br Conclusions In this study we illustrated that it

Conclusions
In this study, we illustrated that it is feasible to speed up acquisition in an ABVS-like system by using plane-wave imaging, retaining an image quality similar to that of conventional, focused scanning. Eleven steering angles were determined as the optimal compromise between speed and image contrast and resolution, having the potential to shorten ABVS acquisition to less than 10 s. Stolt\’s f–k migration algorithm with additional apodization had the best overall performance of the algorithms tested. The image quality was found to be independent of the movement speed of the transducer in the range relevant for ABVS scanning, thus enabling breath-hold examinations.

Acknowledgments
This work is supported by the Dutch Technology Foundation STW (Project 13290), which is part of the Netherlands Organization for Scientific Research (NWO) and is partly funded by the Ministry of Economic Affairs. The authors thank Jan Menssen for useful discussions and provision of great technical support.

Introduction
The stiffness of tissue has been associated with underlying pathology of various medical conditions. Increase in tissue stiffness is associated with age and pathogenic processes and is due mainly to an increase in connective tissue (Liao and Schaefer 2007; Nenadic et al. 2013a, 2013b). For example, increase in NS-398 stiffness results in decreased bladder capacity, which may cause lower urinary tract symptoms and incontinence (Nenadic et al. 2013a, 2013b). Therefore, characterization of tissue stiffness for body organs is of clinical interest.
Quantitative ultrasound shear wave elastography has emerged as a non-invasive technique for assessment of tissue stiffness based on radiation force excitation and tracking of the induced shear waves (Bercoff et al. 2004; Nightingale et al. 2003; Pengfei et al. 2012). Application of such techniques has been reported to be highly reproducible and accurate in assessment of malignancies in different organs (Bonnefous and Pesqué 1986; Denis et al. 2015a, 2015b; Gregory et al. 2015; Mehrmohammadi et al. 2015; Palmeri et al. 2008; Tanter et al. 2008; Urban et al. 2012). Although tracking of transient shear waves in large organs, such as breast and liver, is possible with minor effects from surrounding tissue heterogeneity, structured media such as arteries, heart and bladder pose an additional difficulty, as tracking algorithms should capture wave dynamics along confined anatomic trajectories (Maksuti et al. 2016). These methods should be able first to detect such geometries from the acquired B-mode images and then to track the induced transient transverse waves along the estimated trajectory.
An example of this situation is ultrasound bladder vibrometry (UBV) (Mehrmohammadi et al. 2013; Nenadic et al. 2013a, 2013b). The bladder operates through complex coordination of musculoskeletal, neurologic and psychological functions that allow filling and emptying of bladder content. The relaxation and contraction of detrusor muscles, smooth muscles of the bladder wall, is the primary effector of continence. UBV has been introduced as a non-invasive technique for assessment of bladder wall mechanical properties versus bladder volume. This method employs focused ultrasound to tap the bladder wall and excite transient transverse waves on the wall. As these waves are at frequencies around tens to hundreds of cycles per seconds, an imaging speed of a few thousand frames per second is essential to fully capture the dynamics of these waves. Hence imaging techniques such as plane wave imaging are methods of choice that provide a sufficient imaging frame rate. However, the quality of plane wave imaging is significantly inferior to that of conventional line-by-line scanning (Couture et al. 2012). Hence both wall detection and tracking of transient waves become challenging tasks.
So far, mainly time-of-flight (TOF) methods, such as the time-to-peak (TTP) method (Palmeri et al. 2008) and cross-correlation (Xcorr) technique (McLaughlin and Renzi 2006; Tanter NS-398 et al. 2008), have been used to quantify transient transverse wave speed (TWS) from displacement maps obtained with ultrasound pulse-echo techniques (Hasegawa and Kanai 2006; Nenadic et al. 2013a, 2013b). In TOF techniques, the transient wave arrival time is determined at multiple spatial locations for a window by assuming a fixed direction of propagation. The TWS is then calculated by fitting a regression line to data points. However, it is challenging to apply these methods to in vivo data because of tissue motion, low displacement signal-to-noise ratio (SNR), spatial tissue inhomogeneities and artifacts. These might cause failure in detection of accurate TWS because of the presence of gross outliers.