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.

Background: Uncoupling protein 1 (UCP1) is a proton uncoupler located across the mitochondrial membrane gener‑ ally involved in thermogenesis of brown adipose tissues. Although UCP1 is known to be strongly expressed in brown adipocytes, recent evidence suggest that white adipocytes can also express UCP1 under certain circumstances such as cold- or β-adrenergic receptor-stimulation, allowing them to acquire brown adipocyte-like features thereby becoming ’beige’ adipocytes. Results: In this study, we report that UCP1 can be expressed in adipose-tissue macrophages (ATM) lacking func‑ tional hypoxia-inducible factor-1 (HIF-1) and this does not require cold- nor β-adrenergic receptor activation. By using myeloid-specific Hif-1α knockout (KO) mice, we observed that these mice were protected from diet-induced obesity and exhibited an improved thermogenic tolerance upon cold challenge. ATM isolated from white adipose tissues (WAT) of these mice fed with high fat diet exhibited significantly higher M2-polarization, decreased gly‑ colysis, increased mitochondrial functions and acetyl-CoA levels, along with increased expression of Ucp1, peroxisome proliferator activated receptor-gamma co-activator-1a, and others involved in histone acetylation. Consistent with the increased Ucp1 gene expression, these ATM produced a significant amount of heat mediating lipolysis of cocultured adipocytes liberating free fatty acid. Treating ATM with acetate, a substrate for acetyl-CoA synthesis was able to boost the heat production in wild-type or Hif-1α-deficient but not UCP1-deficient macrophages, indicating that UCP1 was necessary for the heat production in macrophages. Lastly, we observed a significant inverse correlation between the number of UCP1-expressing ATM in WAT and the body mass index of human individuals. Conclusions UCP1-expressing ATM produce the heat to mediate lipolysis of adipocytes, indicating that this can be a novel strategy to treat and prevent diet-induced obesity.

Background: Uncoupling protein 1 (UCP1) is a proton uncoupler located across the mitochondrial membrane gener‑ ally involved in thermogenesis of brown adipose tissues. Although UCP1 is known to be strongly expressed in brown adipocytes, recent evidence suggest that white adipocytes can also express UCP1 under certain circumstances such as cold- or β-adrenergic receptor-stimulation, allowing them to acquire brown adipocyte-like features thereby becoming ’beige’ adipocytes. Results: In this study, we report that UCP1 can be expressed in adipose-tissue macrophages (ATM) lacking func‑ tional hypoxia-inducible factor-1 (HIF-1) and this does not require cold- nor β-adrenergic receptor activation. By using myeloid-specific Hif-1α knockout (KO) mice, we observed that these mice were protected from diet-induced obesity and exhibited an improved thermogenic tolerance upon cold challenge. ATM isolated from white adipose tissues (WAT) of these mice fed with high fat diet exhibited significantly higher M2-polarization, decreased gly‑ colysis, increased mitochondrial functions and acetyl-CoA levels, along with increased expression of Ucp1, peroxisome proliferator activated receptor-gamma co-activator-1a, and others involved in histone acetylation. Consistent with the increased Ucp1 gene expression, these ATM produced a significant amount of heat mediating lipolysis of cocultured adipocytes liberating free fatty acid. Treating ATM with acetate, a substrate for acetyl-CoA synthesis was able to boost the heat production in wild-type or Hif-1α-deficient but not UCP1-deficient macrophages, indicating that UCP1 was necessary for the heat production in macrophages. Lastly, we observed a significant inverse correlation between the number of UCP1-expressing ATM in WAT and the body mass index of human individuals. Conclusions UCP1-expressing ATM produce the heat to mediate lipolysis of adipocytes, indicating that this can be a novel strategy to treat and prevent diet-induced obesity.

Background: Uncoupling protein 1 (UCP1) is a proton uncoupler located across the mitochondrial membrane gener‑ ally involved in thermogenesis of brown adipose tissues. Although UCP1 is known to be strongly expressed in brown adipocytes, recent evidence suggest that white adipocytes can also express UCP1 under certain circumstances such as cold- or β-adrenergic receptor-stimulation, allowing them to acquire brown adipocyte-like features thereby becoming ’beige’ adipocytes. Results: In this study, we report that UCP1 can be expressed in adipose-tissue macrophages (ATM) lacking func‑ tional hypoxia-inducible factor-1 (HIF-1) and this does not require cold- nor β-adrenergic receptor activation. By using myeloid-specific Hif-1α knockout (KO) mice, we observed that these mice were protected from diet-induced obesity and exhibited an improved thermogenic tolerance upon cold challenge. ATM isolated from white adipose tissues (WAT) of these mice fed with high fat diet exhibited significantly higher M2-polarization, decreased gly‑ colysis, increased mitochondrial functions and acetyl-CoA levels, along with increased expression of Ucp1, peroxisome proliferator activated receptor-gamma co-activator-1a, and others involved in histone acetylation. Consistent with the increased Ucp1 gene expression, these ATM produced a significant amount of heat mediating lipolysis of cocultured adipocytes liberating free fatty acid. Treating ATM with acetate, a substrate for acetyl-CoA synthesis was able to boost the heat production in wild-type or Hif-1α-deficient but not UCP1-deficient macrophages, indicating that UCP1 was necessary for the heat production in macrophages. Lastly, we observed a significant inverse correlation between the number of UCP1-expressing ATM in WAT and the body mass index of human individuals. Conclusions UCP1-expressing ATM produce the heat to mediate lipolysis of adipocytes, indicating that this can be a novel strategy to treat and prevent diet-induced obesity.

Background: Uncoupling protein 1 (UCP1) is a proton uncoupler located across the mitochondrial membrane gener‑ ally involved in thermogenesis of brown adipose tissues. Although UCP1 is known to be strongly expressed in brown adipocytes, recent evidence suggest that white adipocytes can also express UCP1 under certain circumstances such as cold- or β-adrenergic receptor-stimulation, allowing them to acquire brown adipocyte-like features thereby becoming ’beige’ adipocytes. Results: In this study, we report that UCP1 can be expressed in adipose-tissue macrophages (ATM) lacking func‑ tional hypoxia-inducible factor-1 (HIF-1) and this does not require cold- nor β-adrenergic receptor activation. By using myeloid-specific Hif-1α knockout (KO) mice, we observed that these mice were protected from diet-induced obesity and exhibited an improved thermogenic tolerance upon cold challenge. ATM isolated from white adipose tissues (WAT) of these mice fed with high fat diet exhibited significantly higher M2-polarization, decreased gly‑ colysis, increased mitochondrial functions and acetyl-CoA levels, along with increased expression of Ucp1, peroxisome proliferator activated receptor-gamma co-activator-1a, and others involved in histone acetylation. Consistent with the increased Ucp1 gene expression, these ATM produced a significant amount of heat mediating lipolysis of cocultured adipocytes liberating free fatty acid. Treating ATM with acetate, a substrate for acetyl-CoA synthesis was able to boost the heat production in wild-type or Hif-1α-deficient but not UCP1-deficient macrophages, indicating that UCP1 was necessary for the heat production in macrophages. Lastly, we observed a significant inverse correlation between the number of UCP1-expressing ATM in WAT and the body mass index of human individuals. Conclusions UCP1-expressing ATM produce the heat to mediate lipolysis of adipocytes, indicating that this can be a novel strategy to treat and prevent diet-induced obesity.

Background: Uncoupling protein 1 (UCP1) is a proton uncoupler located across the mitochondrial membrane gener‑ ally involved in thermogenesis of brown adipose tissues. Although UCP1 is known to be strongly expressed in brown adipocytes, recent evidence suggest that white adipocytes can also express UCP1 under certain circumstances such as cold- or β-adrenergic receptor-stimulation, allowing them to acquire brown adipocyte-like features thereby becoming ’beige’ adipocytes. Results: In this study, we report that UCP1 can be expressed in adipose-tissue macrophages (ATM) lacking func‑ tional hypoxia-inducible factor-1 (HIF-1) and this does not require cold- nor β-adrenergic receptor activation. By using myeloid-specific Hif-1α knockout (KO) mice, we observed that these mice were protected from diet-induced obesity and exhibited an improved thermogenic tolerance upon cold challenge. ATM isolated from white adipose tissues (WAT) of these mice fed with high fat diet exhibited significantly higher M2-polarization, decreased gly‑ colysis, increased mitochondrial functions and acetyl-CoA levels, along with increased expression of Ucp1, peroxisome proliferator activated receptor-gamma co-activator-1a, and others involved in histone acetylation. Consistent with the increased Ucp1 gene expression, these ATM produced a significant amount of heat mediating lipolysis of cocultured adipocytes liberating free fatty acid. Treating ATM with acetate, a substrate for acetyl-CoA synthesis was able to boost the heat production in wild-type or Hif-1α-deficient but not UCP1-deficient macrophages, indicating that UCP1 was necessary for the heat production in macrophages. Lastly, we observed a significant inverse correlation between the number of UCP1-expressing ATM in WAT and the body mass index of human individuals. Conclusions UCP1-expressing ATM produce the heat to mediate lipolysis of adipocytes, indicating that this can be a novel strategy to treat and prevent diet-induced obesity.