Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.

Automated Assessment of Image Quality and Dose Attributes in Clinical CT Images

Computed tomography (CT) is a valuable technology to the healthcare enterprise as evidenced by the more than 70 million CT exams performed every year. As a result, CT has become the largest contributor to population doses amongst all medical imaging modalities that utilize man-made ionizing radiation. Acknowledging the fact that ionizing radiation poses a health risk, there exists the need to strike a balance between diagnostic benefit and radiation dose. Thus, to ensure that CT scanners are optimally used in the clinic, an understanding and characterization of image quality and radiation dose are essential.

The state-of-the-art in both image quality characterization and radiation dose estimation in CT are dependent on phantom based measurements reflective of systems and protocols. For image quality characterization, measurements are performed on inserts imbedded in static phantoms and the results are ascribed to clinical CT images. However, the key objective for image quality assessment should be its quantification in clinical images; that is the only characterization of image quality that clinically matters as it is most directly related to the actual quality of clinical images. Moreover, for dose estimation, phantom based dose metrics, such as CT dose index (CTDI) and size specific dose estimates (SSDE), are measured by the scanner and referenced as an indicator for radiation exposure. However, CTDI and SSDE are surrogates for dose, rather than dose per-se.

Currently there are several software packages that track the CTDI and SSDE associated with individual CT examinations. This is primarily the result of two causes. The first is due to bureaucracies and governments pressuring clinics and hospitals to monitor the radiation exposure to individuals in our society. The second is due to the personal concerns of patients who are curious about the health risks associated with the ionizing radiation exposure they receive as a result of their diagnostic procedures.

An idea that resonates with clinical imaging physicists is that patients come to the clinic to acquire quality images so they can receive a proper diagnosis, not to be exposed to ionizing radiation. Thus, while it is important to monitor the dose to patients undergoing CT examinations, it is equally, if not more important to monitor the image quality of the clinical images generated by the CT scanners throughout the hospital.

The purposes of the work presented in this thesis are threefold: (1) to develop and validate a fully automated technique to measure spatial resolution in clinical CT images, (2) to develop and validate a fully automated technique to measure image contrast in clinical CT images, and (3) to develop a fully automated technique to estimate radiation dose (not surrogates for dose) from a variety of clinical CT protocols.

Photoacoustic tomography: principles and advances.

Photoacoustic tomography (PAT) is an emerging imaging modality that shows great potential for preclinical research and clinical practice. As a hybrid technique, PAT is based on the acoustic detection of optical absorption from either endogenous chromophores, such as oxy-hemoglobin and deoxy-hemoglobin, or exogenous contrast agents, such as organic dyes and nanoparticles. Because ultrasound scatters much less than light in tissue, PAT generates high-resolution images in both the optical ballistic and diffusive regimes. Over the past decade, the photoacoustic technique has been evolving rapidly, leading to a variety of exciting discoveries and applications. This review covers the basic principles of PAT and its different implementations. Strengths of PAT are highlighted, along with the most recent imaging results.

Probabilistic Evaluation of Three-Dimensional Reconstructions from X-Ray Images Spanning a Limited Angle

An important part of computed tomography is the calculation of a three-dimensional reconstruction of an object from series of X-ray images. Unfortunately, some applications do not provide sufficient X-ray images. Then, the reconstructed objects no longer truly represent the original. Inside of the volumes, the accuracy seems to vary unpredictably. In this paper, we introduce a novel method to evaluate any reconstruction, voxel by voxel. The evaluation is based on a sophisticated probabilistic handling of the measured X-rays, as well as the inclusion of a priori knowledge about the materials that the object receiving the X-ray examination consists of. For each voxel, the proposed method outputs a numerical value that represents the probability of existence of a predefined material at the position of the voxel while doing X-ray. Such a probabilistic quality measure was lacking so far. In our experiment, false reconstructed areas get detected by their low probability. In exact reconstructed areas, a high probability predominates. Receiver Operating Characteristics not only confirm the reliability of our quality measure but also demonstrate that existing methods are less suitable for evaluating a reconstruction.

Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur

Introduction: Bone mineral density (BMD) is currently the preferred surrogate for bone strength in clinical practice. Finite element analysis (FEA) is a computer simulation technique that can predict the deformation of a structure when a load is applied, providing a measure of stiffness (Nmm−1). Finite element analysis of X-ray images (3D-FEXI) is a FEA technique whose analysis is derived froma single 2D radiographic image. Methods: 18 excised human femora had previously been quantitative computed tomography scanned, from which 2D BMD-equivalent radiographic images were derived, and mechanically tested to failure in a stance-loading configuration. A 3D proximal femur shape was generated from each 2D radiographic image and used to construct 3D-FEA models. Results: The coefficient of determination (R2%) to predict failure load was 54.5% for BMD and 80.4% for 3D-FEXI. Conclusions: This ex vivo study demonstrates that 3D-FEXI derived from a conventional 2D radiographic image has the potential to significantly increase the accuracy of failure load assessment of the proximal femur compared with that currently achieved with BMD. This approach may be readily extended to routine clinical BMD images derived by dual energy X-ray absorptiometry. Crown Copyright © 2009 Published by Elsevier Ltd on behalf of IPEM. All rights reserved

Three-dimensional reconstruction of an ancient Egyptian mummy

A complete series of cross-sectional computed tomography (CT) scans were obtained of a mummy of an Egyptian priestess, Tjenmutengebtiu, (Jeni), who lived in the twenty-second Dynasty (c. 945-715 BC). The purpose of this joint British Museum and St. Thomas’ Hospital project was effectively to ‘unwrap’ a mummy using cross-sectional X-rays. Jeni is encased in a beautifully decorated anthropomorphic cartonnage coffin. The head and neck were scanned with 2mm slices, the teeth with 1mm slices and the rest of the body with 4 mm slices, a 512 x 512 matrix was used. The 2D CT images, and 3D surface reconstruction’s, demonstrate many features of the embalming techniques and funerary customs of the XXII Dynasty. The presence of cloth protruding from the nasal cavities into the otherwise empty cranial cavity indicates that the brain was extracted via the nose. The remains of the heart can be seen as well as four organ packs corresponding to the mummified and repackaged lungs, intestines, stomach and liver. Each of the organ packs encloses a wax figurine representing one of the four sons of Horus. The teeth are in very good condition with little signs of wear, which, considering the gritty diet of the Egyptians, indicates that Jeni must have been very young when she died. A young age of death is also suggested by analysis of the shape of the molar teeth. The body is generally in very good condition demonstrating the consummate skill of the twenty-second Dynasty embalmers.

CT imaging of an Egyptian mummy

Over the last few years various research groups around the world have employed X-ray Computed Tomography (CT) imaging in the study of mummies – Toronto-Boston (1,2), Manchester(3). Prior to the development of CT scanners, plane X-rays were used in the investigation of mummies. Xeroradiography has also been employed(4). In a xeroradiograph, objects of similar X-ray density (very difficult to see on a conventional X-ray) appear edge-enhanced and so are seen much more clearly. CT scanners became available in the early 1970s. A CT scanner produces cross-sectional X-rays of objects. On a conventional X-radiograph individual structures are often very difficult to see because all the structures lying in the path of the X-ray beam are superimposed, a problem that does not occur with CT. Another advantage of CT is that the information in a series of consecutive images may be combined to produce a three-dimensional reconstruction of an object. Slices of different thickness and magnification may be chosen. Why CT a mummy? Prior to the availability of CT scanners, the only way of finding out about the inside of a mummy in any detail was to unwrap and dissect it. This has been done by various research groups – most notably the Manchester, UK and Pennsylvania University, USA mummy projects(5,6). Unwrapping a mummy and carrying out an autopsy is obviously very destructive. CT studies hold the possibility of producing a lot more information than is possible from plain X-rays and are able to show the undisturbed arrangement of the wrapped body. CT is also able to provide information about the internal structure of bones, organ packs, etc that wouldn’t be possible without sawing through the bones etc. The mummy we have scanned is encased in a coffin which would have to have been broken open in order to remove the body.

Unwrapping an ancient Egyptian mummy using x-rays

This article describes a project to unwrap an ancient Egyptian mummy using X-ray computed tomography (CT). About 600 X-ray CT images were obtained through the mummified body of a female named Tjetmutjengebtiu (or Jeni for short), who was a singer in the great temple of Karnak in Egypt during the 22nd dynasty (c. 945-715 BC). The X-ray CT images reveal details of the remains of body organs, wrappings and jewellery. 3D reconstructions of Jeni’s teeth suggest that she was probably only around 20 years old when she died, although the cause of death cannot be ascertained from the CT scans. The CT images were used to build a 3D model of Jeni’s head which enabled an artist to paint a picture of what Jeni may have looked like during life. A PowerPoint presentation and movie clips are provided as supplementary material that may be useful for teaching.

Improved image contrast of the bone-muscle interface with 3T MRI compared to 1.5T MRI [Abstract]

Virtual 3D models of long bones are increasingly being used for implant design and research applications. The current gold standard for the acquisition of such data is Computed Tomography (CT) scanning. Due to radiation exposure, CT is generally limited to the imaging of clinical cases and cadaver specimens. Magnetic Resonance Imaging (MRI) does not involve ionising radiation and therefore can be used to image selected healthy human volunteers for research purposes. The feasibility of MRI as alternative to CT for the acquisition of morphological bone data of the lower extremity has been demonstrated in recent studies [1, 2]. Some of the current limitations of MRI are long scanning times and difficulties with image segmentation in certain anatomical regions due to poor contrast between bone and surrounding muscle tissues. Higher field strength scanners promise to offer faster imaging times or better image quality. In this study image quality at 1.5T is quantitatively compared to images acquired at 3T. --------- The femora of five human volunteers were scanned using 1.5T and 3T MRI scanners from the same manufacturer (Siemens) with similar imaging protocols. A 3D flash sequence was used with TE = 4.66 ms, flip angle = 15° and voxel size = 0.5 × 0.5 × 1 mm. PA-Matrix and body matrix coils were used to cover the lower limb and pelvis respectively. Signal to noise ratio (SNR) [3] and contrast to noise ratio (CNR) [3] of the axial images from the proximal, shaft and distal regions were used to assess the quality of images from the 1.5T and 3T scanners. The SNR was calculated for the muscle and bone-marrow in the axial images. The CNR was calculated for the muscle to cortex and cortex to bone marrow interfaces, respectively. --------- Preliminary results (one volunteer) show that the SNR of muscle for the shaft and distal regions was higher in 3T images (11.65 and 17.60) than 1.5T images (8.12 and 8.11). For the proximal region the SNR of muscles was higher in 1.5T images (7.52) than 3T images (6.78). The SNR of bone marrow was slightly higher in 1.5T images for both proximal and shaft regions, while it was lower in the distal region compared to 3T images. The CNR between muscle and bone of all three regions was higher in 3T images (4.14, 6.55 and 12.99) than in 1.5T images (2.49, 3.25 and 9.89). The CNR between bone-marrow and bone was slightly higher in 1.5T images (4.87, 12.89 and 10.07) compared to 3T images (3.74, 10.83 and 10.15). These results show that the 3T images generated higher contrast between bone and the muscle tissue than the 1.5T images. It is expected that this improvement of image contrast will significantly reduce the time required for the mainly manual segmentation of the MR images. Future work will focus on optimizing the 3T imaging protocol for reducing chemical shift and susceptibility artifacts.

Image acquisition and manipulation protocols for CT-PET fusion to improve the accuracy of gross tumour volume localisation for 3D conformal radiotherapy for lung cancer

Aims: To develop clinical protocols for acquiring PET images, performing CT-PET registration and tumour volume definition based on the PET image data, for radiotherapy for lung cancer patients and then to test these protocols with respect to levels of accuracy and reproducibility. Method: A phantom-based quality assurance study of the processes associated with using registered CT and PET scans for tumour volume definition was conducted to: (1) investigate image acquisition and manipulation techniques for registering and contouring CT and PET images in a radiotherapy treatment planning system, and (2) determine technology-based errors in the registration and contouring processes. The outcomes of the phantom image based quality assurance study were used to determine clinical protocols. Protocols were developed for (1) acquiring patient PET image data for incorporation into the 3DCRT process, particularly for ensuring that the patient is positioned in their treatment position; (2) CT-PET image registration techniques and (3) GTV definition using the PET image data. The developed clinical protocols were tested using retrospective clinical trials to assess levels of inter-user variability which may be attributed to the use of these protocols. A Siemens Somatom Open Sensation 20 slice CT scanner and a Philips Allegro stand-alone PET scanner were used to acquire the images for this research. The Philips Pinnacle3 treatment planning system was used to perform the image registration and contouring of the CT and PET images. Results: Both the attenuation-corrected and transmission images obtained from standard whole-body PET staging clinical scanning protocols were acquired and imported into the treatment planning system for the phantom-based quality assurance study. Protocols for manipulating the PET images in the treatment planning system, particularly for quantifying uptake in volumes of interest and window levels for accurate geometric visualisation were determined. The automatic registration algorithms were found to have sub-voxel levels of accuracy, with transmission scan-based CT-PET registration more accurate than emission scan-based registration of the phantom images. Respiration induced image artifacts were not found to influence registration accuracy while inadequate pre-registration over-lap of the CT and PET images was found to result in large registration errors. A threshold value based on a percentage of the maximum uptake within a volume of interest was found to accurately contour the different features of the phantom despite the lower spatial resolution of the PET images. Appropriate selection of the threshold value is dependant on target-to-background ratios and the presence of respiratory motion. The results from the phantom-based study were used to design, implement and test clinical CT-PET fusion protocols. The patient PET image acquisition protocols enabled patients to be successfully identified and positioned in their radiotherapy treatment position during the acquisition of their whole-body PET staging scan. While automatic registration techniques were found to reduce inter-user variation compared to manual techniques, there was no significant difference in the registration outcomes for transmission or emission scan-based registration of the patient images, using the protocol. Tumour volumes contoured on registered patient CT-PET images using the tested threshold values and viewing windows determined from the phantom study, demonstrated less inter-user variation for the primary tumour volume contours than those contoured using only the patient’s planning CT scans. Conclusions: The developed clinical protocols allow a patient’s whole-body PET staging scan to be incorporated, manipulated and quantified in the treatment planning process to improve the accuracy of gross tumour volume localisation in 3D conformal radiotherapy for lung cancer. Image registration protocols which factor in potential software-based errors combined with adequate user training are recommended to increase the accuracy and reproducibility of registration outcomes. A semi-automated adaptive threshold contouring technique incorporating a PET windowing protocol, accurately defines the geometric edge of a tumour volume using PET image data from a stand alone PET scanner, including 4D target volumes.

Improved image quality for x-ray CT imaging of gel dosimeters

Purpose: This study provides a simple method for improving precision of x-ray computed tomography (CT) scans of irradiated polymer gel dosimetry. The noise affecting CT scans of irradiated gels has been an impediment to the use of clinical CT scanners for gel dosimetry studies. Method: In this study, it is shown that multiple scans of a single PAGAT gel dosimeter can be used to extrapolate a ‘zero-scan’ image which displays a similar level of precision to an image obtained by averaging multiple CT images, without the compromised dose measurement resulting from the exposure of the gel to radiation from the CT scanner. Results: When extrapolating the zero-scan image, it is shown that exponential and simple linear fits to the relationship between Hounsfield unit and scan number, for each pixel in the image, provides an accurate indication of gel density. Conclusions: It is expected that this work will be utilised in the analysis of three-dimensional gel volumes irradiated using complex radiotherapy treatments.

Speckle reduction in optical coherence tomography imaging by affine-motion image registration

Signal-degrading speckle is one factor that can reduce the quality of optical coherence tomography images. We demonstrate the use of a hierarchical model-based motion estimation processing scheme based on an affine-motion model to reduce speckle in optical coherence tomography imaging, by image registration and the averaging of multiple B-scans. The proposed technique is evaluated against other methods available in the literature. The results from a set of retinal images show the benefit of the proposed technique, which provides an improvement in signal-to-noise ratio of the square root of the number of averaged images, leading to clearer visual information in the averaged image. The benefits of the proposed technique are also explored in the case of ocular anterior segment imaging.

A subject-specific model of human buttocks and thighs in a seated posture

Finite element analyses of the human body in seated postures requires digital models capable of providing accurate and precise prediction of the tissue-level response of the body in the seated posture. To achieve such models, the human anatomy must be represented with high fidelity. This information can readily be defined using medical imaging techniques such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Current practices for constructing digital human models, based on the magnetic resonance (MR) images, in a lying down (supine) posture have reduced the error in the geometric representation of human anatomy relative to reconstructions based on data from cadaveric studies. Nonetheless, the significant differences between seated and supine postures in segment orientation, soft-tissue deformation and soft tissue strain create a need for data obtained in postures more similar to the application posture. In this study, we present a novel method for creating digital human models based on seated MR data. An adult-male volunteer was scanned in a simulated driving posture using a FONAR 0.6T upright MRI scanner with a T1 scanning protocol. To compensate for unavoidable image distortion near the edges of the study, images of the same anatomical structures were obtained in transverse and sagittal planes. Combinations of transverse and sagittal images were used to reconstruct the major anatomical features from the buttocks through the knees, including bone, muscle and fat tissue perimeters, using Solidworks® software. For each MR image, B-splines were created as contours for the anatomical structures of interest, and LOFT commands were used to interpolate between the generated Bsplines. The reconstruction of the pelvis, from MR data, was enhanced by the use of a template model generated in previous work CT images. A non-rigid registration algorithm was used to fit the pelvis template into the MR data. Additionally, MR image processing was conducted to both the left and the right sides of the model due to the intended asymmetric posture of the volunteer during the MR measurements. The presented subject-specific, three-dimensional model of the buttocks and thighs will add value to optimisation cycles in automotive seat development when used in simulating human interaction with automotive seats.

CT scanning in archaeology

In this chapter we will review the use of x-ray computed tomography (CT) scanning in the field of archaeology. The story will be told in roughly chronological order, starting with the first reported use of a CT scanner in the field of archaeology and then look at some some possibilities for the future. Since the introduction of the x-ray CT scanner in the 1970’s the quality of the images has steadily improved enabling the role of the CT scanner to expand into the field of archaeology. In the context of this chapter, archaeology will be deemed to include the study of ancient human remains and artefacts but exclude remains from pre-history, which normally comes under the heading of palaeontology. (It would perhaps be appropriate to note that CT scanners have been successfully applied in the study of fossils). CT scans have mostly been used to study mummies but have also been used to examine other archaeological artefacts such as clay tablets, scrolls, pottery, bronze statues and swords.

3D reconstruction of long bones utilising magnetic resonance imaging (MRI)

The design of pre-contoured fracture fixation implants (plates and nails) that correctly fit the anatomy of a patient utilises 3D models of long bones with accurate geometric representation. 3D data is usually available from computed tomography (CT) scans of human cadavers that generally represent the above 60 year old age group. Thus, despite the fact that half of the seriously injured population comes from the 30 year age group and below, virtually no data exists from these younger age groups to inform the design of implants that optimally fit patients from these groups. Hence, relevant bone data from these age groups is required. The current gold standard for acquiring such data–CT–involves ionising radiation and cannot be used to scan healthy human volunteers. Magnetic resonance imaging (MRI) has been shown to be a potential alternative in the previous studies conducted using small bones (tarsal bones) and parts of the long bones. However, in order to use MRI effectively for 3D reconstruction of human long bones, further validations using long bones and appropriate reference standards are required. Accurate reconstruction of 3D models from CT or MRI data sets requires an accurate image segmentation method. Currently available sophisticated segmentation methods involve complex programming and mathematics that researchers are not trained to perform. Therefore, an accurate but relatively simple segmentation method is required for segmentation of CT and MRI data. Furthermore, some of the limitations of 1.5T MRI such as very long scanning times and poor contrast in articular regions can potentially be reduced by using higher field 3T MRI imaging. However, a quantification of the signal to noise ratio (SNR) gain at the bone - soft tissue interface should be performed; this is not reported in the literature. As MRI scanning of long bones has very long scanning times, the acquired images are more prone to motion artefacts due to random movements of the subject‟s limbs. One of the artefacts observed is the step artefact that is believed to occur from the random movements of the volunteer during a scan. This needs to be corrected before the models can be used for implant design. As the first aim, this study investigated two segmentation methods: intensity thresholding and Canny edge detection as accurate but simple segmentation methods for segmentation of MRI and CT data. The second aim was to investigate the usability of MRI as a radiation free imaging alternative to CT for reconstruction of 3D models of long bones. The third aim was to use 3T MRI to improve the poor contrast in articular regions and long scanning times of current MRI. The fourth and final aim was to minimise the step artefact using 3D modelling techniques. The segmentation methods were investigated using CT scans of five ovine femora. The single level thresholding was performed using a visually selected threshold level to segment the complete femur. For multilevel thresholding, multiple threshold levels calculated from the threshold selection method were used for the proximal, diaphyseal and distal regions of the femur. Canny edge detection was used by delineating the outer and inner contour of 2D images and then combining them to generate the 3D model. Models generated from these methods were compared to the reference standard generated using the mechanical contact scans of the denuded bone. The second aim was achieved using CT and MRI scans of five ovine femora and segmenting them using the multilevel threshold method. A surface geometric comparison was conducted between CT based, MRI based and reference models. To quantitatively compare the 1.5T images to the 3T MRI images, the right lower limbs of five healthy volunteers were scanned using scanners from the same manufacturer. The images obtained using the identical protocols were compared by means of SNR and contrast to noise ratio (CNR) of muscle, bone marrow and bone. In order to correct the step artefact in the final 3D models, the step was simulated in five ovine femora scanned with a 3T MRI scanner. The step was corrected using the iterative closest point (ICP) algorithm based aligning method. The present study demonstrated that the multi-threshold approach in combination with the threshold selection method can generate 3D models from long bones with an average deviation of 0.18 mm. The same was 0.24 mm of the single threshold method. There was a significant statistical difference between the accuracy of models generated by the two methods. In comparison, the Canny edge detection method generated average deviation of 0.20 mm. MRI based models exhibited 0.23 mm average deviation in comparison to the 0.18 mm average deviation of CT based models. The differences were not statistically significant. 3T MRI improved the contrast in the bone–muscle interfaces of most anatomical regions of femora and tibiae, potentially improving the inaccuracies conferred by poor contrast of the articular regions. Using the robust ICP algorithm to align the 3D surfaces, the step artefact that occurred by the volunteer moving the leg was corrected, generating errors of 0.32 ± 0.02 mm when compared with the reference standard. The study concludes that magnetic resonance imaging, together with simple multilevel thresholding segmentation, is able to produce 3D models of long bones with accurate geometric representations. The method is, therefore, a potential alternative to the current gold standard CT imaging.