Spontaneous rupture of a uterine leiomyoma accompanied by a hematoma appearing as a cystic lesion on imaging: A case report.

The rupture of a uterine leiomyoma is a rare complication. We report a case of ruptured leiomyoma that formed a hematoma that was initially suggestive of an ovarian origin. Magnetic resonance imaging revealed intact ovaries and a cystic lesion adjacent to leiomyomas. During surgery, the cystic lesion was found to be a hematoma caused by a rupture of the leiomyoma.

Differentiation between pleomorphic adenoma and schwannoma in the parapharyngeal space: histogram analysis of apparent diffusion coefficient.

OBJECTIVES: To elucidate the differences between pleomorphic adenomas and schwannomas occurring in the parapharyngeal space by histogram analyses of apparent diffusion coefficient (ADC) values measured with diffusion-weighted MRI. METHODS: This retrospective study included 29 patients with pleomorphic adenoma and 22 patients with schwannoma arising in the parapharyngeal space or extending into the parapharyngeal space from the parotid region. Using pre-operative MR images, ADC values of tumor lesions showing the maximum diameter were measured. The regions of interest for ADC measurement were placed by contouring the tumor margin, and the histogram metrics of ADC values were compared between pleomorphic adenomas and schwannomas regarding the mean, skewness, and kurtosis by Wilcoxon's rank sum test. Subsequent to the primary analysis which included all lesions, we performed two subgroup analyses regarding b-values and magnetic field strength used for MRI. RESULTS: The mean ADC values did not show significant differences between pleomorphic adenomas and schwannomas for the primary and subgroup analyses. Schwannomas showed higher skewness (p = 0.0001) and lower kurtosis (p = 0.003) of ADC histograms compared with pleomorphic adenomas in the primary analysis. Skewness was significantly higher in schwannomas in all the subgroup analyses. Kurtosis was consistently lower in schwannomas but did not reach statistical significance in one subgroup analysis. CONCLUSIONS: Skewness and kurtosis showed significant differences between pleomorphic adenomas and schwannomas occupying the parapharyngeal space, but the mean ADC values did not. Our results suggest that the skewness and kurtosis of ADC histograms may be useful in differentiating these two parapharyngeal tumors.

Comparison of 1.5 T and 3 T magnetic resonance angiography for detecting cerebral aneurysms using deep learning-based computer-assisted detection software.

PURPOSE: To compare the diagnostic performance of 1.5 T versus 3 T magnetic resonance angiography (MRA) for detecting cerebral aneurysms with clinically available deep learning-based computer-assisted detection software (EIRL aneurysm® [EIRL_an]), which has been approved by the Japanese Pharmaceuticals and Medical Devices Agency. We also sought to analyze the causes of potential false positives. METHODS: In this single-center, retrospective study, we evaluated the MRA scans of 90 patients who underwent head MRA (1.5 T and 3 T in 45 patients each) in clinical practice. Overall, 51 patients had 70 aneurysms. We used MRI from a vendor not included in the dataset used to create the EIRL_an algorithm. Two radiologists determined the ground truth, the accuracy of the candidates noted by EIRL_an, and the causes of false positives. The sensitivity, number of false positives per case (FPs/case), and the causes of false positives were compared between 1.5 T and 3 T MRA. Pearson's χ2 test, Fisher's exact test, and the Mann‒Whitney U test were used for the statistical analyses as appropriate. RESULTS: The sensitivity was high for 1.5 T and 3 T MRA (0.875‒1), but the number of FPs/case was significantly higher with 3 T MRA (1.511 vs. 2.578, p < 0.001). The most common causes of false positives (descending order) were the origin/bifurcation of vessels/branches, flow-related artifacts, and atherosclerosis and were similar between 1.5 T and 3 T MRA. CONCLUSION: EIRL_an detected significantly more false-positive lesions with 3 T than with 1.5 T MRA in this external validation study. Our data may help physicians with limited experience with MRA to correctly diagnose aneurysms using EIRL_an.

Persistent trigeminal artery-superior cerebellar artery segmental fusion diagnosed using magnetic resonance angiography.

PURPOSE: To describe a case of persistent trigeminal artery (PTA)-superior cerebellar artery (SCA) segmental fusion incidentally diagnosed on magnetic resonance (MR) angiography. CASE REPORT: A 53-year-old woman with a history of facial pain underwent cranial MR imaging and MR angiography. MR angiography showed a left lateral-type PTA arising from the precavernous portion of the left internal carotid artery (ICA). PTA branched into the left distal SCA and showed segmental fusion with the proximal SCA at the distal part of the PTA. We also diagnosed an unruptured cerebral aneurysm at the junction between the left ICA and PTA. DISCUSSION: PTA is the most frequent type of carotid-vertebrobasilar anastomosis. The reported prevalence rate is 0.2% by angiography and 0.34% by MR angiography. There are two types of PTA-lateral (usual) and medial (intrasellar). SCA arising from the lateral-type PTA has rarely been reported. Further, a PTA from which the distal SCA branches and segmentally fuses with the proximal SCA at the distal part of the PTA has not been reported. CONCLUSION: Using MR angiography, we diagnosed a rare type of PTA that fused segmentally with SCA. No similar case has been reported in relevant English-language literature.

Clinical Impact of Deep Learning Reconstruction in MRI.

Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning reconstruction (DLR) has recently emerged as a technology used in the image reconstruction process of MRI, which is an essential procedure in generating MR images. Denoising, which is the first DLR application to be realized in commercial MRI scanners, improves signal-to-noise ratio. When applied to lower magnetic field-strength scanners, the signal-to-noise ratio can be increased without extending the imaging time, and image quality is comparable to that of higher-field-strength scanners. Shorter imaging times decrease patient discomfort and reduce MRI scanner running costs. The incorporation of DLR into accelerated acquisition imaging techniques, such as parallel imaging or compressed sensing, shortens the reconstruction time. DLR is based on supervised learning using convolutional layers and is divided into the following three categories: image domain, k-space learning, and direct mapping types. Various studies have reported other derivatives of DLR, and several have shown the feasibility of DLR in clinical practice. Although DLR efficiently reduces Gaussian noise from MR images, denoising makes image artifacts more prominent, and a solution to this problem is desired. Depending on the training of the convolutional neural network, DLR may change the imaging features of lesions and obscure small lesions. Therefore, radiologists may need to adopt the habit of questioning whether any information has been lost on images that appear clean. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders.

AIM: Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits. METHODS: This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders. RESULTS: The excitatory shell-to-core connectivity was greater in all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups. CONCLUSION: Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets.

Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets.

BACKGROUND AND HYPOTHESIS: Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders. STUDY DESIGN: We applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network. STUDY RESULTS: DCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively. CONCLUSIONS: DCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

The contralateral effects of anticipated stimuli on brain activity measured by ERP and fMRI.

The present study examined the effects of unilateral stimulus presentation on the right hemisphere preponderance of the stimulus-preceding negativity (SPN) in the event-related potential (ERP) experiment, and aimed to elucidate whether unilateral stimulus presentation affected activations in the bilateral anterior insula in the functional magnetic resonance imaging (fMRI) experiment. Separate fMRI and ERP experiments were conducted using visual and auditory stimuli by manipulating the position of stimulus presentation (left side or right side) with the time estimation task. The ERP experiment revealed a significant right hemisphere preponderance during left stimulation and no laterality during the right stimulation. The fMRI experiment revealed that the left anterior insula was activated only in the right stimulation of auditory and visual stimuli whereas the right anterior insula was activated by both left and right stimulations. The visual condition retained a contralateral dominance, but the auditory condition showed a right hemisphere dominance in a localized area. The results of this study indicate that the SPN reflects perceptual anticipation, and also that the anterior insula is involved in its occurrence.

Motion correction in MR image for analysis of VSRAD using generative adversarial network.

Voxel-based specific region analysis systems for Alzheimer's disease (VSRAD) are clinically used to measure the atrophied hippocampus captured by magnetic resonance imaging (MRI). However, motion artifacts during acquisition of images may distort the results of the analysis. This study aims to evaluate the usefulness of the Pix2Pix network in motion correction for the input image of VSRAD analysis. Seventy-three patients examined with MRI were distinguished into the training group (n = 51) and the test group (n = 22). To create artifact images, the k-space images were manipulated. Supervised deep learning was employed to obtain a Pix2Pix that generates motion-corrected images, with artifact images as the input data and original images as the reference data. The results of the VSRAD analysis (severity of voxel of interest (VOI) atrophy, the extent of gray matter (GM) atrophy, and extent of VOI atrophy) were recorded for artifact images and motion-corrected images, and were then compared with the original images. For comparison, the image quality of Pix2Pix generated motion-corrected image was also compared with that of U-Net. The Bland-Altman analysis showed that the mean of the limits of agreement was smaller for the motion-corrected images compared to the artifact images, suggesting successful motion correction by the Pix2Pix. The Spearman's rank correlation coefficients between original and motion-corrected images were almost perfect for all results (severity of VOI atrophy: 0.87-0.99, extent of GM atrophy: 0.88-00.98, extent of VOI atrophy: 0.90-1.00). Pix2Pix generated motion-corrected images that showed generally improved quantitative and qualitative image qualities compared with the U-net generated motion-corrected images. Our findings suggest that motion correction using Pix2Pix is a useful method for VSRAD analysis.

Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer.

PURPOSE: To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR). METHODS: Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis. RESULTS: The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005-0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types. CONCLUSIONS: dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time.

Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach.

BACKGROUND: T2-weighted imaging (T2WI) is a key sequence of MRI studies of the pancreas. The single-shot fast spin echo (single-shot FSE) sequence is an accelerated form of T2WI. We hypothesized that denoising approach with deep learning-based reconstruction (dDLR) could facilitate accelerated breath-hold thin-slice single-shot FSE MRI, and reveal the pancreatic anatomy in detail. PURPOSE: To assess the image quality of thin-slice (3 mm) respiratory-triggered FSE T2WI (Resp-FSE) and breath-hold fast advanced spin echo with and without dDLR (BH-dDLR-FASE and BH-FASE, respectively) at 1.5 T. MATERIALS AND METHODS: MR images of 42 prospectively enrolled patients with suspected pancreaticobiliary disease were obtained at 1.5 T. We qualitatively and quantitatively evaluated image quality of BH-dDLR-FASE related to BH-FASE and Resp-FSE. RESULTS: The scan time of BH-FASE was significantly shorter than that of Resp-FSE (30 ± 4 s and 122 ± 25 s, p < 0.001). Qualitatively, dDLR significantly improved BH-FASE image quality, and the image quality of BH-dDLR-FASE was significantly better than that of Resp-FSE; as quantitative parameters, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of BH-dDLR-FASE were also significantly better than those of Resp-FSE. The BH-dDLR-FASE sequence covered the entire pancreas and liver and provided overall image quality rated close to excellent. CONCLUSIONS: The dDLR technique enables accelerated thin-slice single-shot FSE, and BH-dDLR-FASE seems to be clinically feasible.

Whole-lesion histogram analysis of apparent diffusion coefficient for the assessment of non-mass enhancement lesions on breast MRI.

Objectives: To investigate the application of apparent diffusion coefficient (ADC) histogram analysis in differentiating between benign and malignant breast lesions detected as non-mass enhancement on MRI. Materials and Methods: A retrospective study was conducted for 25 malignant and 26 benign breast lesions showing non-mass enhancement on breast MRI. An experienced radiologist without prior knowledge of the pathological results drew a region of interest (ROI) outlining the periphery of each lesion on the ADC map. A histogram was then made for each lesion. Following a univariate analysis of 18 summary statistics values, we conducted statistical discrimination after hierarchical clustering using Ward's method. A comparison between the malignant and the benign groups was made using multiple logistic regression analysis and the Mann-Whitney U test. A P -value of less than 0.05 was considered statistically significant. Results: Univariate analysis for the 18 summary statistics values showed the malignant group had greater entropy (P < 0.001) and lower uniformity (P < 0.001). While there was no significant difference in mean and skewness values, the malignant group tended to show a lower mean (P = 0.090) and a higher skewness (P = 0.065). Hierarchical clustering of the 18 summary statistics values identified four values (10th percentile, entropy, skewness, and uniformity) of which the 10th percentile values were significantly lower for the malignant group (P = 0.035). Conclusions: Whole-lesion ADC histogram analysis may be useful for differentiating malignant from benign lesions which show non-mass enhancement on breast MRI.

Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study.

BACKGROUND AND HYPOTHESIS: Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). STUDY DESIGN: Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. STUDY RESULTS: The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). CONCLUSION: We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.

Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features.

OBJECTIVES: We aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans. METHODS: Scan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22-72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32-53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases. RESULTS: The overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62-0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps. CONCLUSION: MRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries. KEY POINTS: • MRF-based radiomic features are highly repeatable in various dictionary step sizes. • Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used. • Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries.

Texture Analysis in Brain Tumor MR Imaging.

Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors.However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples.To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.

Tumor size in patients with severe pulmonary emphysema might be underestimated on preoperative CT.

OBJECTIVES: To evaluate the effect of emphysema on tumor diameter measured on preoperative computed tomography (CT) images versus pathological specimens. MATERIALS AND METHODS: We investigated patients who underwent primary lung cancer surgery: 55 patients (57 tumors) with severe emphysema and 57 patients (57 tumors) without emphysema. The tumor diameters measured in the postoperative pathological specimens were compared with those measured on the axial CT images and on multiplanar reconstruction (MPR) CT images by two independent radiologists; a subgroup analysis according to tumor size was also performed. A paired or unpaired t test was performed, depending on the tested subjects. RESULTS: In the emphysema group, the mean axial CT diameter was significantly smaller than the mean pathological diameter (p = 0.025/0.001 for reader 1/2), whereas in the non-emphysema group, the mean axial CT diameter was not significantly different from the pathological one for both readers. The difference between CT axial diameter and pathological diameter (= CT diameter - pathological diameter) was significantly smaller (i.e., had a stronger tendency toward underestimation on radiological measurements) in the emphysema group compared with the non-emphysema group (p = 0.014/0.008 for reader 1/2), and the difference was significantly smaller in tumors sized > 30 mm than tumors sized ≤ 20 mm in both groups. CONCLUSIONS: Tumor size is significantly smaller on preoperative CT in patients with severe emphysema compared to patients without emphysema, especially in the case of large tumors. MPR measurement using the widest of three dimensions should be used to select T-stage for patients with severe emphysema. KEY POINTS: • The presence of emphysema affects the accuracy of tumor size measurements on CT. • Compared to patients without emphysema, the tumor size in severe emphysema patients tends to be measured smaller in preoperative CT than the pathological specimen. • This trend is more evident when large tumors are measured on axial CT images alone.

Adenocarcinoma in situ and minimally invasive adenocarcinoma in lungs of smokers: image feature differences from those in lungs of non-smokers.

PURPOSE: We aimed to examine the characteristics of imaging findings of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in the lungs of smokers compared with those of non-smokers. MATERIALS AND METHODS: We included seven cases of AIS and 20 cases of MIA in lungs of smokers (pack-years ≥ 20) and the same number of cases of AIS and MIA in lungs of non-smokers (pack-years = 0). We compared the diameter of the entire lesion and solid component measured on computed tomography (CT) images, pathological size and invasive component diameter measured from pathological specimens, and CT values of the entire lesion and ground-glass opacity (GGO) portions between the smoker and non-smoker groups. RESULTS: The diameters of AIS and MIA on CT images and pathological specimens of the smoker group were significantly larger than those of the non-smoker group (p = 0.036 and 0.008, respectively), whereas there was no significant difference in the diameter of the solid component on CT images or invasive component of pathological specimens between the two groups. Additionally, mean CT values of the entire lesion and GGO component of the lesions in the smoker group were significantly lower than those in the non-smoker group (p = 0.036 and 0.040, respectively). CONCLUSION: AIS and MIA in smoker's lung tended to have larger lesion diameter and lower internal CT values compared with lesions in non-smoker's lung. This study calls an attention on smoking status in CT-based diagnosis for early stage adenocarcinoma.

Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique.

OBJECTIVES: To assess the image quality of conventional respiratory-triggered 3-dimentional (3D) magnetic resonance cholangiopancreatography (Resp-MRCP) and breath-hold 3D MRCP (BH-MRCP) with and without denoising procedure using deep learning-based reconstruction (dDLR) at 1.5 T. METHODS: Forty-two patients underwent MRCP at 1.5 T MRI. The following imaging sequences were performed: Resp-MRCP and BH-MRCP. We applied the dDLR method to the BH-MRCP data (BH-dDLR-MRCP). As a qualitative analysis, two radiologists rated the visibility of the proximal common bile duct (CBD), pancreaticobiliary junction, distal main pancreatic duct, cystic duct, and right and left hepatic ducts. Artifacts and overall image quality were also rated. The signal-to-noise ratios (SNRs), contrast ratios, and contrast-to-noise ratios (CNRs) of the CBD images were calculated for quantitative analysis. RESULTS: BH-MRCP was successfully performed in a single BH. The qualitative and quantitative measurements for BH-dDLR-MRCP were significantly higher than for BH-MRCP (P < 0.02 and P < 0.001, respectively), and the qualitative measurements for BH-dDLR-MRCP were equivalent to or higher than for Resp-MRCP (P = 0.048-1.000). The SNRs and CNRs for BH-dDLR-MRCP were significantly higher than for Resp-MRCP (P < 0.001 and P = 0.001, respectively). CONCLUSION: dDLR is useful and clinically feasible for BH-MRCP at 1.5 T MRI, and enables rapid imaging without loss of image quality compared to conventional Resp-MRCP.

Effects of Gadolinium Deposition in the Brain on Motor or Behavioral Function: A Mouse Model.

Background Recent studies showing gadolinium deposition in multiple organs have raised concerns about the safety of gadolinium-based contrast agents (GBCAs). Purpose To explore whether gadolinium deposition in brain structures will cause any motor or behavioral alterations. Materials and Methods This study was performed from July 2019 to December 2020. Groups of 17 female BALB/c mice were each repeatedly injected with phosphate-buffered saline (control group, group A), a macrocyclic GBCA (group B), or a linear GBCA (group C) for 8 weeks (5 mmol per kilogram of bodyweight per week for GBCAs). Brain MRI studies were performed every other week to observe the signal intensity change caused by the gadolinium deposition. After the injection period, rotarod performance test, open field test, elevated plus-maze test, light-dark anxiety test, locomotor activity assessment test, passive avoidance memory test, Y-maze test, and forced swimming test were performed to assess the locomotor abilities, anxiety level, and memory. Among-group differences were compared by using one-way or two-way factorial analysis of variance with Tukey post hoc testing or Dunnett post hoc testing. Results Gadolinium deposition in the bilateral deep cerebellar nuclei was confirmed with MRI only in mice injected with a linear GBCA. At 8 weeks, contrast ratio of group C (0.11; 95% CI: 0.10, 0.12) was higher than that of group A (-2.1 × 10-3; 95% CI: -0.011, 7.5 × 10-3; P < .001) and group B (2.7 × 10-4; 95% CI: -8.2 × 10-3, 8.7 × 10-3; P < .001). Behavioral analyses showed that locomotor abilities, anxiety level, and long-term or short-term memory were not different in mice injected with linear or macrocyclic GBCAs. Conclusion No motor or behavioral alterations were observed in mice with brain gadolinium deposition. Also, the findings support the safety of macrocyclic gadolinium-based contrast agents. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chen in this issue.

A multi-site, multi-disorder resting-state magnetic resonance image database.

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants ("traveling subjects") visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.