Browsing by Author "Schmidt, Michaela"

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    Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography
    (NLM (Medline), 2023) Wood, Gregory; Uglebjerg Pedersen, Alexandra; Kunze, Karl P; Neji, Radhouene; Hajhosseiny, Reza; Wetzl, Jens; Yoon, Seung Su; Schmidt, Michaela; Norgaard, Bjarne Linde; Prieto Vásquez, Claudia; Botnar, René Michael; Kim, Won Yong
    BACKGROUND: Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection. METHODS: Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel. RESULTS: There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5-98.1 s). CONCLUSIONS: Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.
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    Evaluation of accelerated motion-compensated 3d water/fat late gadolinium enhanced MR for atrial wall imaging
    (SPRINGER, 2021) Munoz, Camila; Sim, Iain; Neji, Radhouene; Kunze, Karl P.; Masci, Pier Giorgio; Schmidt, Michaela; O'Neill, Mark; Williams, Steven; Botnar, Rene M.; Prieto, Claudia
    Objective 3D late gadolinium enhancement (LGE) imaging is a promising non-invasive technique for the assessment of atrial fibrosis. However, current techniques result in prolonged and unpredictable scan times and high rates of non-diagnostic images. The purpose of this study was to compare the performance of a recently proposed accelerated respiratory motion-compensated 3D water/fat LGE technique with conventional 3D LGE for atrial wall imaging. Materials and methods 18 patients (age: 55.7 +/- 17.1 years) with atrial fibrillation underwent conventional diaphragmatic navigator gated inversion recovery (IR)-prepared 3D LGE (dNAV) and proposed image-navigator motion-corrected water/fat IR-prepared 3D LGE (iNAV) imaging. Images were assessed for image quality and presence of fibrosis by three expert observers. The scan time for both techniques was recorded. Results Image quality scores were improved with the proposed compared to the conventional method (iNAV: 3.1 +/- 1.0 vs. dNAV: 2.6 +/- 1.0, p = 0.0012, with 1: Non-diagnostic to 4: Full diagnostic). Furthermore, scan time for the proposed method was significantly shorter with a 59% reduction is scan time (4.5 +/- 1.2 min vs. 10.9 +/- 3.9 min, p < 0.0001). The images acquired with the proposed method were deemed as inconclusive less frequently than the conventional images (expert 1/expert 2: 4/7 dNAV and 2/4 iNAV images inconclusive). Discussion The motion-compensated water/fat LGE method enables atrial wall imaging with diagnostic quality comparable to the current conventional approach with a significantly shorter scan of about 5 min.
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    Non-rigid motion-corrected free-breathing 3D myocardial Dixon LGE imaging in a clinical setting
    (SPRINGER, 2022) Zeilinger, Martin Georg; Kunze, Karl Philipp; Munoz, Camila; Neji, Radhouene; Schmidt, Michaela; Croisille, Pierre; Heiss, Rafael; Wuest, Wolfgang; Uder, Michael; Botnar, Rene Michael; Treutlein, Christoph; Prieto, Claudia
    Objectives To investigate the efficacy of an in-line non-rigid motion-compensated reconstruction (NRC) in an image-navigated high-resolution three-dimensional late gadolinium enhancement (LGE) sequence with Dixon water-fat separation, in a clinical setting. Methods Forty-seven consecutive patients were enrolled prospectively and examined with 1.5 T MRI. NRC reconstructions were compared to translational motion-compensated reconstructions (TC) of the same datasets in overall and different sub-category image quality scores, diagnostic confidence, contrast ratios, LGE pattern, and semiautomatic LGE quantification. Results NRC outperformed TC in all image quality scores (p < 0.001 to 0.016; e.g., overall image quality 5/5 points vs. 4/5). Overall image quality was downgraded in only 23% of NRC datasets vs. 53% of TC datasets due to residual respiratory motion. In both reconstructions, LGE was rated as ischemic in 11 patients and non-ischemic in 10 patients, while it was absent in 26 patients. NRC delivered significantly higher LGE-to-myocardium and blood-to-myocardium contrast ratios (median 6.33 vs. 5.96, p < 0.001 and 4.88 vs. 4.66, p < 0.001, respectively). Automatically detected LGE mass was significantly lower in the NRC reconstruction (p < 0.001). Diagnostic confidence was identical in all cases, with high confidence in 89% and probable in 11% datasets for both reconstructions. No case was rated as inconclusive. Conclusions The in-line implementation of a non-rigid motion-compensated reconstruction framework improved image quality in image-navigated free-breathing, isotropic high-resolution 3D LGE imaging with undersampled spiral-like Cartesian sampling and Dixon water-fat separation compared to translational motion correction of the same datasets. The sharper depictions of LGE may lead to more accurate measures of LGE mass.