Line-Scanning Particle Image Velocimetry: An Optical Approach for Quantifying a Wide Range of Blood Flow Speeds in Live Animals

The ability to measure blood velocities is critical for studying vascular development, physiology, and pathology. A key challenge is to quantify a wide range of blood velocities in vessels deep within living specimens with concurrent diffraction-limited resolution imaging of vascular cells. Two-photon laser scanning microscopy (TPLSM) has shown tremendous promise in analyzing blood velocities hundreds of micrometers deep in animals with cellular resolution. However, current analysis of TPLSM-based data is limited to the lower range of blood velocities and is not adequate to study faster velocities in many normal or disease conditions.We developed line-scanning particle image velocimetry (LS-PIV), which used TPLSM data to quantify peak blood velocities up to 84 mm/s in live mice harboring brain arteriovenous malformation, a disease characterized by high flow. With this method, we were able to accurately detect the elevated blood velocities and exaggerated pulsatility along the abnormal vascular network in these animals. LS-PIV robustly analyzed noisy data from vessels as deep as 850 µm below the brain surface. In addition to analyzing in vivo data, we validated the accuracy of LS-PIV up to 800 mm/s using simulations with known velocity and noise parameters.To our knowledge, these blood velocity measurements are the fastest recorded with TPLSM. Partnered with transgenic mice carrying cell-specific fluorescent reporters, LS-PIV will also enable the direct in vivo correlation of cellular, biochemical, and hemodynamic parameters in high flow vascular development and diseases such as atherogenesis, arteriogenesis, and vascular anomalies

Biogenic gas nanostructures as ultrasonic molecular reporters

Ultrasound is among the most widely used non-invasive imaging modalities in biomedicine, but plays a surprisingly small role in molecular imaging due to a lack of suitable molecular reporters on the nanoscale. Here, we introduce a new class of reporters for ultrasound based on genetically encoded gas nanostructures from microorganisms, including bacteria and archaea. Gas vesicles are gas-filled protein-shelled compartments with typical widths of 45–250 nm and lengths of 100–600 nm that exclude water and are permeable to gas. We show that gas vesicles produce stable ultrasound contrast that is readily detected in vitro and in vivo, that their genetically encoded physical properties enable multiple modes of imaging, and that contrast enhancement through aggregation permits their use as molecular biosensors

Data from: A convex formulation for magnetic particle imaging x-space reconstruction

Magnetic Particle Imaging (MPI) is an emerging imaging modality with exceptional promise for clinical applications in rapid angiography, cell therapy tracking, cancer imaging, and inflammation imaging. Recent publications have demonstrated quantitative mpi across rat sized fields of view with x-space reconstruction methods. Critical to any medical imaging technology is the reliability and accuracy of image reconstruction. Because the average value of the mpi signal is lost during direct-feedthrough signal filtering, mpi reconstruction algorithms must recover this zero-frequency value. Prior x-space mpi recovery techniques were limited to 1d approaches which could introduce artifacts when reconstructing a 3d image. In this paper, we formulate x-space reconstruction as a 3d convex optimization problem and apply robust a priori knowledge of image smoothness and non-negativity to reduce non-physical banding and haze artifacts. We conclude with a discussion of the powerful extensibility of the presented formulation for future applications

A Convex Formulation for Magnetic Particle Imaging X-Space Reconstruction.

Magnetic Particle Imaging (mpi) is an emerging imaging modality with exceptional promise for clinical applications in rapid angiography, cell therapy tracking, cancer imaging, and inflammation imaging. Recent publications have demonstrated quantitative mpi across rat sized fields of view with x-space reconstruction methods. Critical to any medical imaging technology is the reliability and accuracy of image reconstruction. Because the average value of the mpi signal is lost during direct-feedthrough signal filtering, mpi reconstruction algorithms must recover this zero-frequency value. Prior x-space mpi recovery techniques were limited to 1d approaches which could introduce artifacts when reconstructing a 3d image. In this paper, we formulate x-space reconstruction as a 3d convex optimization problem and apply robust a priori knowledge of image smoothness and non-negativity to reduce non-physical banding and haze artifacts. We conclude with a discussion of the powerful extensibility of the presented formulation for future applications

MPI region of interest (ROI) analysis and quantification of iron in different volumes

MPI directly detects superparamagnetic iron oxides (SPIONs), which should enable precise, accurate, and linear quantification. However, selecting a region of interest (ROI) has strong effects on MPI quantification results. Ideally, ROI selection should be simple, user-independent, and widely applicable. In this work, we describe and compare four MPI ROI selection methods and assess their performance in vitro and in vivo. To explore the effect of ROI selection, ten ferucarbotran phantoms were imaged, each contained the same amount of iron but varied in volume. Three users tested the accuracy of the ROI methods for quantification of these samples. Lastly, the four ROI methods were applied to quantify ferucarbotran in vivo after intravenous, intramuscular, and subcutaneous injections in mice. We discuss the strengths and limitations of each ROI method, such as the ability to capture MPI signals of custom shapes (i.e. size of the ROI), degree of user variability, speed of analysis, and quantification accuracy of SPIONs in different volumes.   Int. J. Mag. Part. Imag. 8(1), 2022, Article ID: 2208002, DOI: 10.18416/IJMPI.2022.220800