Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

"Acknowledgements" section should be inserted in this article.

Hemifacial microsomia (HFM) is the most common craniofacial anomaly after cleft lip and cleft palate; this deformity primarily involves the facial skeleton and ear, with either underdevelopment or absence of both components. In patients with HFM, the management of the asymmetries requires a series of treatment phases that focus on their interception and correction, such as distraction osteogenesis or functional appliance treatment during growth and presurgical orthodontic treatment followed by mandibular and maxillary surgery. Satisfactory results were obtained in a 9-year-old girl with HFM who was treated with distraction osteogenesis. At the age of 19, genioplasty and mandible body augmentation with a porous polyethylene implant (PPE, Medpor(R), Porex) was sequentially performed for the functional and esthetic reconstruction of the face. We report a case of HFM with a review of the literature.
Child ; Cleft Lip ; Cleft Palate ; Congenital Abnormalities ; Ear ; Female ; Follow-Up Studies* ; Genioplasty ; Goldenhar Syndrome* ; Humans ; Mandible ; Osteogenesis, Distraction ; Polyethylene ; Skeleton

Great advances have been made in the field of assisted reproductive technology (ART) since the first in vitro fertilization (IVF) baby was born in Korea in the year of 1985. However, it deserve to say that the invaluable data from fertility centers may serve as a useful source to find out which factors affect successful IVF outcome and to offer applicable information to infertile patients and fertility clinics. This article intended to report the status of ART in 2009 Korean Society of Obstetrics and Gynecology surveyed. The current survey was performed to assess the status and success rate of ART performed in Korea, between January 1 and December 31, 2009. Reporting forms had been sent out to IVF centers via e-mail, and collected by e-mail as well in 2012. With International Committee Monitoring Assisted Reproductive Technologies recommendation, intracytoplasmic sperm injection (ICSI) and non-ICSI cases have been categorized and also IVF-ET cases involving frozen embryo replacement have been surveyed separately. Seventy-four centers have reported the treatment cycles initiated in the year of 2009, and had performed a total of 27,947 cycles of ART treatments. Among a total of 27,947 treatment cycles, IVF and ICSI cases added up to 22,049 (78.9%), with 45.3% IVF without ICSI and 54.7% IVF with ICSI, respectively. Among the IVF and ICSI patients, patients confirmed to have achieved clinical pregnancy was 28.8% per cycle with oocyte retrieval, and 30.9% per cycle with embryo transfer. The most common number of embryos transferred in 2009 is three embryos (40.4%), followed by 2 embryos (28.4%) and a single embryo transferred (13.6%). Among IVF and ICSI cycles that resulted in multiple live births, twin pregnancy rate was 45.3% and triple pregnancy rate was 1.1%. A total of 191 cases of oocyte donation had been performed to result in 25.0% of live birth rate. Meanwhile, a total of 5,619 cases of frozen embryo replacement had been performed with 33.7% of clinical pregnancy rate per cycle with embryo transfer. When comparing with international registry data, clinical pregnancy rate per transfer from fresh IVF cycles including ICSI (34.1%,) was comparable to clinical pregnancy rate per transfer in European Society for Human Reproduction and Embryology report was 32.5% though lower than 45.0% for USA data. There was no remarkable difference in status of assisted reproductive technology in Korea between the current report and the data reported in 2008. The age of women trying to get pregnant was reconfirmed to be the most important factor that may have impact on success of ART treatment.
Electronic Mail ; Embryo Transfer ; Female ; Fertility ; Fertilization in Vitro ; Humans ; Korea* ; Live Birth ; Oocyte Donation ; Oocyte Retrieval ; Pregnancy ; Pregnancy Rate ; Pregnancy, Twin ; Reproduction ; Reproductive Techniques ; Reproductive Techniques, Assisted* ; Sperm Injections, Intracytoplasmic

Persistent trigeminal artery is an embryonic remnant of the anastomotic channel linking the internal carotid artery and the basilar artery. Cases of vertebrobasilar insufficiency caused by the persistent trigeminal artery with internal carotid artery stenosis has been described previously, but vertebrobasilar insufficiency entirely due to in situ stenosis of the persistent trigeminal artery has not been reported. A 71-year-old man presented with frequent dizzy episodes. The brain MRI showed no parenchymal lesions. MR angiography showed poor visualization of vertebrobasilar system. He was diagnosed as having vertebrobasilar insufficiency. Cerebral angiography revealed that there was complete occlusion at the vertebrobasilar junction, and the basilar artery was supplied by the persistent trigeminal artery which had severe stenosis at its origin. There was no stenosis of the internal carotid artery of both sides. We believe that this is the first report of vertebrobasilar insufficiency due to stenosed persistent trigeminal artery, without internal carotid artery stenosis.

PURPOSE: Cardiac troponin T (cTnT), a useful marker for diagnosing acute myocardial infarction (AMI) in the general population, is significantly higher than the usual cut-off value in many end-stage renal disease (ESRD) patients without clinically apparent evidence of AMI. The aim of this study was to evaluate the clinical usefulness of cTnT in ESRD patients with acute coronary syndrome (ACS). MATERIALS AND METHODS: Two hundred eighty-four ESRD patients with ACS were enrolled between March 2002 and February 2008. These patients were followed until death or June 2009. Medical records were reviewed retrospectively. The cut-off value of cTnT for AMI was evaluated using a receiver operating characteristic (ROC) curve. We calculated Kaplan-Meier survival curves, and potential outcome predictors were determined by Cox proportional hazard analysis. RESULTS: AMIs were diagnosed in 40 patients (14.1%). The area under the curve was 0.98 in the ROC curve (p<0.001; 95% CI, 0.95-1.00). The summation of sensitivity and specificity was highest at the initial cTnT value of 0.35 ng/mL (sensitivity, 0.95; specificity, 0.97). Survival analysis showed a statistically significant difference in all-cause and cardiovascular mortalities for the group with an initial cTnT > or =0.35 ng/mL compared to the other groups. Initial serum cTnT concentration was an independent predictor for mortality. CONCLUSION: Because ESRD patients with an initial cTnT concentration > or =0.35 ng/mL have a poor prognosis, it is suggested that urgent diagnosis and treatment be indicated in dialysis patients with ACS when the initial cTnT levels are > or =0.35 ng/mL.
Acute Coronary Syndrome/blood/complications/*diagnosis/mortality ; Aged ; Biological Markers/blood ; Female ; Humans ; Kidney Failure, Chronic/blood/complications/*diagnosis/mortality ; Male ; Middle Aged ; Prognosis ; Retrospective Studies ; Sensitivity and Specificity ; Troponin T/*blood