Biomedical subjects

Nicholas A Ablitt

Publications and source records attributed to Nicholas A Ablitt.

4 recordsLinked to original sources

PubMed2005

Interstudy reproducibility of quantitative perfusion cardiovascular magnetic resonance.

PURPOSE: To determine the interstudy reproducibility of quantitative first-pass perfusion cardiovascular magnetic resonance with comparison of 2 previously described analysis techniques. There is no published data on the interstudy reproducibility of perfusion cardiovascular magnetic resonance which can be used to determine the significance of longitudinal changes in myocardial perfusion after pharmacologic or therapeutic interventions with defined sample sizes. METHODS: Sixteen subjects (7 normal volunteers, 9 patients with coronary artery disease) had rest and adenosine stress perfusion cardiovascular magnetic resonance studies on two separate visits. A short axis slice was studied on each visit using a fast low-angle shot sequence. The global and regional myocardial perfusion reserve indices were calculated using 2 methods: model based constrained deconvolution with the Fermi function, and normalized upslopes. Reproducibility was defined as the standard deviation of the measurement differences, divided by the mean (coefficient of variation). RESULTS: The reproducibility of global myocardial perfusion reserve indices was 21% in normal volunteers, which was similar to that in patients with coronary artery disease (CAD) (23%, p = .88). The reproducibility of regional myocardial perfusion reserve indices was 28% (p = .45 vs. global analysis). The reproducibility of global MPRi was superior with Fermi deconvolution compared with normalized upslopes (21% vs. 41%, p = .02). CONCLUSION: At this stage of clinical development, the reproducibility of quantitative perfusion cardiovascular magnetic resonance is good, and superior using Fermi deconvolution in preference to upslope analysis.

Adenosine↗

PubMed2004

Accurate assessment of the arterial input function during high-dose myocardial perfusion cardiovascular magnetic resonance.

PURPOSE: To develop a method for accurate measurement of the arterial input function (AIF) during high-dose, single-injection, quantitative T1-weighted myocardial perfusion cardiovascular magnetic resonance (CMR). MATERIALS AND METHODS: Fast injection of high-dose gadolinium with highly T1 sensitive myocardial perfusion imaging is normally incompatible with quantitative perfusion modeling because of distortion of the peak of the AIF caused by full recovery of the blood magnetization. We describe a new method that for each cardiac cycle uses a low-resolution short-axis (SA) image with a short saturation-recovery time immediately after the R-wave in order to measure the left ventricular (LV) blood pool signal, which is followed by a single SA high-resolution image with a long saturation-recovery time in order to measure the myocardial signal with high sensitivity. Fifteen subjects were studied. Using the new method, we compared the myocardial perfusion reserve (MPR) with that obtained from the dual-bolus technique (a low-dose bolus to measure the blood pool signal and a high-dose bolus to measure the myocardial signal). RESULTS: A small significant difference was found between MPRs calculated using the new method and the MPRs calculated using the dual-bolus method. CONCLUSION: This new method for measuring the AIF introduced no major error, while removing the practical difficulties of the dual-bolus approach. This suggests that quantification of the MPR can be achieved using the simple high-dose single-bolus technique, which could also image multiple myocardial slices.

Contrast Media↗

PubMed2004

Respiratory reordered UNFOLD perfusion imaging.

PURPOSE: To propose a respiratory reordered UNFOLD (RR-UNFOLD) imaging sequence to significantly reduce the amount of k-space data required for first-pass MR myocardial perfusion imaging. MATERIALS AND METHODS: Rapid acquisition of high-resolution imaging data is essential to detailed quantitative analysis of first-pass myocardial perfusion. Existing MR sequences have explored the full capacity of the imaging hardware to reduce the acquisition window within each cardiac cycle while maintaining the desired spatial resolution. Further improvement in perfusion imaging will require a more efficient use of the information content of the k-space data. The method uses prospective diaphragmatic navigator echoes to ensure that temporal filtering of UNFOLD is carried out on a series of images that are spatially registered. An adaptive real-time rebinning algorithm is developed for the creation of static image subseries related to different levels of respiratory motion. Issues concerning the temporal smoothing of tracer kinetic signals are discussed, and a solution based on oversampling of the central k-space is provided. The method is assessed in 10 normal subjects without the administration of contrast agent, and further validated by administration of Gd-DTPA in 10 patients at rest. RESULTS: The results of this study show that RR-UNFOLD significantly extends the applicability of UNFOLD to perfusion imaging, which yields a 40% reduction in image artifact when the same amount of k-space information is used. CONCLUSION: The scan efficiency achieved can be used in combination with MR hardware improvements for extending the three-dimensional spatial coverage and shortening the data acquisition window to provide detailed information on regional myocardial perfusion abnormalities.

Adult↗

PubMed2004

Predictive cardiac motion modeling and correction with partial least squares regression.

Respiratory-induced cardiac deformation is a major problem for high-resolution cardiac imaging. This paper presents a new technique for predictive cardiac motion modeling and correction, which uses partial least squares regression to extract intrinsic relationships between three-dimensional (3-D) cardiac deformation due to respiration and multiple one-dimensional real-time measurable surface intensity traces at chest or abdomen. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory-induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modeling can be used for real-time prospective motion tracking or correction. Detailed numerical issues related to the technique are discussed and the effectiveness of the motion and deformation modeling is validated with 3-D magnetic resonance data sets acquired from ten asymptomatic subjects covering the entire respiratory range.

Algorithms↗