Purpose and background Perfusion imaging in the angiography suite may provide a way to reduce time from stroke onset to endovascular revascularization of patients with a large vessel occlusion. Our purpose was to compare CBCTP with MDCTP. Materials and Methods Data from seven subjects with both MDCTP and CBCTP were retrospectively processed and analyzed. Two algorithms were used to enhance temporal resolution, temporal sampling density and reduce noise of CBCT data before generating perfusion maps. Two readers performed qualitative image quality evaluation on maps using a 5-point scale. ROIs indicating CBF/CBV abnormalities were drawn. Quantitative analyses were performed using the Sørensen–Dice coefficients to quantify the similarity of abnormalities. A non-inferiority hypothesis was tested to compare CBCTP against CBCTP. Results Averaged image quality score for MDCTP and CBCTP images was 2.4 and 2.3 respectively. Averaged confidence scores in diagnosis were both 1.4 for MDCT and CBCT; averaged confidence scores on presence of a CBV/CBF mismatch was 1.7 (κ = 0.50) and 1.5 (κ = 0.64). For MDCTP and CBCTP maps the average score of confidence in making treatment decision was 1.4 (κ = 0.79) and 1.3 (κ = 0.90). Area under visual grading characteristic (AVGC) for the above four qualitative quality score showed an average AVGC of 0.50 with 95% confidence level cover centered at the mean for both readers. Sørensen–Dice coefficient for CBF maps is 0.81 and for CBV maps is 0.55. Conclusions After post-processing methods were applied to enhance image quality for CBCTP maps, the CBCTP maps were not inferior to those generated from MDCTP.

Deep learning angiography accurately recreated the vascular anatomy of the 3D rotational angiography reconstructions without a mask. Deep learning angiography reduced misregistration artifacts induced by intersweep motion, and it reduced radiation exposure required to obtain clinically useful 3D rotational angiography.

Purpose: A major technical obstacle to bringing x-ray multicontrast (i.e., attenuation, phase, and dark-field) imaging methodology to clinical use is the prolonged data acquisition time caused by the phase stepping procedure. The purpose of this work was to introduce a fast acquisition with seamless stage translation (FASST) technique to a prototype multicontrast breast imaging system for reduced image acquisition time that is clinically acceptable. Methods: The prototype system was constructed based on a Hologic full-field digital mammography + digital breast tomosynthesis combination system. During each FASST acquisition process, a motorized stage holding a diffraction grating travels continuously with a constant velocity, and a train of 15 short x-ray pulses (35 ms each) was delivered by using the Zero-Degree Tomo mode of the Hologic system. Standard phase retrieval was applied to the 15 subimages without spatial interpolation to avoid spatial resolution loss. The method was evaluated using a physical phantom, a bovine udder specimen, and a freshly resected mastectomy specimen. The FASST technique was experimentally compared with single-shot acquisition methods and the standard phase stepping method. Results: The image acquisition time of the proposed method is 3.7 s. In comparison, conventional phase stepping took 105 s using the same prototype imaging system. The mean glandular dose of both methods was matched at 1.3 mGy. No artifacts or spatial resolution loss was observed in images produced by FASST. In contrast, the single-shot methods led to spatial resolution loss and residual moir e artifacts. Conclusions: The FASST technique reduces the data acquisition time of the prototype multicontrast x-ray breast imaging system to 3.7 s, such that it is comparable to a clinical digital breast tomosynthesis exam.

No abstract

Purpose: The development and clinical employment of a computed tomography (CT) imaging system benefit from a thorough understanding of the statistical properties of the output images; cerebral CT perfusion (CTP) imaging system is no exception. A series of articles will present statistical properties of CTP systems and the dependence of these properties on system parameters. This Part I paper focuses on the signal and noise properties of cerebral blood volume (CBV) maps calculated using a nondeconvolution-based method. Methods: The CBV imaging chain was decomposed into a cascade of subimaging stages, which facilitated the derivation of analytical models for the probability density function, mean value, and noise variance of CBV. These models directly take CTP source image acquisition, reconstruction, and postprocessing parameters as inputs. Both numerical simulations and in vivo canine experiments were performed to validate these models. Results: The noise variance of CBV is linearly related to the noise variance of source images and is strongly influenced by the noise variance of the baseline images. Uniformly partitioning the total radiation dose budget across all time frames was found to be suboptimal, and an optimal dose partition method was derived to minimize CBV noise. Results of the numerical simulation and animal studies validated the derived statistical properties of CBV. Conclusions: The statistical properties of CBV imaging systems can be accurately modeled by extending the linear CT systems theory. Based on the statistical model, several key signal and noise characteristics of CBV were identified and an optimal dose partition method was developed to improve the image quality of CBV.