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Densitometric Radiographic Imaging With Contour Sensors

We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient’s volume to derive densitometric images. Traditionally, these… Click to show full abstract

We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient’s volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient’s volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph ( $\mathcal {X}$ ). In order to derive this map of lengths ( $\mathcal {L}$ ), the camera equations of the X-ray system and the contour sensor are determined. The patient’s surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived $\mathcal {L}$ is applied to $\mathcal {X}$ as a physical operation (subtraction), obtaining soft tissue- ( $\mathcal {D}_{S}$ ) or bone-enhanced ( $\mathcal {D}'_{B}$ ) figures. In the $\mathcal {D}_{S}$ type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding $\mathcal {X}$ ). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed $\mathcal {X}$ instances. In the $\mathcal {D}'_{B}$ type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination.

Keywords: tex math; math notation; inline formula; formula tex; notation latex

Journal Title: IEEE Access
Year Published: 2019

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