Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding.

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.

BACKGROUND: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors.

Multi-shot Diffusion-Weighted MRI With Multiplexed Sensitivity Encoding (MUSE) in the Assessment of Active Inflammation in Crohn's Disease.

BACKGROUND: Single-shot diffusion-weighted imaging (ssDWI) has been shown useful for detecting active bowel inflammation in Crohn's disease (CD) without MRI contrast. However, ssDWI suffers from geometric distortion and low spatial resolution. PURPOSE: To compare conventional ssDWI with higher-resolution ssDWI (HR-ssDWI) and multi-shot DWI based on multiplexed sensitivity encoding (MUSE-DWI) for evaluating bowel inflammation in CD, using contrast-enhanced MR imaging (CE-MRI) as the reference standard. STUDY TYPE: Prospective. SUBJECTS: Eighty nine patients with histological diagnosis of CD from previous endoscopy (55 male/34 female, age: 17-69 years). FIELD STRENGTH/SEQUENCES: ssDWI (2.7 mm × 2.7 mm), HR-ssDWI (1.8 mm × 1.8 mm), MUSE-DWI (1.8 mm × 1.8 mm) based on echo-planar imaging, T2-weighted imaging, and CE-MRI sequences, all at 1.5 T. ASSESSMENT: Five raters independently evaluated the tissue texture conspicuity, geometry accuracy, minimization of artifacts, diagnostic confidence, and overall image quality using 5-point Likert scales. The diagnostic performance (sensitivity, specificity and accuracy) of each DWI sequences was assessed on per-bowel-segment basis. STATISTICAL TESTS: Inter-rater agreement for qualitative evaluation of each parameter was measured by the intra-class correlation coefficient (ICC). Paired Wilcoxon signed-rank tests were performed to evaluate the statistical significance of differences in qualitative scoring between DWI sequences. A P value <0.05 was considered to be statistically significant. RESULTS: Tissue texture conspicuity, geometric distortions, and overall image quality were significantly better for MUSE-DWI than for ssDWI and HR-ssDWI with good agreement among five raters (ICC: 0.70-0.89). HR-ssDWI showed significantly poorer performance to ssDWI and MUSE-DWI for all qualitative scores and had the worst diagnostic performance (sensitivity of 57.0% and accuracy of 87.3%, with 36 undiagnosable cases due to severe artifacts). MUSE-DWI showed significantly higher sensitivity (97.5% vs. 86.1%) and accuracy (98.9% vs. 95.1%) than ssDWI for detecting bowel inflammation. DATA CONCLUSION: MUSE-DWI was advantageous in assessing bowel inflammation in CD, resulting in improved spatial resolution and image quality. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.

OBJECTIVES: To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS). METHODS: Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant. RESULTS: Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm2 for GT on DWI to 0.59 cm2 for prediction at an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. CONCLUSIONS: Combining an ADC threshold of 0.6 × 10-3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs. KEY POINTS: • Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10-3 mm2/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Classification of parotid gland tumors by using multimodal MRI and deep learning.

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

Imaging quality of PROPELLER diffusion-weighted MR imaging and its diagnostic performance in distinguishing pleomorphic adenomas from Warthin tumors of the parotid gland.

The aim of this study was to evaluate the imaging quality and diagnostic performance of fast spin echo diffusion-weighted imaging with periodically rotated overlapping parallel lines with enhanced reconstruction (FSE-PROP-DWI) in distinguishing parotid pleomorphic adenoma (PMA) from Warthin tumor (WT). This retrospective study enrolled 44 parotid gland tumors from 34 patients, including 15 PMAs and 29 WTs with waived written informed consent. All participants underwent 1.5 T diffusion-weighted imaging including FSE-PROP-DWI and single-shot echo-planar diffusion-weighted imaging (SS-EP-DWI). After imaging resizing and registration among T2WI, FSE-PROP-DWI and SS-EP-DWI, imaging distortion was quantitatively analyzed by using the Dice coefficient. Signal-to-noise ratio and contrast-to-noise ratio were qualitatively evaluated. The mean apparent diffusion coefficient (ADC) of parotid gland tumors was calculated. Wilcoxon signed-rank test was used for paired comparison between FSE-PROP-DWI versus SS-EP-DWI. Mann-Whitney U test was used for independent group comparison between PMAs versus WTs. Diagnostic performance was evaluated by receiver operating characteristics curve analysis. P < 0.05 was considered statistically significant. The Dice coefficient was statistically significantly higher on FSE-PROP-DWI than SS-EP-DWI for both tumors (P < 0.005). Mean ADC was statistically significantly higher in PMAs than WTs on both FSE-PROP-DWI and SS-EP-DWI (P < 0.005). FSE-PROP-DWI and SS-EP-DWI successfully distinguished PMAs from WTs with an AUC of 0.880 and 0.945, respectively (P < 0.05). Sensitivity, specificity, positive predictive value, negative predictive value and accuracy in diagnosing PMAs were 100%, 69.0%, 62.5%, 100% and 79.5% for FSE-PROP-DWI, and 100%, 82.8%, 75%, 100% and 88.6% for SS-EP-DWI, respectively. FSE-PROP-DWI is useful to distinguish parotid PMAs from WTs with less distortion of tumors but lower AUC than SS-EP-DWI.

Proton change of parotid glands after gustatory stimulation examined by magnetic resonance imaging.

The aim of this study was to investigate proton changes of the parotid gland after gustatory stimulation by semi-quantitative parameters and an empirical mathematical model (EMM) using high-temporal-resolution, double-echo, echo-planar imaging (EPI). Approved by a local institutional review board, this study examined 20 parotid glands from 10 healthy volunteers (male:female = 6: 4; age ± standard deviation =35.1 ± 14.1 years) with written informed consent obtained. All participants underwent 1.5-T, double-echo EPI with gustatory stimulation. Semi-quantitative parameters, including maximal drop ratio (MDR), time to peak (TTP), drop slope (DS), recovery slope (RS) and recovery ratio (RR), were calculated. The effect of temporal resolution on parotid functional parameters was evaluated. An EMM comprising an output function ( Sot=Aoe-kot+B) and an input function ( Sint=Ain1-e-kint) was also applied to fit all dynamic curves. Kruskal-Wallis test, Wilcoxon test, linear regression analysis and goodness of fit were used for statistical analysis. p < 0.05 was considered to be statistically significant. The signal intensity dropped significantly after gustatory stimulation on the proton density (PD) image (p < 0.01). MDR was 8.26% in the PD image. MDR and RR were negatively associated with time interval, whereas DS and TTP were significantly positively associated with time interval (all p < 0.05). EMM parametric values derived from PD-time curves of parotid glands were 12.04 ± 6.81%, 6.43 ± 4.23 min-1 , 88.73 ± 6.18%, 8.41 ± 4.86 min-1 and 1.09 ± 1.35 for Ao , ko , B, Ain and kin , respectively. Semi-quantitative functional parameters and EMM parameters using high-temporal-resolution, double-echo EPI allow the quantification of parotid proton changes after gustatory stimulation.

JOURNAL CLUB: The Warthin Tumor Score: A Simple and Reliable Method to Distinguish Warthin Tumors From Pleomorphic Adenomas and Carcinomas.

OBJECTIVE: The objective of this article is to propose a Warthin tumor (WT) score to distinguish WTs from other parotid tumors. MATERIALS AND METHODS: The study included 78 patients with 92 histologically proven parotid tumors, including 42 WTs, 30 pleomorphic adenomas (PMAs), and 20 carcinomas. Echo-planar DW images were acquired. The WT score, which comprised the mean apparent diffusion coefficient (ADCM) and the SD of the ADC (ADCSD) of tumors, patient age, and patient sex, was used to predict WTs. The diagnostic performance of the WT score was evaluated using ROC analyses. Statistical significance was denoted by p < 0.05. RESULTS: With the use of optimized criteria, including an ADCM less than or equal to 1.016 × 10-3 mm2/s (WT score, 1), an ADCSD less than or equal to 0.1171 × 10-3 mm2/s (WT score, 1), patient age older than 49 years (WT score, 1), and male sex (WT score, 1), a WT score greater than 2 had a sensitivity, specificity, positive negative value, negative predictive value, and accuracy of 85.7%, 100.0%, 100.0%, 89.3%, and 93.4%, respectively. CONCLUSION: The WT score allows parotid WTs to be distinguished from PMAs and carcinomas with high accuracy.

Effects of gender, age, and body mass index on fat contents and apparent diffusion coefficients in healthy parotid glands: an MRI evaluation.

OBJECTIVES: To establish standard apparent diffusion coefficient (ADC) and the fat content as a function of age, gender and body mass index (BMI) in healthy parotid glands, and to address the influences of fat suppression on ADC measurements. METHODS: A total of 100 healthy adults (gender and age evenly distributed) were prospectively recruited, with parotid fat content measured from gradient-echo images with fat-water separated using iterative decomposition with echo asymmetry and least squares (IDEAL). The ADCs were estimated using both fat-saturated and non-fat-saturated diffusion-weighted imaging via a periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique. RESULTS: Parotid fat content was larger in men than in women by about 10 percentage points (P < 0.005), and positively associated with BMI and age for both genders (mostly with P < 0.001). ADCs estimated with non-fat-saturated PROPELLER were significantly lower in men than in women (P < 0.005), but showed no gender difference if measured using fat-saturated PROPELLER (P = 0.840). The negative association between parotid ADC and age/BMI/fat (P < 0.001) showed greater regression slopes in non-fat-saturated PROPELLER than in fat-saturated data. CONCLUSIONS: Parotid fat content in healthy adults correlates positively with both age and BMI; the correlation with age is gender-dependent. Parotid ADC measurements are strongly influenced by fat saturation. KEY POINTS: Parotid fat content in healthy adults correlates positively with age and BMI. The rate of aging-related increase in fat contents is gender-dependent. Parotid ADC measurements are strongly influenced by fat saturation.

Understanding ADC variation by fat content effect using a dual-function MRI phantom.

BACKGROUND: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. METHODS: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the "iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ," IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. RESULTS: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R2 = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. CONCLUSIONS: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. RELEVANCE STATEMENT: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. KEY POINTS: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values are determined by GBD and FF. • The dual-function phantom showed the mutual ADC addition between FF and GBD.

Effects of microvascular permeability changes on contrast-enhanced T1 and pharmacokinetic MR imagings after ischemia.

BACKGROUND AND PURPOSE: Brain enhancement on contrast-enhanced T1-weighted imaging (CET1-WI) after ischemic stroke is generally accepted as an indicator of the blood-brain barrier disruption. However, this phenomenon usually starts to become visible at the subacute phase. The purpose of this study was to evaluate the time-course profiles of K(trans), cerebral blood volume (vp), and CET1-WI with early detection of blood-brain barrier changes on K(trans) maps and their role for prediction of subsequent hemorrhagic transformation in acute middle cerebral arterial infarct. METHODS: Twenty-six patients with acute middle cerebral arterial stroke and early spontaneous reperfusion, whose MR images were obtained at predetermined stroke stages, were included. T2*-based MR perfusion-weighted images were acquired using the first-pass pharmacokinetic model to derive K(trans) and vp. Parenchymal enhancement observed on maps of K(trans), vp, and CET1-WI at each stage was compared. Association among these measurements and hemorrhagic transformation was analyzed. RESULTS: K(trans) map showed significantly higher parenchymal enhancement in ischemic parenchyma as compared with that of vp map and CET1-WI at early stroke stages (P<0.05). The increased K(trans) at acute stage was not associated with parenchymal enhancement in CET1-WI at the same stage. Parenchymal enhancement in CET1-WI started to occur at the late subacute stage and tended to be luxury reperfusion-dependent. Patients with hemorrhagic transformation showed higher mean K(trans) values as compared with patients without hemorrhagic transformation (P=0.02). CONCLUSIONS: Postischemic brain enhancement on routine CET1-WI seems to be closely related to the luxury reperfusion at the late subacute stage and is not dependent on microvascular permeability changes at the acute stage.

Parotid fat contents in healthy subjects evaluated with iterative decomposition with echo asymmetry and least squares fat-water separation.

PURPOSE: To evaluate the effectiveness of three fat measurement methods for parotid glands in healthy subjects, with or without metallic dental implants. MATERIALS AND METHODS: The institutional review board approved this study, with informed consent obtained from 114 volunteers undergoing magnetic resonance (MR) imaging at 1.5 T. Fat-saturated (FS) and non-fat-saturated (NFS) fast spin-echo T1-weighted imaging (T1 method), FS and NFS T2-weighted periodically rotated overlapping parallel lines with enhanced reconstruction fast spin-echo imaging (T2 method), and gradient-echo imaging with fat-water separation using iterative decomposition with echo asymmetry and least squares (IDEAL) method were used to derive parotid fat contents. Two raters examined the homogeneity of fat saturation to determine whether parotid fat quantification was successful, with the success rate in the 114 subjects recorded for each protocol. In subjects whose fat quantification was successful with all three imaging methods, linear regression was used to analyze the correlation between any pair of the three parotid fat content measurement methods. RESULTS: Success rates in parotid fat measurements by using T1, T2, and IDEAL methods were 87.7% (100 of 114), 87.7% (100 of 114), and 100% (114 of 114), respectively. The means of measured parotid fat contents revealed significant differences (P < .001) between any pair of the three measurement methods. The parotid fat contents measured with the three methods were significantly correlated with each other between any pair of combinations. CONCLUSION: The IDEAL method provided a high success rate for parotid fat measurements, even in subjects with metallic dental implants.

Sudden death due to medulloblastoma: a case report.

PURPOSE: Medulloblastoma is one of the notorious CNS malignancies for subtle and atypical clinical presentations, causing rapid neurological deterioration and death, especially in pediatric patients. The delay in diagnosis leads to painful remorse, conflicts, and lawsuits for parents and medical staff. CASE REPORT: We report a 2 year old girl with initial presentation of febrile pyuria. Soon after admission, a generalized clonic-tonic seizure attacked to her and led to an impression of febrile convulsion. However, an unusual postical slowness of pupils to light stimulation propelled a further investigation. A contrast enhanced brain computer tomography (CT) unexpectedly showed a mass occupied the fourth ventricle resulting in obstructive hydrocephalus and compressed adjacent brain stem and cerebellum. The disease rapidly progressed and she died 18 hours after an emergent decompression with extraventricular drainage (EVD) installation. Cytology of cerebrospinal fluid proved medulloblastoma. CONCLUSION: This case report highlights the importance of clinical suspicion, such as a trivial but unusual presentation, a lagged pupil response to light stimulation. A brain CT scan should be done to rule out any possibility of an organic lesion. Close monitor is required in order to catch and treat medulloblastoma early. However, once discovered, the cancer has spread.

Utilization of CT and MRI scanning in Taiwan, 2000-2017.

OBJECTIVES: This population-based study aimed to collect, analyze, and summarize the long-term trends in medical imaging use in Taiwan. METHODS: A retrospective cohort population-based study of medical imaging usage for the individuals who received care under the National Health Insurance system from 2000 to 2017. CT and MRI utilization rates were determined overall as well as across certain variables including patient age, hospital type, health care type, hospital characteristics, and geographic area. RESULTS: Individuals registered in our health insurance system have received 21,766,745 CT scans and 7,520,088 MRI scans from 2000 to 2017. Annual growth rates for both imaging types were positive over that period, though growth rates have slowed in recent years. The growth rate for CT use was greatest (9-12%) between 2001 and 2004, dropped to 2% in 2005, then generally rose thereafter, reaching 3% in 2017. Similarly, MRI use growth peaked at 24% between 2001 and 2003, dropped to 4% in 2005, then increased in a fluctuating manner, reaching 2% in 2017. CONCLUSION: Over the past 2 decades, CT and MRI use in Taiwan has increased sharply, especially in the oldest age group (≥ 60 years old), but growth rates have slowed in recent years. Increases in imaging use have corresponded with improved clinical outcomes, including greater life expectancy and reduced mortality rates, though further assessment is required to demonstrate a direct link with imaging. Nevertheless, the better clinical outcomes are also predisposed by the comprehensive care covered by the NHI system.

Delayed parkinsonism after CO intoxication: evaluation of the substantia nigra with inversion-recovery MR imaging.

PURPOSE: To quantitatively investigate signal alterations of the substantia nigra in patients with delayed parkinsonism following CO intoxication, as seen on gray matter (GM)-suppressed inversion-recovery (IR) magnetic resonance (MR) images. MATERIALS AND METHODS: This prospective study was approved by the local institutional review board, and written informed consent was obtained from all subjects. Thirteen patients with delayed onset of CO-induced parkinsonism (nine men and four women; mean age, 40.3 years), 13 age-matched CO-intoxicated patients without parkinsonism, and 13 age-matched healthy volunteers were examined with GM-suppressed IR MR imaging. The signal intensity of the substantia nigra was normalized to the adjacent normal-appearing white matter in the temporal lobe, followed by semiautomatic segmentation into medial, middle, and lateral parts by using a skeleton-based algorithm. Multivariate and univariate analyses and Spearman rank correlation test were performed to examine the relationships between variables. Clinical severity was assessed with the modified Hoehn and Yahr rating scale. RESULTS: The normalized signal ratios in the middle and lateral segments of the substantia nigra were significantly higher in those with CO-induced parkinsonism, compared with those with CO intoxication without parkinsonism or normal volunteers (P=.02). For the medial segments, the ratios showed no significant differences among the groups. The normalized signal ratios of substantia nigra were correlated with the severity of parkinsonism, particularly in the lateral segments (ρ=0.927, P<.001). CONCLUSION: CO toxicity to the substantia nigra plays a role in pathophysiologic mechanisms of CO-induced parkinsonism. GM-suppressed IR MR imaging is a useful tool in depicting substantia nigra injury following CO intoxication.

The cerebral vasomotor response in varying CO(2) concentrations, as evaluated using cine phase contrast MRI: Flow, volume, and cerebrovascular resistance indices.

PURPOSE: Previous studies have identified that impaired cerebral vasomotor reactivity (VMR) is associated with a higher risk of stroke and transient ischemic attack. This study aims to evaluate VMR by measuring the blood flow waveforms of the supplying arteries and dural sinuses using cine phase contrast MRI (PC MRI) and hypercapnic challenge. METHODS: PC MRI flow quantification was performed on an oblique slice approximately perpendicular to the target vessels to include the left (LICA) and right internal carotid artery (RICA), basilar artery (BA), sinus rectus (SR), and superior sagittal sinus (SSS). A total of four PC MRI scans were performed at different CO(2) concentrations (room air and 3%, 5%, and 7% CO(2)). RESULTS: The analyses obtained the flow parameters and cerebrovascular resistance parameters for all five vessels. Results indicated that the vascular resistance indices decreased with increasing CO(2) concentration in four vessels (LICA, RICA, BA, and SR). The obtained VMR parameters demonstrated exponential increases with increasing CO(2) concentration. CONCLUSIONS: Using entire blood flow waveforms, this study applied separate flow dynamics during systolic and diastolic periods to obtain cerebrovascular resistance parameters and extensive flow-related information. It is the first to investigate the cerebrovascular resistance parameters under hypercapnic challenge using cine MRI. This technique could provide a useful tool for clinical application in cerebrovascular disease.