Prostate cancer detection at early stages is crucial for desirable treatment outcome. Among available imaging modalities, ultrasound (US) elastography is being developed as an effective clinical tool for prostate cancer diagnosis. Current clinical US elastography systems utilise strain imaging where tissue strain images are generated to approximate the tissue elastic modulus distribution. While strain images can be generated in real-time fashion, they lack the accuracy necessary for having desirable sensitivity and specificity. To improve strain imaging, full inversion based elastography techniques were proposed. Among these techniques, a constrained elastography technique was developed which showed promising results as long as the tumor and prostate geometry can be obtained accurately from the imaging modality used in conjunction with the elastography system. This requirement is not easy to fulfill, especially with US imaging. To address this issue, we present an unconstrained full inversion prostate elastography method in conjunction with US imaging where knowledge of tissue geometry is not necessary. One of the reasons that full inversion elastography techniques have not been routinely used in the clinic is lack of clinical validation studies. To our knowledge, no quasistatic full inversion based prostate US elastography technique has been applied in vivo before. In this work, the proposed method was applied to clinical prostate data and reconstructed elasticity images were compared to corresponding annotated histopathology images which is the first quasi-static full inversion based prostate US elastography technique applied successfully in vivo. Results demonstrated a good potential for clinical utility of the proposed method.
A biomechanical model is proposed to predict deflated lung tumor motion caused by diaphragm respiratory motion. This
model can be very useful for targeting the tumor in tumor ablative procedures such as lung brachytherapy. To minimize
motion within the target lung, these procedures are performed while the lung is deflated. However, significant amount of
tissue deformation still occurs during respiration due to the diaphragm contact forces. In the absence of effective realtime
image guidance, biomechanical models can be used to estimate tumor motion as a function of diaphragm's position.
To develop this model, Finite Element Method (FEM) was employed. To demonstrate the concept, we conducted an
animal study of an ex-vivo porcine deflated lung with a tumor phantom. The lung was deformed by compressing a
diaphragm mimicking cylinder against it. Before compression, 3D-CT image of this lung was acquired, which was
segmented and turned into FE mesh. The lung tissue was modeled as hyperelastic material with a contact loading to
calculate the lung deformation and tumor motion during respiration. To validate the results from FE model, the motion
of a small area on the surface close to the tumor was tracked while the lung was being loaded by the cylinder. Good
agreement was demonstrated between the experiment results and simulation results. Furthermore, the impact of tissue
hyperelastic parameters uncertainties in the FE model was investigated. For this purpose, we performed in-silico
simulations with different hyperelastic parameters. This study demonstrated that the FEM was accurate and robust for
tumor motion prediction.
A novel technique is proposed to construct CT image of a totally deflated lung using breath-hold lung's preoperative
CT images acquired during respiration. Such a constructed CT image is very useful in tumor targeting
during tumor ablative procedures such as lung brachytherapy used for lung cancer treatment. To minimize motion
within the target lung, tumor ablative procedures are frequently performed while the lung is totally deflated.
Deflating the lung during such procedures renders pre-operative images ineffective for tumor targeting, because
those images correspond to the lung while it is partially inflated. Furthermore, the problem cannot be solved using
intra-operative Ultrasound (US) images. This is because the quality of lung US images degrades substantially as a
result of the residual air inside the deflated lung, thus it is not an effective intra-operative imaging modality by
itself. One possible approach for image-guided lung brachytherapy is to register high quality preoperative CT
images of the deflated lung with their corresponding low quality intra-operative US images. To obtain the CT
images of deflated lung, a novel image construction technique is presented. The proposed technique was
implemented using two deformable registration methods: multi-resolution B-spline and multi-resolution demons.
The technique was applied to ex vivo porcine lungs where results obtained were found to be very encouraging.
KEYWORDS: Tumors, Lung, Motion models, Tissues, Finite element methods, Lung cancer, Data modeling, Chemical elements, Medical research, Systems modeling
Deflated lung's geometry simplifications effects on the accuracy of its biomechanical model used for its tumor
motion prediction are investigated. This investigation is necessary to determine the highest degree of
simplifications that can be incorporated in the lung's Finite Element (FE) model without compromising its ability
to predict tumor motion with reasonable accuracy. The simplifications involve neglecting the lung's airways in its
FE model. Such simplification is important to avoid unnecessary complications and to pave the way for fast tumor
location prediction during a lung tumor ablative procedure such as brachytherapy. One major factor, which may
affect the accuracy of such ablative procedures, is tumor motion resulting from lung tissue deformation caused by
respiration. Although the target lung is almost completely deflated during the procedure, tissue deformation
remains an issue due to diaphragm contact forces during respiration. In this investigation several numerical
experiments were conducted using different tumor and airway sizes and locations in conjunction with both elastic
and hyperelastic material models. Sensitivity of the tumor's motion prediction accuracy to the geometry
simplification was then presented as a function of airways' size relative to the tumor's size. FE analysis results
obtained for both material models suggest that tumor displacements due to surface contact forces are not very
sensitive to geometry simplification carried out by omitting airways as long as the airways size does not exceed the
tumor size.
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