Table 1.
Overview of design and characterisation of CCD/CMOS based X-ray detector.
Table 2.
Point grey camera (CMOS sensor) physical parameters.
Fig 1.
Optically coupled X-ray detector.
3D view of OCX detector designed with an inexpensive monochrome/RGB CMOS sensor. The CsI(Tl) scintillator screen converts the incident X-rays to light photons. The light photons were reflected by the mirror and collected by the lens coupled with the CMOS sensor. The auto-focus module adjusts the aperture and focus of the lens through the LabVIEW user interface.
Fig 2.
Cascaded linear model of the OCX detector.
The cascaded linear model had different stages, from the X-ray incident on the screen until the CMOS sensor received it. We characterized each stage by an absorption/gain factor or MTF characterizing the resulting blur due to the stage.
Fig 3.
X-ray spectrum of the micro-focus tube(50 kVp) used in the experiment.
Fig 4.
The metal ball phantom is used to perform accurate geometric calibration. The phantom had a series of metal balls of diameter 50 μm taped on the surface of the solid PMMA cylinder (diameter 25 mm).
Fig 5.
Iodine test phantom to estimate the CNR of the system.
The capillary tube of diameter 1.2 mm was filled with different dilutions of ICA (Omnipaque 350 mg-I/ml)-350, 175, 87.5, 43.8, 21.87 mg-I/ml. One end of the capillary was sealed and fixed in the PMMA based material.
Fig 6.
Micro-CT imaging system integrated with the OCX detector. Here, SOD and SDD gives the distance between source to object and the distance between source to the detector.
Fig 7.
Approximate calibration method.
(a) The copper ring phantom was kept parallel and orthogonal to the rotary axis in the micro-CT scanner. The detector’s center point(U0, V0) was estimated approximately from the radiographic images. Here, SOD refers to the distance between source to object, and SDD refers to the distance between source to the detector, (b) 2D projection image of the customized copper ring phantom scanned at the energy 35 kVp, 750 μA, 2 fps is shown. The particular projection image shown was used to estimate V0.
Fig 8.
Micro-CT contrast phantom with HA rods.
Cylindrical micro-CT contrast phantom had different concentrations of HA rods such as 0 mg/cc, 50 mg/cc, 100 mg/cc, 250 mg/cc, and 500 mg/cc. The micro-CT rods of diameter 2 mm and height 20 mm were placed circularly in the 40 mm height, 25 mm diameter solid acrylic customized phantom.
Fig 9.
X-ray photons to light conversion.
(a) Blue curve represents the number of X-ray photons exiting the Be window(courtesy: Source-ray inc., USA), the brown curve in the same graph is the X-ray transmission response of the CsI(Tl) screen (courtesy: Hamamatsu), (b) shows the attenuation of the light photons through various stages of the detector(stage 2 to 4). Here, the blue graph indicates the light photons created by the CsI(Tl) screen, the black curve indicates the reflection efficiency of the mirror for the input light photons(slightly overlapped with the blue curve), the green graph is the photon response of the relay lens(f1.4, f0.95), and the red curve indicates the light collection response of the CMOS sensor(Monochrome and RGB).
Fig 10.
The point spread function of the camera was observed using a 50 μm diameter metal ball. Here, the RGB camera’s full width at half maximum(FWHM) was estimated as 53 μm, and the monochrome camera was estimated as 44 μm.
Table 3.
Metal ball phantom image acquisition parameters.
Fig 11.
MTF plot for source and X-ray detectors.
(a) MTF of the source blur plotted using the Eq 5. Here, source blur1 and source blur2 represent the geometrical unsharpness of the monochrome and RGB detector. Also, MTF of the monochrome and RGB based OCX detector is plotted in the same graph, (b) MTF curve of CsI(Tl) screen estimated using the screen specification as described by Ganguly A et al. [18].
Fig 12.
NPS and DQE spectrum of the detector.
(a) Noise power spectrum obtained from the gain image, (b) DQE of the scintillator screen and the imaging system. Here, the DQE of the scintillator is plotted from the contrast data shared by the manufacturer(Fig 11), and the DQE of the imaging detector is estimated from the experimental data using metal ball PSF.
Fig 13.
Spatial resolution of the OCX detector.
(a) Projection image of the QRM bar pattern acquired at the maximum magnification of 8.3 and 35 kVp, 750 μA, 5 fps as system setting, (b) MTF plot for the bar pattern. The contrast value in the MTF graph was obtained from the intensity profiles of line patterns at each spatial frequency in the projection image.
Fig 14.
CNR plot for the fast frame rate studies.
CNR is plotted as a function of frame rate for (a) monochrome and (b) RGB camera. The CNR study is performed with an iodinated contrast agent (ICA) at different dilutions and 50 kVp tube voltage. CNR value observed from the in-vivo studies is shown as dotted lines in the graphs.
Fig 15.
CNR graph of tungsten wire estimated from images acquired using the monochrome sensor.
CNR graph of tungsten wire phantom imaged using the monochrome sensor at different magnification and frame rates. X-ray tube settings: 50 kVp, 900 μA.
Fig 16.
Experiment-I angiographic studies using monochrome camera.
System setting: 50 kVp, 1 mA, 41 fps (a) arrival of ICA to the heart through IVC, (b) ICA gets in the right atrium, (c) the coronary arteries covered by the heart were visible, and (d) the pulmonary artery (PA) pumped out the ICA to lungs.
Fig 17.
Experiment-II & III cardiac studies (mice-2 & 3) using RGB camera.
System setting: 50 kVp, 1 mA, 40 fps. Experiment with mice 2 is shown in (a) and (b), iodine highlighting the IVC, RA, CA, PA, aorta, and lungs. A similar experiment with mice 3, is shown in (c) and (d), where the coronary arteries and lungs are highlighted.
Fig 18.
Comparison against the commercial scanner.
Cylindrical contrast phantom with different concentrations of HA micro-CT rods scanned using our micro-CT and GE scanner. (a) reconstructed image from the GE scanner, (b) image reconstructed from the micro-CT scanner using monochrome CMOS sensor, and (c) image reconstructed from the scanner using RGB CMOS sensor. The phantoms were reconstructed using an in-house FDK reconstruction algorithm in python.