Feature analysis methods for intelligent breast imaging parameter optimisation using CMOS active pixel sensors

This thesis explores the concept of real time imaging parameter optimisation in digital mammography using statistical information extracted from the breast during a scan. Transmission and Energy dispersive x-ray diffraction (EDXRD) imaging were the two very different imaging modalities investigated. An attempt to determine if either could be used in a real time imaging system enabling differentiation between healthy and suspicious tissue regions was made. This would consequently enable local regions (potentially cancerous regions) within the breast to be imaged using optimised imaging parameters. The performance of possible statistical feature functions that could be used as information extraction tools were investigated using low exposure breast tissue images. The images were divided into eight regions of interest, seven regions corresponding to suspicious tissue regions marked by a radiologist, where the final region was obtained from a location in the breast consisting solely of healthy tissue. Results obtained from this investigation showed that a minimum of 82% of the suspicious tissue regions were highlighted in all images, whilst the total exposure incident on the sample was reduced in all instances. Three out of the seven (42%) intelligent images resulted in an increased contrast to noise ratio (CNR) compared to the conventionally produced transmission images. Three intelligent images were of similar diagnostic quality to their conventional counter parts whilst one was considerably lower. EDXRD measurements were made on breast tissue samples containing potentially cancerous tissue regions. As the technique is known to be able to distinguish between breast tissue types, diffraction signals were used to produce images corresponding to three suspicious tissue regions consequently enabling pixel intensities within the images to be analysed. A minimum of approximately 70% of the suspicious tissue regions were highlighted in each image, with at least 50% of each image remaining unsuspicious, hence was imaged with a reduced incident exposure

Feature analysis methods for intelligent breast imaging parameter optimisation using CMOS active pixel sensors.

This thesis explores the concept of real time imaging parameter optimisation in digital mammography using statistical information extracted from the breast during a scan. Transmission and Energy dispersive x-ray diffraction (EDXRD) imaging were the two very different imaging modalities investigated. An attempt to determine if either could be used in a real time imaging system enabling differentiation between healthy and suspicious tissue regions was made. This would consequently enable local regions (potentially cancerous regions) within the breast to be imaged using optimised imaging parameters. The performance of possible statistical feature functions that could be used as information extraction tools were investigated using low exposure breast tissue images. The images were divided into eight regions of interest, seven regions corresponding to suspicious tissue regions marked by a radiologist, where the final region was obtained from a location in the breast consisting solely of healthy tissue. Results obtained from this investigation showed that a minimum of 82% of the suspicious tissue regions were highlighted in all images, whilst the total exposure incident on the sample was reduced in all instances. Three out of the seven (42%) intelligent images resulted in an increased contrast to noise ratio (CNR) compared to the conventionally produced transmission images. Three intelligent images were of similar diagnostic quality to their conventional counter parts whilst one was considerably lower. EDXRD measurements were made on breast tissue samples containing potentially cancerous tissue regions. As the technique is known to be able to distinguish between breast tissue types, diffraction signals were used to produce images corresponding to three suspicious tissue regions consequently enabling pixel intensities within the images to be analysed. A minimum of approximately 70% of the suspicious tissue regions were highlighted in each image, with at least 50% of each image remaining unsuspicious, hence was imaged with a reduced incident exposure.

I-ImaS: intelligent imaging sensors

Conventional x-radiography uniformly irradiates the relevant region of the patient. Across that region, however, there is likely to be significant variation in both the thickness and pathological composition of the tissues present, which means that the x-ray exposure conditions selected, and consequently the image quality achieved, are a compromise. The I-ImaS concept eliminates this compromise by intelligently scanning the patient to identify the important diagnostic features, which are then used to adaptively control the x-ray exposure conditions at each point in the patient. In this way optimal image quality is achieved throughout the region of interest whilst maintaining or reducing the dose. An I-ImaS system has been built under an EU Framework 6 project and has undergone preclinical testing. The system is based upon two rows of sensors controlled via an FPGA based DAQ board. Each row consists of a 160mm ×1 mm linear array of ten scintillator coated 3T CMOS APS devices with 32 μm pixels and a readable array of 520×40 pixels. The first sensor row scans the patient using a fraction of the total radiation dose to produce a preview image, which is then interrogated to identify the optimal exposure conditions at each point in the image. A signal is then sent to control a beam filter mechanism to appropriately moderate x-ray beam intensity at the patient as the second row of sensors follows behind. Tests performed on breast tissue sections found that the contrast-to-noise ratio in over 70% of the images was increased by an average of 15% at an average dose reduction of 9%. The same technology is currently also being applied to baggage scanning for airport security. © 2010 IOP Publishing Ltd and SISSA

Characterisation of the Components of a PrototypeScanning Intelligent Imaging System for Use inDigital Mammography: The I-ImaS System

The physical performance characteristics of a prototype scanning digital mammography (DM) system have been investigated. The I-ImaS system utilises CMOS MAPS technology promoting on-chip data processing; consequently statistical analysis is therefore achievable in real-time for the purpose of exposure modulation via a feedback mechanism during the image acquisition procedure. The imager employs a dual array of twenty CMOS APS sensing devices each individually coupled to a 100 mum thick thallium doped structured CsI scintillator. Results indicate the implementation of real-time intelligence into the image acquisition phase of digital mammography is foreseeable

Design and characterization of the I-ImaS multi-element X-ray detector system

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce new, intelligent x-ray imaging systems using novel APS sensors to create optimal diagnostic images. Initial systems have been constructed for medical imaging; specifically mammography and dental encephalography. However, the I-ImaS system concept could be applied to all areas of x-ray imaging, including homeland security and industrial QA. The I-ImaS system intelligence is implemented by the use of APS technology and FPGAs, allowing real-time analysis of data during image acquisition. This gives the system the capability to perform as an on-the-fly adaptive imaging system, with the potential to create images with maximum diagnostic information within given dose constraints. The I-ImaS system uses a scanning linear array of scintillatorcoupled 1.5-D CMOS Active Pixel Sensors to create a full 2-D x-ray image of an object. This paper describes the parameters considered when choosing the scintillator elements of the detectors. A study of the positioning of the sensors to form a linear detector is also considered, along with a discussion of the potential losses in image quality associated with creating a linear sensor by tiling many smaller sensors. Preliminary results show that the detectors have sufficient performance to be used successfully in the initial mammographie and encephalographic I-ImaS systems that are currently under construction. © 2008 IEEE

A scanning system for intelligent imaging: I-ImaS.

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce adaptive x-ray imaging systems using Monolithic Active Pixel Sensors (MAPS) to create optimal diagnostic images. Initial systems concentrate on mammography and cephalography. The on-chip intelligence available to MAPS technology will allow real-time analysis of data during image acquisition, giving the capability to build a truly adaptive imaging system with the potential to create images with maximum diagnostic information within given dose constraints. In our system, the exposure in each image region is optimized and the beam intensity is a function not only of tissue thickness and attenuation, but also of local physical and statistical parameters found in the image itself. Using a linear array of detectors with on-chip intelligence, the system will perform an on-line analysis of the image during the scan and then will optimize the X-ray intensity in order to obtain the maximum diagnostic information from the region of interest while minimizing exposure of less important, or simply less dense, regions. This paper summarizes the testing of the sensors and their electronics carried out using synchrotron radiation, x-ray sources and optical measurements. The sensors are tiled to form a 1.5D linear array. These have been characterised and appropriate correction techniques formulated to take into account misalignments between individual sensors. Full testing of the mammography and cephalography I-ImaS prototype is now underway and the system intelligence is constantly being upgraded through iterative testing in order to obtain the optimal algorithms and settings. In preliminary simulations the dose savings between the regulated images and the reference images were estimated to between 30 to 70%