Empowering the on-site detection of nucleic acids by integrating CRISPR and digital signal processing.

Addressing the global disparity in cancer care necessitates the development of rapid and affordable nucleic acid (NA) testing technologies. This need is particularly critical for cervical cancer, where molecular detection of human papillomavirus (HPV) has emerged as an accurate screening method. However, implementing this transition in low- and middle-income countries has been challenging due to the high costs and centralized facilities required for current NA tests. Here, we present CreDiT (CRISPR Enhanced Digital Testing) for on-site NA detection. The CreDiT platform integrates i) a one-pot CRISPR strategy that simultaneously amplifies both target NAs and analytical signals and ii) a robust fluorescent detection based on digital communication (encoding/decoding) technology. These features enable a rapid assay (<35 minutes) in a single streamlined workflow. We demonstrate the sensitive detection of cell-derived HPV DNA targets down to single copies and accurate identification of HPV types in clinical cervical brushing specimens (n = 121).

Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study.

The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment. This retrospective study included patients who underwent CT for abnormal cervical LN and lymphoma assessment between January 2021 and July 2022. Patients were grouped into the development (n = 76), internal test 1 (n = 27), internal test 2 (n = 87), and external test (n = 26) cohorts. A 3D SegResNet model was used to train the CT images. The volume change rates of cervical LN across longitudinal CT scans were compared among patients with different treatment outcomes (stable, response, and progression). Dice similarity coefficient (DSC) and the Bland-Altman plot were used to assess the model's segmentation performance and reliability, respectively. No significant differences in baseline clinical characteristics were found across cohorts (age, P = 0.55; sex, P = 0.13; diagnoses, P = 0.06). The mean DSC was 0.39 ± 0.2 with a precision and recall of 60.9% and 57.0%, respectively. Most LN volumes were within the limits of agreement on the Bland-Altman plot. The volume change rates among the three groups differed significantly (progression (n = 74), 342.2%; response (n = 8), - 79.2%; stable (n = 5), - 8.1%; all P < 0.01). Our proposed DL segmentation model showed modest performance in quantifying the cervical LN burden on CT in patients with lymphoma. Longitudinal changes in cervical LN volume, as predicted by the DL model, were useful for treatment response assessment.

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.

BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort. PURPOSE: To develop a deep learning model for 3D breast cancer segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using weak annotation with reliable performance. STUDY TYPE: Retrospective. POPULATION: Seven hundred and thirty-six women with breast cancer from a single institution, divided into the development (N = 544) and test dataset (N = 192). FIELD STRENGTH/SEQUENCE: 3.0-T, 3D fat-saturated gradient-echo axial T1-weighted flash 3D volumetric interpolated brain examination (VIBE) sequences. ASSESSMENT: Two radiologists performed a weak annotation of the ground truth using bounding boxes. Based on this, the ground truth annotation was completed through autonomic and manual correction. The deep learning model using 3D U-Net transformer (UNETR) was trained with this annotated dataset. The segmentation results of the test set were analyzed by quantitative and qualitative methods, and the regions were divided into whole breast and region of interest (ROI) within the bounding box. STATISTICAL TESTS: As a quantitative method, we used the Dice similarity coefficient to evaluate the segmentation result. The volume correlation with the ground truth was evaluated with the Spearman correlation coefficient. Qualitatively, three readers independently evaluated the visual score in four scales. A P-value <0.05 was considered statistically significant. RESULTS: The deep learning model we developed achieved a median Dice similarity score of 0.75 and 0.89 for the whole breast and ROI, respectively. The volume correlation coefficient with respect to the ground truth volume was 0.82 and 0.86 for the whole breast and ROI, respectively. The mean visual score, as evaluated by three readers, was 3.4. DATA CONCLUSION: The proposed deep learning model with weak annotation may show good performance for 3D segmentations of breast cancer using DCE-MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Ensemble learning-based radiomics with multi-sequence magnetic resonance imaging for benign and malignant soft tissue tumor differentiation.

Many previous studies focused on differentiating between benign and malignant soft tissue tumors using radiomics model based on various magnetic resonance imaging (MRI) sequences, but it is still unclear how to set up the input radiomic features from multiple MRI sequences. Here, we evaluated two types of radiomics models generated using different feature incorporation strategies. In order to differentiate between benign and malignant soft tissue tumors (STTs), we compared the diagnostic performance of an ensemble of random forest (R) models with single-sequence MRI inputs to R models with pooled multi-sequence MRI inputs. One-hundred twenty-five STT patients with preoperative MRI were retrospectively included and consisted of training (n = 100) and test (n = 25) sets. MRI included T1-weighted (T1-WI), T2-weighted (T2-WI), contrast-enhanced (CE)-T1-WI, diffusion-weighted images (DWIs, b = 800 sec/mm2) and apparent diffusion coefficient (ADC) maps. After tumor segmentation on each sequence, 100 original radiomic features were extracted from each sequence image and divided into three-feature sets: T features from T1- and T2-WI, CE features from CE-T1-WI, and D features from DWI and ADC maps. Four radiomics models were built using Lasso and R with four combinations of three-feature sets as inputs: T features (R-T), T+CE features (R-C), T+D features (R-D), and T+CE+D features (R-A) (Type-1 model). An ensemble model was built by soft voting of five, single-sequence-based R models (Type-2 model). AUC, sensitivity, specificity, and accuracy of each model was calculated with five-fold cross validation. In Type-1 model, AUC, sensitivity, specificity, and accuracy were 0.752, 71.8%, 61.1%, and 67.2% in R-T; 0.756, 76.1%, 70.4%, and 73.6% in R-C; 0.750, 77.5%, 63.0%, and 71.2% in R-D; and 0.749, 74.6%, 61.1%, and 68.8% R-A models, respectively. AUC, sensitivity, specificity, and accuracy of Type-2 model were 0.774, 76.1%, 68.5%, and 72.8%. In conclusion, an ensemble method is beneficial to incorporate features from multi-sequence MRI and showed diagnostic robustness for differentiating malignant STTs.

Altered Brain Glymphatic Flow at Diffusion-Tensor MRI in Rapid Eye Movement Sleep Behavior Disorder.

Background Brain glymphatic dysfunction may contribute to the development of α-synucleinopathies. Yet, noninvasive imaging and quantification remain lacking. Purpose To examine glymphatic function of the brain in isolated rapid eye movement sleep behavior disorder (RBD) and its relevance to phenoconversion with use of diffusion-tensor imaging (DTI) analysis along the perivascular space (ALPS). Materials and Methods This prospective study included consecutive participants diagnosed with RBD, age- and sex-matched control participants, and participants with Parkinson disease (PD) who were enrolled and examined between May 2017 and April 2020. All study participants underwent 3.0-T brain MRI including DTI, susceptibility-weighted and susceptibility map-weighted imaging, and/or dopamine transporter imaging using iodine 123-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane SPECT at the time of participation. Phenoconversion status to α-synucleinopathies was unknown at the time of MRI. Participants were regularly followed up and monitored for any signs of α-synucleinopathies. The ALPS index reflecting glymphatic activity was calculated by a ratio of the diffusivities along the x-axis in the projection and association neural fibers to the diffusivities perpendicular to them and compared according to the groups with use of the Kruskal-Wallis and Mann-Whitney U tests. The phenoconversion risk in participants with RBD was evaluated according to the ALPS index with use of a Cox proportional hazards model. Results Twenty participants diagnosed with RBD (12 men; median age, 73 years [IQR, 66-76 years]), 20 control participants, and 20 participants with PD were included. The median ALPS index was lower in the group with RBD versus controls (1.53 vs 1.72; P = .001) but showed no evidence of a difference compared with the group with PD (1.49; P = .68). The conversion risk decreased with an increasing ALPS index (hazard ratio, 0.57 per 0.1 increase in the ALPS index [95% CI: 0.35, 0.93]; P = .03). Conclusion DTI-ALPS in RBD demonstrated a more severe reduction of glymphatic activity in individuals with phenoconversion to α-synucleinopathies. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Filippi and Balestrino in this issue.

Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake.

PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (123I-FP-CIT) single-photon emission computerized tomography (SPECT) can evaluate Parkinsonism. Nigral hyperintensity from nigrosome-1 and striatal dopamine transporter uptake are reduced in Parkinsonism; however, quantification is only possible with SPECT. Here, we aimed to develop a deep-learning-based regressor model that can predict striatal 123I-FP-CIT uptake on nigrosome magnetic resonance imaging (MRI) as a biomarker for Parkinsonism. METHODS: Between February 2017 and December 2018, participants who underwent 3 T brain MRI including SWI and 123I-FP-CIT SPECT based on suspected Parkinsonism were included. Two neuroradiologists evaluated the nigral hyperintensity and annotated the centroids of nigrosome-1 structures. We used a convolutional neural network-based regression model to predict striatal specific binding ratios (SBRs) measured via SPECT using the cropped nigrosome images. The correlation between measured and predicted SBRs was evaluated. RESULTS: We included 367 participants (203 women (55.3%); age, 69.0 ± 9.2 [range, 39-88] years). Random data from 293 participants (80%) were used for training. In the test set (74 participants [20%]), the measured and predicted 123I-FP-CIT SBRs were significantly lower with the loss of nigral hyperintensity (2.31 ± 0.85 vs. 2.44 ± 0.90) than with intact nigral hyperintensity (4.16 ± 1.24 vs. 4.21 ± 1.35, P < 0.01). The sorted measured 123I-FP-CIT SBRs and the corresponding predicted values were significantly and positively correlated (ρc = 0.7443; 95% confidence interval, 0.6216-0.8314; P < 0.01). CONCLUSION: A deep learning-based regressor model effectively predicted striatal 123I-FP-CIT SBRs based on nigrosome MRI with high correlation using manually-measured values, enabling nigrosome MRI as a biomarker for nigrostriatal dopaminergic degeneration in Parkinsonism.

Characteristics of perivascular space dilatation in normal aging.

The increased incidence of dilated perivascular spaces (dPVSs) visible on MRI has been observed with advancing age, but the relevance of PVS dilatation to normal aging across the lifespan has yet to be fully clarified. In the current study, we sought to find out the age dependence of dPVSs by exploring changes in different characteristics of PVS dilatation across a wide range of age. For 1220 healthy subjects aged between 18 and 100 years, PVSs were automatically segmented and characteristics of PVS dilatation were assessed in terms of the burden, location, and morphology of PVSs in the white matter (WM) and basal ganglia (BG). A machine learning model using the random forests method was constructed to estimate the subjects' age by employing the PVS features. The constructed machine learning model was able to estimate the age of the subjects with an error of 9.53 years on average (correlation = 0.875). The importance of the PVS features indicated the primary contribution of the burden of PVSs in the BG and the additional contribution of locational and morphological changes of PVSs, specifically peripheral extension and reduced linearity, in the WM to age estimation. Indeed, adding the PVS location or morphology features to the PVS burden features provided an improvement to the performance of age estimation. The age dependence of dPVSs in terms of such various characteristics of PVS dilatation in healthy subjects could provide a more comprehensive reference for detecting brain disease-related PVS dilatation.

Feasibility and intra-and interobserver reproducibility of quantitative susceptibility mapping with radiomic features for intracranial dissecting intramural hematomas and atherosclerotic calcifications.

Quantitative susceptibility mapping (QSM) for 61 patients with dissecting intramural hematomas (n = 36) or atherosclerotic calcifications (n = 25) in intracranial vertebral arteries were collected to assess intra- and interobserver reproducibility in a 3.0-T MR system between January 2015 and December 2017. Two independent observers each segmented regions of interest for lesions twice. The reproducibility was evaluated using intra-class correlation coefficients (ICC) and within-subject coefficients of variation (wCV) for means and concordance correlation coefficients (CCC) and ICC for radiomic features (CCC and ICC > 0.85) were used. Mean QSM values were 0.277 ± 0.092 ppm for dissecting intramural hematomas and - 0.208 ± 0.078 ppm for atherosclerotic calcifications. ICCs and wCVs were 0.885-0.969 and 6.5-13.7% in atherosclerotic calcifications and 0.712-0.865 and 12.4-18.7% in dissecting intramural hematomas, respectively. A total of 9 and 19 reproducible radiomic features were observed in dissecting intramural hematomas and atherosclerotic calcifications, respectively. QSM measurements in dissecting intramural hematomas and atherosclerotic calcifications were feasible and reproducible between intra- and interobserver comparisons, and some reproducible radiomic features were demonstrated.

MRI-visible Perivascular Spaces in the Neonatal Brain.

Background Mounting evidence suggests that perivascular spaces (PVSs) visible at MRI reflect the function of the glymphatic system. Understanding PVS burden in neonates may guide research on early glymphatic-related pathologic abnormalities. Purpose To perform a visual and volumetric evaluation of PVSs that are visible at MRI in neonates and to evaluate potential associations with maturation, sex, and preterm birth. Materials and Methods In this retrospective study, T2-weighted brain MRI scans in neonates from the Developing Human Connectome Project were used for visual grading (grades 0-4) of PVSs in the basal ganglia (BG) and white matter (WM) and for volumetric analysis of BG PVSs. The BG PVS fraction was obtained by dividing the BG PVS volume by the deep gray matter volume. The association between postmenstrual age at MRI and BG PVS burden was evaluated using linear regression. PVS burden was compared according to sex and preterm birth using the Mann-Whitney test. Results A total of 244 neonates were evaluated (median gestational age at birth, 39 weeks; IQR, 6 weeks; 145 male neonates; 59%), including 88 preterm neonates (median gestational age at birth, 33 weeks; IQR, 6 weeks; 53 male neonates; 60%) and 156 term neonates (median gestational age at birth, 40 weeks; IQR, 2 weeks; 92 male neonates; 59%). For BG PVSs, all neonates showed either grade 0 (90 of 244; 37%) or grade 1 (154 of 244; 63%), and for WM PVSs, most neonates showed grade 0 (227 of 244; 93%). The BG PVS fraction demonstrated a negative relationship with postmenstrual age at MRI (r = -0.008; P < .001). No evidence of differences was found between the sexes for BG PVS volume (P = .07) or BG PVS fraction (P = .28). The BG PVS volume was smaller in preterm neonates than in term neonates (median, 45.3 mm3 [IQR, 15.2 mm3] vs 49.9 mm3 [IQR, 21.3 mm3], respectively; P = .04). Conclusion The fraction of perivascular spaces (PVSs) in the basal ganglia (BG) was lower with higher postmenstrual age at MRI. Preterm birth affected the volume of PVSs in the BG, but sex did not. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Huisman in this issue.

χ-Separation Imaging for Diagnosis of Multiple Sclerosis versus Neuromyelitis Optica Spectrum Disorder.

Background Use of χ-separation imaging can provide surrogates for iron and myelin that relate closely to abnormal changes in multiple sclerosis (MS) lesions. Purpose To evaluate the appearances of MS and neuromyelitis optica spectrum disorder (NMOSD) brain lesions on χ-separation maps and explore their diagnostic value in differentiating the two diseases in comparison with previously reported diagnostic criteria. Materials and Methods This prospective study included individuals with MS or NMOSD who underwent χ-separation imaging from October 2017 to October 2020. Positive (χpos) and negative (χneg) susceptibility were estimated separately by using local frequency shifts and calculating R2' (R2' = R2* - R2). R2 mapping was performed with a machine learning approach. For each lesion, presence of the central vein sign (CVS) and paramagnetic rim sign (PRS) and signal characteristics on χneg and χpos maps were assessed and compared. For each participant, the proportion of lesions with CVS, PRS, and hypodiamagnetism was calculated. Diagnostic performances were assessed using receiver operating characteristic (ROC) curve analysis. Results A total of 32 participants with MS (mean age, 34 years ± 10 [SD]; 25 women, seven men) and 15 with NMOSD (mean age, 52 years ± 17; 14 women, one man) were evaluated, with a total of 611 MS and 225 NMOSD brain lesions. On the χneg maps, 80.2% (490 of 611) of MS lesions were categorized as hypodiamagnetic versus 13.8% (31 of 225) of NMOSD lesions (P < .001). Lesion appearances on the χpos maps showed no evidence of a difference between the two diseases. In per-participant analysis, participants with MS showed a higher proportion of hypodiamagnetic lesions (83%; IQR, 72-93) than those with NMOSD (6%; IQR, 0-14; P < .001). The proportion of hypodiamagnetic lesions achieved excellent diagnostic performance (area under the ROC curve, 0.96; 95% CI: 0.91, 1.00). Conclusion On χ-separation maps, multiple sclerosis (MS) lesions tend to be hypodiamagnetic, which can serve as an important hallmark to differentiate MS from neuromyelitis optica spectrum disorder. © RSNA, 2022 Supplemental material is available for this article.

Diagnosis of Marginal Infiltration in Soft Tissue Sarcoma by Radiomics Approach Using T2-Weighted Dixon Sequence.

BACKGROUND: Determination of preoperative soft tissue sarcoma (STS) margin is crucial for patient prognosis. PURPOSE: To evaluate diagnostic performance of radiomics model using T2-weighted Dixon sequence for infiltration degree of STS margin. STUDY TYPE: Retrospective. POPULATION: Seventy-two STS patients consisted of training (n = 58) and test (n = 14) sets. FIELD STRENGTH/SEQUENCE: A 3.0 T; T2-weighted Dixon images. ASSESSMENT: Pathologic result of marginal infiltration in STS (circumscribed margin; n = 27, group 1, focally infiltrative margin; n = 31, group 2-A, diffusely infiltrative margin; n = 14, group 2-B) was the reference standard. Radiomic volume and shape (VS) and other (T2) features were extracted from entire tumor volume and margin, respectively. Twelve radiomics models were generated using four combinations of classifier algorithms (R, SR, LR, LSR) and three different inputs (VS, T2, VS + T2 [VST2] features) to differentiate the three groups. Three radiologists (reader 1, 2, 3) analyzed the marginal infiltration with 6-scale confidence score. STATISTICAL TESTS: Area under the receiver operating characteristic curve (AUC) and concordance rate. RESULTS: Averaged AUCs of R, SR, LR, LSR models were 0.438, 0.466, 0.438, 0.466 using VS features, 0.596, 0.584, 0.814, 0.815 using T2 features, and 0.581, 0.587, 0.821, 0.821 using VST2 features, respectively. The LR and LSR models constructed with T2 or VST2 features showed higher AUC and concordance rate compared to radiologists' analysis (AUC; 0.730, 0.675, 0.706, concordance rate; 0.46, 0.43, 0.47 in reader 1, 2, 3). DATA CONCLUSION: Radiomics model constructed with features from tumor margin on T2-weighted Dixon sequence is a promising method for differentiating infiltration degree of STS margin. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

MRI-visible Dilated Perivascular Space in the Brain by Age: The Human Connectome Project.

Background Dilated perivascular spaces (dPVS) are associated with aging and various disorders; however, the effect of age on dPVS burden in young populations and normative data have not been fully evaluated. Purpose To investigate the dPVS burden and provide normative data according to age in a healthy population, including children. Materials and Methods In this retrospective study, three-dimensional T2-weighted brain MRI scans from the Human Connectome Project data sets were used for visual grading (grade 0, 1, 2, 3, 4 for 0, 1-10, 11-20, 21-40, and >40 dPVS on a single section of either hemispheric region) and automated volumetry of dPVS in basal ganglia (BGdPVS) and white matter (WMdPVS). Linear and nonlinear regression were performed to assess the association of dPVS volume with age. Optimal cutoff ages were determined with use of the maximized continuous-scale C-index. Participants were grouped by cutoff values. Linear regression was performed to assess the age-dPVS volume relationship in each age group. Normative data of dPVS visual grades were provided per age decade. Results A total of 1789 participants (mean age, 35 years; age range, 8-100 years; 1006 female participants) were evaluated. Age was related to dPVS volume in all regression models (R2 range, 0.41-0.55; P < .001). Age-dPVS volume relationships were altered at the mid-30s and age 55 years; BGdPVS and WMdPVS volumes negatively correlated with age until the mid-30s (ß, -1.2 and -7.8), then positively until age 55 years (ß, 3.3 and 54.1) and beyond (ß, 3.9 and 42.8; P < .001). The 90th percentile for dPVS grades was grade 1 for age 49 years and younger, grade 2 for age 50-69 years, and grade 3 for age 70 years and older (overall, grade 2) for BGdPVS, and grade 3 for age 49 years and younger and grade 4 for age 50 years and older (overall, grade 3) for WMdPVS. Conclusion Dilated perivascular spaces (dPVS) showed a biphasic volume pattern with brain MRI, lower volumes until the mid-30s, then higher afterward. Grades of 3 or higher and 4 might be considered pathologic dPVS in basal ganglia and white matter, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Bapuraj and Chaudhary in this issue.

MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI. METHODS: This retrospective study included T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) images from 185 clinical scans: 135 for DNN training, 11 for DNN validation, 20 for qualitative evaluation, and 19 for quantitative evaluation. Additionally, 18 vessel wall imaging (VWI) data were included to evaluate generalization. In each scan of the DNN training set, two noise-independent images were generated from the k-space data, resulting in an input-label pair. 2.5D U-net architecture was utilized for the DNN model. Qualitative evaluation between conventional MP-RAGE and DNN-based MP-RAGE was performed by two radiologists in image quality, fine structure delineation, and lesion conspicuity. Quantitative evaluation was performed with full sampled data as a reference by measuring quantitative error metrics and volumetry at 7 different simulated noise levels. DNN application on VWI was evaluated by two radiologists in image quality. RESULTS: Our DNN-based MP-RAGE outperformed conventional MP-RAGE in all image quality parameters (average scores = 3.7 vs. 4.9, p < 0.001). In the quantitative evaluation, DNN showed better error metrics (p < 0.001) and comparable (p > 0.09) or better (p < 0.02) volumetry results than conventional MP-RAGE. DNN application to VWI also revealed improved image quality (3.5 vs. 4.6, p < 0.001). CONCLUSION: The proposed DNN model successfully denoises 3D MR image and improves its image quality by using routine clinical scans only. KEY POINTS: • Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI.

[Applications of Artificial Intelligence in MR Image Acquisition and Reconstruction]. / MRI ì‹ í˜¸íšë“ê³¼ 영상재구성에서의 인공지능 ì ìš©.

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

Diagnosis of nasal bone fractures on plain radiographs via convolutional neural networks.

This study aimed to assess the performance of deep learning (DL) algorithms in the diagnosis of nasal bone fractures on radiographs and compare it with that of experienced radiologists. In this retrospective study, 6713 patients whose nasal radiographs were examined for suspected nasal bone fractures between January 2009 and October 2020 were assessed. Our dataset was randomly split into training (n = 4325), validation (n = 481), and internal test (n = 1250) sets; a separate external dataset (n = 102) was used. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the DL algorithm and the two radiologists were compared. The AUCs of the DL algorithm for the internal and external test sets were 0.85 (95% CI, 0.83-0.86) and 0.86 (95% CI, 0.78-0.93), respectively, and those of the two radiologists for the external test set were 0.80 (95% CI, 0.73-0.87) and 0.75 (95% CI, 0.68-0.82). The DL algorithm therefore significantly exceeded radiologist 2 (P = 0.021) but did not significantly differ from radiologist 1 (P = 0.142). The sensitivity and specificity of the DL algorithm were 83.1% (95% CI, 71.2-93.2%) and 83.7% (95% CI, 69.8-93.0%), respectively. Our DL algorithm performs comparably to experienced radiologists in diagnosing nasal bone fractures on radiographs.

[Brain Iron Imaging in Aging and Cognitive Disorders: MRI Approaches]. / ë ¸í™” 및 ì¸ì§€ê¸°ëŠ¥ìž¥ì• ì—ì„œ 뇌 ì² ì˜ìƒ 기법: ìžê¸°ê³µëª ì˜ìƒì„ 이용한 ì ‘ê·¼.

Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer's disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future.

[MRI Findings in Parkinson's Disease: Radiologic Assessment of Nigrostriatal Degeneration]. / 파킨슨병의 ìžê¸°ê³µëª ì˜ìƒ 소견: í‘ì§ˆì„ ì¡°ì²´ 변성의 ì˜ìƒí•™ì  평가.

Parkinson's disease (PD) is a movement disorder that develops due to degenerative loss of dopaminergic cells in the substantia nigra of the midbrain. Recent advances in MRI techniques have demonstrated various imaging findings that can reflect the underlying pathophysiological processes occurring in Parkinson's disease. Many imaging studies have shown that such findings can assist in the diagnosis of Parkinson's disease and its differentiation from atypical parkinsonism. In this review, we present MRI techniques that can be used in clinical assessment, such as nigrosome imaging and neuromelanin imaging, and we provide the detailed imaging features of Parkinson's disease reflecting nigrostriatal degeneration.

Clinical Implications of Focal Mineral Deposition in the Globus Pallidus on CT and Quantitative Susceptibility Mapping of MRI.

OBJECTIVE: To assess focal mineral deposition in the globus pallidus (GP) by CT and quantitative susceptibility mapping (QSM) of MRI scans and evaluate its clinical significance, particularly cerebrovascular degeneration. MATERIALS AND METHODS: This study included 105 patients (66.1 ± 13.7 years; 40 male and 65 female) who underwent both CT and MRI with available QSM data between January 2017 and December 2019. The presence of focal mineral deposition in the GP on QSM (GPQSM) and CT (GPCT) was assessed visually using a three-point scale. Cerebrovascular risk factors and small vessel disease (SVD) imaging markers were also assessed. The clinical and radiological findings were compared between the different grades of GPQSM and GPCT. The relationship between GP grades and cerebrovascular risk factors and SVD imaging markers was assessed using univariable and multivariable linear regression analyses. RESULTS: GPCT and GPQSM were significantly associated (p < 0.001) but were not identical. Higher GPCT and GPQSM grades showed smaller gray matter (p = 0.030 and p = 0.025, respectively) and white matter (p = 0.013 and p = 0.019, respectively) volumes, as well as larger GP volumes (p < 0.001 for both). Among SVD markers, white matter hyperintensity was significantly associated with GPCT (p = 0.006) and brain atrophy was significantly associated with GPQSM (p = 0.032) in at univariable analysis. In multivariable analysis, the normalized volume of the GP was independently positively associated with GPCT (p < 0.001) and GPQSM (p = 0.002), while the normalized volume of the GM was independently negatively associated with GPCT (p = 0.040) and GPQSM (p = 0.035). CONCLUSION: Focal mineral deposition in the GP on CT and QSM might be a potential imaging marker of cerebral vascular degeneration. Both were associated with increased GP volume.

Ipsilateral Recurrence of DCIS in Relation to Radiomics Features on Contrast Enhanced Breast MRI.

The purpose of this retrospective study was to investigate the association between ipsilateral recurrence of ductal carcinoma in situ (DCIS) and radiomics features from DCIS and contralateral normal breast on contrast enhanced breast MR imaging. A total of 163 patients with DCIS who underwent preoperative MR imaging between January 2010 and December 2014 were included (training cohort; n = 117, validation cohort; n = 46). Radiomics features were extracted from whole tumor volume of DCIS on early dynamic T1-subtraction images and from the contralateral normal breast on precontrast T1 and early dynamic T1-subtraction images. After feature selection, a Rad-score was established by LASSO Cox regression model. Performance of Rad-score was evaluated by the receiver operating characteristic (ROC) curve and Kaplan Meier curve with log rank test. The Rad-score was significantly associated with ipsilateral recurrence free survival (RFS). The low-risk group with a low Rad-score showed higher ipsilateral RFS than the high-risk group with a high Rad-score in both training and validation cohorts (p < 0.01). The Rad-score based on radiomics features from DCIS and contralateral normal breast on breast MR imaging showed the potential for prediction of ipsilateral RFS of DCIS.

A prospective multi-centre study of susceptibility map-weighted MRI for the diagnosis of neurodegenerative parkinsonism.

OBJECTIVES: This study aimed to compare susceptibility map-weighted imaging (SMwI) using various MRI machines (three vendors) with N-3-fluoropropyl-2-ß-carbomethoxy-3-ß-(4-iodophe nyl)nortropane (18F-FP-CIT) PET in the diagnosis of neurodegenerative parkinsonism in a multi-centre setting. METHODS: We prospectively recruited 257 subjects, including 157 patients with neurodegenerative parkinsonism, 54 patients with non-neurodegenerative parkinsonism, and 46 healthy subjects from 10 hospitals between November 2019 and October 2020. All participants underwent both SMwI and 18F-FP-CIT PET. SMwI was interpreted by two independent reviewers for the presence or absence of abnormalities in nigrosome 1, and discrepancies were resolved by consensus. 18F-FP-CIT PET was used as the reference standard. Inter-observer agreement was tested using Cohen's kappa coefficient. McNemar's test was used to test the agreement between the interpretations of SMwI and 18F-FP-CIT PET per participant and substantia nigra (SN). RESULTS: The inter-observer agreement was 0.924 and 0.942 per SN and participant, respectively. The diagnostic sensitivity of SMwI was 97.9% and 99.4% per SN and participant, respectively; its specificity was 95.9% and 95.2%, respectively, and its accuracy was 97.1% and 97.7%, respectively. There was no significant difference between the results of SMwI and 18F-FP-CIT PET (p > 0.05, for both SN and participant). CONCLUSIONS: This study demonstrated that the high diagnostic performance of SMwI was maintained in a multi-centre setting with various MRI scanners, suggesting the generalisability of SMwI for determining nigrostriatal degeneration in patients with parkinsonism. KEY POINTS: • Susceptibility map-weighted imaging helps clinicians to predict nigrostriatal degeneration. • The protocol for susceptibility map-weighted imaging can be standardised across MRI vendors. • Susceptibility map-weighted imaging showed diagnostic performance comparable to that of dopamine transporter PET in a multi-centre setting with various MRI scanners.