In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as singleenergy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. The end point of the deep learning approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. We retrospectively studied 22 patients who received contrast-enhanced abdomen DECT scan. The difference between the predicted and original high-energy CT images are 3.47 HU, 2.95 HU, 2.38 HU, and 2.40 HU for spine, aorta, liver and stomach, respectively. The difference between virtual non-contrast (VNC) images obtained from original DECT and deep learning DECT are 4.10 HU, 3.75 HU, 2.33 HU and 2.92 HU for spine, aorta, liver and stomach, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9%. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.
X-ray luminescence computed tomography (XLCT) and X-ray fluorescence computed tomography (XFCT) are two emerging technologies in X-ray imaging. In these modalities, images are formed through detection of secondary emissions (light in XLCT, or secondary X-rays in XFCT) following X-ray excitations. XLCT and XFCT enable us to leverage the widely used X-ray imaging for simultaneous <i>in vivo</i> molecular and functional imaging. Depending on the geometry of the excitation X-ray beam (pencil-, fan-, and cone-beam or coded apertures), optimal tradeoff between imaging efficiency and spatial resolution can be achieved. The novel imaging principles of XLCT/XFCT make it possible to achieve a spatial resolution comparable to that of anatomical X-ray imaging. Here, we summarize our studies in this area in the past decade and discuss their prospects.
We present a photoacoustic imaging system based on a low-cost high-power miniature light emitting diode (LED), which has the capability of in vivo mapping vasculature networks in biological tissue. Phantoms were used to demonstrate the feasibility of the system, while in vivo imaging the vasculature of mouse ear shows that LED-based photoacoustic imaging (LED-PAI) could have great potential for label-free biomedical imaging applications, overcoming the practical limitations of the use of bulky and expensive pulsed lasers.
Near infrared spectroscopy (NIRS) is an emerging functional brain imaging tool capable of assessing cerebral concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) during brain activation noninvasively. As an extension of NIRS, diffuse optical tomography (DOT) not only shares the merits of providing continuous readings of cerebral oxygenation, but also has the ability to provide spatial resolution in the millimeter scale. Based on the scattering and absorption properties of nonionizing near-infrared light in biological tissue, DOT has been successfully applied in the imaging of breast tumors, osteoarthritis and cortex activations. Here, we present a state-of-art fast high density DOT system suitable for brain imaging. It can achieve up to a 21 Hz sampling rate for a full set of two-wavelength data for 3-D DOT brain image reconstruction. The system was validated using tissue-mimicking brain-model phantom. Then, experiments on healthy subjects were conducted to demonstrate the capability of the system.
Optical coherence tomography (OCT) can obtain light scattering properties with a high resolution, while photoacoustic
imaging (PAI) is ideal for mapping optical absorbers in biological tissues, and ultrasound (US) could penetrate deeply
into tissues and provide elastically structural information. It is attractive and challenging to integrate these three imaging
modalities into a miniature probe, through which, both optical absorption and scattering information of tissues as well as
deep-tissue structure can be obtained. Here, we present a novel side-view probe integrating PAI, OCT and US imaging
based on double-clad fiber which is used as a common optical path for PAI (light delivery) and OCT (light
delivery/detection), and a 40 MHz unfocused ultrasound transducer for PAI (photoacoustic detection) and US
(ultrasound transmission/receiving) with an overall diameter of 1.0 mm. Experiments were conducted to demonstrate the
capabilities of the integrated multimodal imaging probe, which is suitable for endoscopic imaging and intravascular