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philips achieva 1.5t  (Philips Healthcare)

 
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    Structured Review

    Philips Healthcare philips achieva 1.5t
    Philips Achieva 1.5t, supplied by Philips Healthcare, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/1%2E5t+philips+achieva/3+scanner/pmc12989892-110-18-18
    Average 86 stars, based on 1 article reviews
    philips achieva 1.5t - by Bioz Stars, 2026-09
    86/100 stars

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    Related Articles

    other:

    Article Title: Diagnostic significance of mesenteric lymph node involvement in proximal small bowel Crohn’s disease
    Article Snippet: In the second institution, MRE was performed on a 1.5T Philips Achieva (Philips, Amsterdm, Netherlands).

    Article Title: Harmonization of Multi-Center Diffusion Tensor Tractography in Neonates with Congenital Heart Disease: Optimizing Post-Processing and Application of ComBat
    Article Snippet: Infants imaged at TCH were scanned on a 1.5T Philips Achieva (8 channel head coil, FOV=200mm, voxel dimensions=2×2×2.7mm, TE/TR=90/6065ms, 15 directions at B=860s/mm 2 ).

    Article Title: Brain Markers of Resilience to Psychosis in High-Risk Individuals: A Systematic Review and Label-Based Meta-Analysis of Multimodal MRI Studies.
    Article Snippet: Sariçiçek et al., 2015 [97] 25 FHR (FDR, 32 yr, 46% M) 28 BD (36 yr, 26% M) 29 HC (34 yr, 28% M) 1.5T Philips Achieva: T1 images (FFE sequence, TR 25 ms, TE 6 ms, FA 8◦, FOV 24 cm, axial slices, and 1 mm thickness) VBM analyses in SPM8 using DARTEL.

    Article Title: Unsupervised domain adaptive model for 3D prostate zonal segmentation
    Article Snippet: NCI-ISBI13 consists of 40 prostate MRI scans obtained from Radboud University Nijmegen Medical Centre with the imaging device of 1.5T Philips Achieva.

    Article Title: Brain Markers of Resilience to Psychosis in High-Risk Individuals: A Systematic Review and Label-Based Meta-Analysis of Multimodal MRI Studies
    Article Snippet: Sariçiçek et al., 2015 [ ] , 25 FHR (FDR, 32 yr, 46% M) 28 BD (36 yr, 26% M) 29 HC (34 yr, 28% M) , 1.5T Philips Achieva: T1 images (FFE sequence, TR 25 ms, TE 6 ms, FA 8°, FOV 24 cm, axial slices, and 1 mm thickness) , VBM analyses in SPM8 using DARTEL. , ANCOVA for VBM group comparisons, with ICV and education as covariates; thresholds: cluster p < 0.05, voxel p < 0.01, and cluster size > 205. , Relative to HC, both FHR and BD had reduced volume of the cerebellum (incl. vermis) and increased volume of the bilateral IFG, but only FHR showed increased gray matter volume of the left SMG and parahippocampal gyrus ..

    Article Title: Brain Markers of Resilience to Psychosis in High-Risk Individuals: A Systematic Review and Label-Based Meta-Analysis of Multimodal MRI Studies
    Article Snippet: Sepede et al., 2015 [ ] , 22 FHR (FDR, 32 yr, 32% M) 23 BD (35 yr, 39% M) 24 HC (33 yr, 33% M) , IAPS-based emotional task (identifying vegetable items inside neutral or negative pictures) , 1.5T Philips Achieva: T2 * (EPI sequence, TR 3 s, TE 50 ms, FA 90°, voxel size 4 mm 3 , 30 transaxial slices, and no gap); T1 scan (3D sequence, TR 25 ms, TE 4.7 ms, FA 30°, and voxels 1 mm 3 ) , BrainVoyager QX 2.2 for processing; motion correction as part of pre-processing, with no details provided. , Group effects tested with random effect GLM, controlling for performance and mood symptoms and using voxel p < 0.001 and cluster > 4 to account for multiple comparisons. , BD patients showed reduced accuracy in target detection, while FHR performed similar to HC. Compared with both HC and BD, FHR showed hyperactivation of right LG and reduced activation of right SFG and pre-SMA ; may reflect resilience markers..

    Article Title: Brain Markers of Resilience to Psychosis in High-Risk Individuals: A Systematic Review and Label-Based Meta-Analysis of Multimodal MRI Studies.
    Article Snippet: Sepede et al., 2015 [114] 22 FHR (FDR, 32 yr, 32% M) 23 BD (35 yr, 39% M) 24 HC (33 yr, 33% M) IAPS-based emotional task (identifying vegetable items inside neutral or negative pictures) 1.5T Philips Achieva: T2 * (EPI sequence, TR 3 s, TE 50 ms, FA 90◦ , voxel size 4 mm3, 30 transaxial slices, and no gap); T1 scan (3D sequence, TR 25 ms, TE 4.7 ms, FA 30◦ , and voxels 1 mm3) BrainVoyager QX 2.2 for processing; motion correction as part of pre-processing, with no details provided.

    Comparison:

    Article Title: Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
    Article Snippet: .. For out-of-domain performance, we tested the proposed method and other methods in comparison on a completely independent dataset, the Multi-center Multi-vendor (M&M) cardiac MRI (Campello et al., 2021), which contains 320 SSFP cine scans of end-systolic (ES) and end-diastolic (ED) images collected from 6 medical centers using different 1.5T -(Siemens Avanto, Germany, Philips Achieva, Netherlands; GE Signa Excite; and Cannon Vantage Orian); and 3T scanners (Siemens Skyra, Germany). ..

    Magnetic Resonance Imaging:

    Article Title: Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
    Article Snippet: .. For out-of-domain performance, we tested the proposed method and other methods in comparison on a completely independent dataset, the Multi-center Multi-vendor (M&M) cardiac MRI (Campello et al., 2021), which contains 320 SSFP cine scans of end-systolic (ES) and end-diastolic (ED) images collected from 6 medical centers using different 1.5T -(Siemens Avanto, Germany, Philips Achieva, Netherlands; GE Signa Excite; and Cannon Vantage Orian); and 3T scanners (Siemens Skyra, Germany). ..



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    Block diagram for personalized-training and using the neural network for estimating T 1 values with <t>MOLLI.</t> Personalized-training (solid lines) was performed by simulating the <t>MOLLI</t> <t>pulse</t> sequence with its parameters and the physiological ECG triggers so as to generate a customized database from the MRI simulator for the specific MRI acquisition. Once the network was trained with volunteer-specific simulated MR data then it could be used (dashed lines) for generating T 1 maps from MOLLI images acquired by the scanner
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    Image Search Results


    Block diagram for personalized-training and using the neural network for estimating T 1 values with MOLLI. Personalized-training (solid lines) was performed by simulating the MOLLI pulse sequence with its parameters and the physiological ECG triggers so as to generate a customized database from the MRI simulator for the specific MRI acquisition. Once the network was trained with volunteer-specific simulated MR data then it could be used (dashed lines) for generating T 1 maps from MOLLI images acquired by the scanner

    Journal: BMC Medical Imaging

    Article Title: Neural networks with personalized training for improved MOLLI T 1 mapping

    doi: 10.1186/s12880-025-01769-z

    Figure Lengend Snippet: Block diagram for personalized-training and using the neural network for estimating T 1 values with MOLLI. Personalized-training (solid lines) was performed by simulating the MOLLI pulse sequence with its parameters and the physiological ECG triggers so as to generate a customized database from the MRI simulator for the specific MRI acquisition. Once the network was trained with volunteer-specific simulated MR data then it could be used (dashed lines) for generating T 1 maps from MOLLI images acquired by the scanner

    Article Snippet: MOLLI pulse sequences at a 1.5T Philips Achieva scanner (Philips Healthcare, Best, Netherlands) with two different acquisition schemes were used: 5(3)3, the clinical MOLLI pulse sequence for native myocardial T 1 mapping with an additional three heartbeat pause between inversions.

    Techniques: Blocking Assay, Sequencing