Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography.

PURPOSE: This study aimed to investigate the impact of deep learning reconstruction (DLR) on acute infarct depiction compared with hybrid iterative reconstruction (Hybrid IR). METHODS: This retrospective study included 29 (75.8 ± 13.2 years, 20 males) and 26 (64.4 ± 12.4 years, 18 males) patients with and without acute infarction, respectively. Unenhanced head CT images were reconstructed with DLR and Hybrid IR. In qualitative analyses, three readers evaluated the conspicuity of lesions based on five regions and image quality. A radiologist placed regions of interest on the lateral ventricle, putamen, and white matter in quantitative analyses, and the standard deviation of CT attenuation (i.e., quantitative image noise) was recorded. RESULTS: Conspicuity of acute infarct in DLR was superior to that in Hybrid IR, and a statistically significant difference was observed for two readers (p ≤ 0.038). Conspicuity of acute infarct with time from onset to CT imaging at < 24 h in DLR was significantly improved compared with Hybrid IR for all readers (p ≤ 0.020). Image noise in DLR was significantly reduced compared with Hybrid IR with both the qualitative and quantitative analyses (p < 0.001 for all). CONCLUSION: DLR in head CT helped improve acute infarct depiction, especially those with time from onset to CT imaging at < 24 h.

Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction.

PURPOSE: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT). METHODS: This retrospective study included 29 and 20 patients with and without vertebral masses. CT images were reconstructed using DLR and hybrid-IR. Three readers performed vertebral mass detection tests and evaluated the presence of spinal cord compression, the depiction of vertebral masses, and image noise. Quantitative image noise was measured by placing regions of interest on the aorta and spinal cord. RESULTS: Deep learning reconstruction tended to improve the performance of readers with less diagnostic imaging experience in detecting vertebral masses (area under the receiver operating characteristic curve [AUC] = .892-.966) compared with hybrid-IR (AUC = .839-.917). Diagnostic performance in evaluating spinal cord compression in DLR (AUC = .887-.995) also improved compared with that in hybrid-IR (AUC = .866-.942) for some readers. The depiction of vertebral masses and subjective image noise in DLR were significantly improved compared with those in hybrid-IR (P < .041). Quantitative image noise in DLR was also significantly reduced compared with that in hybrid-IR (P < .001). CONCLUSION: Deep learning reconstruction improved the depiction of vertebral masses, which resulted in a tendency to improve the performance of CT compared to hybrid-IR in detecting vertebral masses and diagnosing spinal cord compression for some readers.

Effects of deep learning on radiologists' and radiology residents' performance in identifying esophageal cancer on CT.

OBJECTIVE: To investigate the effectiveness of a deep learning model in helping radiologists or radiology residents detect esophageal cancer on contrast-enhanced CT images. METHODS: This retrospective study included 250 and 25 patients with and without esophageal cancer, respectively, who underwent contrast-enhanced CT between December 2014 and May 2021 (mean age, 67.9 ± 10.3 years; 233 men). A deep learning model was developed using data from 200 and 25 patients with esophageal cancer as training and validation data sets, respectively. The model was then applied to the test data set, consisting of additional 25 and 25 patients with and without esophageal cancer, respectively. Four readers (one radiologist and three radiology residents) independently registered the likelihood of malignant lesions using a 3-point scale in the test data set. After the scorings were completed, the readers were allowed to reference to the deep learning model results and modify their scores, when necessary. RESULTS: The area under the curve (AUC) of the deep learning model was 0.95 and 0.98 in the image- and patient-based analyses, respectively. By referencing to the deep learning model results, the AUCs for the readers were improved from 0.96/0.93/0.96/0.93 to 0.97/0.95/0.99/0.96 (p = 0.100/0.006/<0.001/<0.001, DeLong's test) in the image-based analysis, with statistically significant differences noted for the three less-experienced readers. Furthermore, the AUCs for the readers tended to improve from 0.98/0.96/0.98/0.94 to 1.00/1.00/1.00/1.00 (p = 0.317/0.149/0.317/0.073, DeLong's test) in the patient-based analysis. CONCLUSION: The deep learning model mainly helped less-experienced readers improve their performance in detecting esophageal cancer on contrast-enhanced CT. ADVANCES IN KNOWLEDGE: A deep learning model could mainly help less-experienced readers to detect esophageal cancer by improving their diagnostic confidence and diagnostic performance.

Comparison of Deep-Learning Image Reconstruction With Hybrid Iterative Reconstruction for Evaluating Lung Nodules With High-Resolution Computed Tomography.

OBJECTIVE: This study aimed to investigate the impact of deep-learning reconstruction (DLR) on the detailed evaluation of solitary lung nodule using high-resolution computed tomography (HRCT) compared with hybrid iterative reconstruction (hybrid IR). METHODS: This retrospective study was approved by our institutional review board and included 68 consecutive patients (mean ± SD age, 70.1 ± 12.0 years; 37 men and 31 women) who underwent computed tomography between November 2021 and February 2022. High-resolution computed tomography images with a targeted field of view of the unilateral lung were reconstructed using filtered back projection, hybrid IR, and DLR, which is commercially available. Objective image noise was measured by placing the regions of interest on the skeletal muscle and recording the SD of the computed tomography attenuation. Subjective image analyses were performed by 2 blinded radiologists taking into consideration the subjective noise, artifacts, depictions of small structures and nodule rims, and the overall image quality. In subjective analyses, filtered back projection images were used as controls. Data were compared between DLR and hybrid IR using the paired t test and Wilcoxon signed-rank sum test. RESULTS: Objective image noise in DLR (32.7 ± 4.2) was significantly reduced compared with hybrid IR (35.3 ± 4.4) ( P < 0.0001). According to both readers, significant improvements in subjective image noise, artifacts, depictions of small structures and nodule rims, and overall image quality were observed in images derived from DLR compared with those from hybrid IR ( P < 0.0001 for all). CONCLUSIONS: Deep-learning reconstruction provides a better high-resolution computed tomography image with improved quality compared with hybrid IR.

Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction.

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.

Association between postmortem computed tomography value of cerebrospinal fluid and time after death: A longitudinal study of antemortem and postmortem computed tomography.

This study was designed to examine the antemortem factors affecting cerebrospinal fluid (CSF) Hounsfield Units (HU) on postmortem computed tomography (PMCT) compared to the antemortem CT (AMCT). Fifty-five participants without brain lesions who died at a university hospital and underwent AMCT, PMCT, and an autopsy were enrolled. We recorded age, sex, time after death, the CSF HU on AMCT and PMCT at multiple measuring points, 4-point-scale brain atrophy grade on AMCT, and the cella media index. We tested the effects of CSF HU factors observed on PMCT. No significant differences were observed between CSF HUs at any of the PMCT measurement points. The average CSF HU on PMCT was positively correlated with the natural logarithm of the time after death (Pearson's correlation coefficient, 0.81; p < 0.001). No other factors showed correlative relationships. Up until approximately 12 h after death, the CSF HU on PMCT depended only on the time since death.

Delayed cerebral enhancement on post-mortem computed tomography due to residual contrast medium administered shortly before death.

Postmortem computed tomography (CT) is currently a well-known procedure and helps in postmortem investigations. In this case report, we report a unique postmortem CT finding: delayed cerebral enhancement associated with the antemortem infusion of contrast medium. A 72-year-old female lost consciousness at a restaurant and was taken to a hospital in an ambulance. Despite resuscitation efforts, she died of hypoxic-ischemic encephalopathy caused by cardiac arrest. About 6 h before her death, she underwent enhanced antemortem CT of the head. No abnormal enhancement was observed in the cerebral parenchyma. Then, 11 h after her death, she underwent unenhanced postmortem CT, which showed bilateral hyperdense caudate nucleus and putamina, due to residual iodinated contrast medium, in addition to other characteristic findings of hypoxic-ischemic encephalopathy. The mechanism underlying this phenomenon could be the destruction of the blood-brain barrier, and/or selective vulnerability, due to hypoxic-ischemic changes in the gray matter. Enhancement of basal ganglia on postmortem CT due to antemortem infusion of iodinated contrast medium might suggest hypoxic-ischemic encephalopathy, which should be noted in postmortem CT interpretations.

Fat-forming solitary fibrous tumor of the sacrum: A case report and literature review.

Fat-forming variant of solitary fibrous tumor (SFT) is a rare mesenchymal neoplasm. Here we report the case of a 33-year-old woman who developed pain and muscle weakness from the posterior aspect of the right hip to lower extremity. Imaging examinations revealed a mass with fatty components and hypervascular solid components filling the sacral spinal canal and sacral foramen. The sacral mass was resected and histological examination of the specimens revealed patternless proliferation of short spindle-shaped cells with staghorn blood vessels. A number of mature adipocyte-like cells were also observed. The tumor cells were positive for STAT6 and the nuclei of the adipocytes were also positive, which was diagnostic for fat-forming SFT.

Longitudinal comparison of ascites attenuation between antemortem and postmortem computed tomography.

OBJECTIVE: To investigate the changes in ascites attenuation between antemortem (AMCT) and postmortem computed tomography (PMCT) analyses of the same subjects. METHODS: Thirty-five subjects who underwent unenhanced or enhanced AMCT within 7 days before death, unenhanced PMCT, and autopsy were evaluated. In each subject, ascites attenuation was measured at similar sites on AMCT and PMCT. Attenuation changes were evaluated in 42 unenhanced AMCT/PMCT site pairs (23 subjects) and 20 enhanced AMCT/PMCT site pairs (12 subjects). Factors contributing to CT attenuation changes were also assessed, including the time interval between AMCT and PMCT, serum albumin level, estimated glomerular filtration rate, and ascites volume. RESULTS: Significantly elevated CT attenuation was observed between enhanced AMCT and PMCT (12.2 ± 6.3 vs. 18.7 ± 10.4 Hounsfield units; paired t-test, p = 0.006), but not between unenhanced AMCT and PMCT (13.5 ± 8.9 vs. 13.4 ± 9.3; p = 0.554). A significant inverse association was observed between the degree of CT attenuation change and the time interval between enhanced AMCT and PMCT (Spearman's rank correlation coefficient, r = -0.56, p = 0.01). CONCLUSIONS: We confirmed an elevated level of ascites attenuation on PMCT relative to AMCT in subjects who underwent enhanced AMCT shortly before death.

Computer-aided detection of cerebral aneurysms with magnetic resonance angiography: usefulness of volume rendering to display lesion candidates.

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.

Interaction of Necl-4/CADM4 with ErbB3 and integrin α6 ß4 and inhibition of ErbB2/ErbB3 signaling and hemidesmosome disassembly.

Nectin-like molecule 4 (Necl-4)/CADM4, a transmembrane cell-cell adhesion molecule with three Ig-like domains, was shown to serve as a tumor suppressor, but its mode of action has not been elucidated. In this study, we showed that Necl-4 interacted in cis with ErbB3 through their extracellular regions, recruited PTPN13 and inhibited the heregulin-induced activation of the ErbB2/ErbB3 signaling. In addition, we extended our previous finding that Necl-4 interacts in cis with integrin α6 ß4 through their extracellular regions and found that Necl-4 inhibited the phorbol ester-induced disassembly of hemidesmosomes. These results indicate that Necl-4 serves as a tumor suppressor by inhibiting the ErbB2/ErbB3 signaling and hemidesmosome disassembly.