Targeted vessel reconstruction in non‐contrast‐enhanced steady‐state free precession angiography

Abstract

Image quality in non-contrast-enhanced (NCE) angiograms is often limited by scan time constraints. An effective solution is to undersample angiographic acquisitions and to recover vessel images with penalized reconstructions. However, conventional methods leverage penalty terms with uniform spatial weighting, which typically yield insufficient suppression of aliasing interference and suboptimal blood/background contrast. Here we propose a two-stage strategy where a tractographic segmentation is employed to auto-extract vasculature maps from undersampled data. These maps are then used to incur spatially adaptive sparsity penalties on vascular and background regions. In vivo steady-state free precession angiograms were acquired in the hand, lower leg and foot. Compared with regular non-adaptive compressed sensing (CS) reconstructions (CSlow ), the proposed strategy improves blood/background contrast by 71.3 ± 28.9% in the hand (mean ± s.d. across acceleration factors 1-8), 30.6 ± 11.3% in the lower leg and 28.1 ± 7.0% in the foot (signed-rank test, P < 0.05 at each acceleration). The proposed targeted reconstruction can relax trade-offs between image contrast, resolution and scan efficiency without compromising vessel depiction.

Accelerated, first‐pass cardiac perfusion pulse sequence with radial k‐space sampling, compressed sensing, and k‐space weighted image contrast reconstruction tailored for visual analysis and quantification of myocardial blood flow

“…where F is the undersampled nonuniform fast Fourier transform (NUFFT) operator, S is the coil sensitivities in x‐y space, x represents the estimated dynamic images being reconstructed, y is the undersampled multicoil k‐space data, α is a matrix of regularization weights defined with different weights for the foreground and the background, T is the finite difference operator, which is applied along the time dimension, and m is an m‐by‐m square mask used to distinguish the foreground from the background. For the mask, the foreground was defined as signal intensity greater than 10% of the maximum intensity of the time‐averaged image . To standardize the regularization weights across different rest perfusion data sets, the normalized regularization weight for the foreground was optimized as 10% of the maximum intensity of a low‐resolution proton density image .…”

“…For the mask, the foreground was defined as signal intensity greater than 10% of the maximum intensity of the time-averaged image. 32 To standardize the regularization weights across different rest perfusion data sets, the normalized regularization weight for the foreground was optimized as 10% of the maximum intensity of a low-resolution proton density image. 33 For the background, the normalized regularization weight was set as the maximum intensity of a low-resolution proton density image.…”

Magnetic resonance angiography with compressed sensing: An evaluation of moyamoya disease

“…only 10 seconds were needed to cover the whole brain[ 5 ]). Non-contrast time-of-flight (TOF) MRA is also a good candidate for CS based acceleration, as images feature a bright trees of sparse vessels over a well suppressed anatomical background signal[ 6 – 10 ]. In a preceding study on normal volunteers[ 11 ], the scan time of cerebrovascular non-contrast TOF-MRA with CS (CS-TOF) was reduced to approximately half of that of TOF-MRA with parallel imaging (PI-TOF), the current clinical standard for accelerating acquisition[ 12 – 14 ], while maintaining the image quality.…”

Elliptical signal model based simultaneous parameter estimation in bSSFP imaging

“…Dengeli kararlı-durum serbest devinim (bSSFP), Manyetik Rezonans Görüntülemede (MRG) kullanılan bir görüntüleme teknigidir [1]. Sagladıgı sinyal gürültü oranı (SGO) ve hızlı görüntüleme süresiyle klinik uygulamalarda özellikle anatomik beyin görüntüleme, kardiyak görüntüleme ve anjiyografide geniş kullanım alanı bulunmaktadır [2]- [5]. Fakat bSSFP'deki manyetik alan eşitsizliklerine olan hassasiyet, görüntülerde bükülme adı verilen yapayolguları oluşturur [6].…”