24 patients) and the non-metastasis group (non-M group, 25 lesions of 20 patients) based on the follow-up or the histopathological studies. The lesions were assessed with the maximum standardized uptake value (SUVmax) on FDG-PET CT images and signal change ratio (SCR) on CSI. RESULTS: The median SUVmax were 5.62 and 2.91; the median SCR were - 0.08 and - 34.8 in M and non-M groups respectively, with significant difference (p < 0.001). With ROC curve analysis, the optimal cutoff value of SUVmax was 4.48 with a sensitivity of 72.4%, a specificity of 100%, and AUC of 0.905. The cutoff value of SCR was - 6.15 with a sensitivity of 82.8%, a specificity of 80%, and AUC of 0.818. CONCLUSION: FDG-PET and CSI on MRI are useful in distinguishing ITM from HBMH. Though their sensitivities are similar, the specificity of FDG-PET was higher than that of MRI.

Quantitative Susceptibility Mapping for Carotid Atherosclerotic Plaques: A Pilot Study.

PURPOSE: Identifying plaque components such as intraplaque hemorrhage, lipid rich necrosis, and calcification is important to evaluate vulnerability of carotid atherosclerotic plaque; however, conventional vessel wall MR imaging may fail to discriminate plaque components. We aimed to evaluate the components of plaques using quantitative susceptibility mapping (QSM), a newly developed post-processing technique to provide voxel-based quantitative susceptibilities. METHODS: Seven patients scheduled for carotid endarterectomy were enrolled. Magnitude and phase images of five-echo 3D fast low angle shot (FLASH) were obtained using a 3T MRI, and QSM was calculated from the phase images. Conventional carotid vessel wall images (black-blood T1-weighted images [T1WI], T2-weighted images [T2WI], proton-density weighted images [PDWI], and time-of-flight images [TOF]) were also obtained. Pathological findings including intraplaque hemorrhage, calcification, and lipid rich necrosis at the thickest plaque section were correlated with relative susceptibility values with respect to the sternocleidomastoid muscle on QSM. On conventional vessel wall images, the contrast-noise ratio (CNR) between the three components and sternocleidomastoid muscle was measured respectively. Wilcoxon signed-rank test analyses were performed to assess the relative susceptibility values and CNR. RESULTS: Pathologically, lipid rich necrosis was proved in all of seven cases, and intraplaque hemorrhage in five of seven cases. Mean relative susceptibility value of hemorrhage was higher than lipid rich necrosis unexceptionally (P = 0.0313). There were no significant differences between CNR of hemorrhage and lipid rich necrosis on all sequences. In all six cases with plaque calcification, susceptibility value of calcification was significantly lower than lipid rich necrosis unexceptionally (P = 0.0156). There were significant differences between CNRs of lipid rich necrosis and calcification on T1WI, PDWI, TOF (P < 0.05). CONCLUSION: QSM of carotid plaque would provide a novel quantitative MRI contrast that enables reliable differentiation among intraplaque hemorrhage, lipid rich necrosis, and calcification, and be useful to identify vulnerable plaques.

Cerebral lipiodol embolism related to a vascular lake during chemoembolization in hepatocellular carcinoma: A case report and review of the literature.

A male patient underwent conventional transcatheter chemoembolization for advanced recurrent hepatocellular carcinoma (HCC). Even after the injection of 7 mL of lipiodol followed by gelatin sponge particles, the flow of feeding arteries did not slow down. A repeat angiography revealed a newly developed vascular lake draining into systemic veins; however, embolization was continued without taking noticing of the vascular lake. The patient's level of consciousness deteriorated immediately after the procedure, and non-contrast computed tomography revealed pulmonary and cerebral lipiodol embolisms. The patient's level of consciousness gradually improved after 8 wk in intensive care. In this case, a vascular lake emerged during chemoembolization and drained into systemic veins, offering a pathway carrying lipiodol to pulmonary vessels, the most likely cause of this serious complication. We should be aware that vascular lakes in HCC may drain into systemic veins and can cause intratumoral arteriovenous shunts.

Fatal case of subdural empyema caused by Campylobacter rectus and Slackia exigua

ABSTRACT We report a fatal subdural empyema caused by Campylobacter rectus in a 66-year-old female who developed acute onset of confusion, dysarthria, and paresis in her left extremities. A CT scan showed hypodensity in a crescentic formation with a mild mid-line shift. She had a bruise on her forehead caused by a fall several days before admission, which initially raised subdural hematoma (SDH) diagnosis, and a burr hole procedure was planned. However, her condition deteriorated on the admission night, and she died before dawn. An autopsy revealed that she had subdural empyema (SDE) caused by Campylobacter rectus and Slackia exigua. Both microorganisms are oral microorganisms that rarely cause extra-oral infection. In our case, head trauma caused a skull bone fracture, and sinus infection might have expanded to the subdural space causing SDE. CT/MRI findings were not typical for either SDH or SDE. Early recognition of subdural empyema and prompt initiation of treatment with antibiotics and surgical drainage is essential for cases of SDE. We present our case and a review of four reported cases.