As thermal imaging attempts to estimate very small tissue motion (on the order of tens of microns), it can be negatively influenced by signal decorrelation. Patient's breathing and cardiac cycle generate shifts in the RF signal patterns. Other sources of movement could be found outside the patient's body, like transducer slippage or small vibrations due to environment factors like electronic noise. Here, we build upon a robust displacement estimation method for ultrasound elastography and we investigate an iterative motion compensation algorithm, which can detect and remove non-heat induced tissue motion at every step of the ablation procedure. The validation experiments are performed on laboratory induced ablation lesions in ex-vivo tissue. The ultrasound probe is either held by the operator's hand or supported by a robotic arm. We demonstrate the ability to detect and remove non-heat induced tissue motion in both settings. We show that removing extraneous motion helps unmask the effects of heating. Our strain estimation curves closely mirror the temperature changes within the tissue. While previous results in the area of motion compensation were reported for experiments lasting less than 10 seconds, our algorithm was tested on experiments that lasted close to 20 minutes.
Ultrasound elastography is an imaging technology which can detect differences in tissue stiffness based on tissue
deformation. For successful clinical use in cancer diagnosis and monitoring the method should be robust to sources
of decorrelation between ultrasound images. A regularized Dynamic Programming (DP) approach was used for
displacement estimation in compressed tissue. In the Analytic Minimization (AM) extension of DP, integer
displacements are calculated just for one RF-line, and later propagated laterally throughout the entire image.
This makes the seed RF-line very important; faulty seed lines could propagate erroneous displacement values
throughout the image resulting in the appearance of false "lesions". In this paper we analyze the robustness of
this method in free-hand palpation of laboratory tissue phantoms. We are proposing an update to the algorithm
which includes a random search for the most robust seed RF-line. Axial integer displacements are obtained
on each random seed line individually with DP optimization. For each random axial RF-line, multiple random
values for decorrelation compensation are used in the displacement estimation. The displacement values are then
compared and several metrics of stability and consistency are considered. A ranking is established and the line
deemed most robust will become the seed line for displacement propagation, while also selecting the most stable
value for decorrelation compensation. The random search can be achieved at no additional computational cost
in a parallel implementation. The results indicate significant improvement in the robustness of the DP approach,
while maintaining real-time computation of strain images.
Monitoring the ablation process in order to document the adequacy of margins during treatment is of significant
importance. Observing that the ablation lesion is harder than normal tissue, it has been proposed to monitor
the ablation using ultrasound elastography. Furthermore, it has been reported that the ablated cancer tumor is
harder than ablated normal tissue. In this paper we propose an ultrasound elastography technique for visualizing
the ablation lesion and the ablated cancerous tumor in Hepatocellular carcinoma (HCC). This work focuses on
devising techniques to generate elasticity images which distinguish the ablated cancerous tumor and the ablated
normal lesion. We first calculate the displacement field between two ultrasound images acquired before and after
some compression. We then use the displacement field to calculate the correlation coefficient between the two
images. Parts of the tissue that undergo large deformation give small correlation coefficient due to decorrelation
within each window, and parts of the tissue that undergo small deformation give large correlation coefficient.
Simulating phantoms with two lesions, a harder tumor inside a hard lesion, using finite element and Field II, we
show that this method enables delineating the tumor from the lesion.
Radical prostatectomy using the laparoscopic and robot-assisted approach lacks tactile feedback. Without palpation,
the surgeon needs an affordable imaging technology which can be easily incorporated into the laparoscopic
surgical procedure, allowing for precise real time intraoperative tumor localization that will guide the extent
of surgical resection. Ultrasound elastography (USE) is a novel ultrasound imaging technology that can detect
differences in tissue density or stiffness based on tissue deformation. USE was evaluated here as an enabling
technology for image guided laparoscopic prostatectomy. USE using a 2D Dynamic Programming (DP) algorithm
was applied on data from ex vivo human prostate specimens. It proved consistent in identification of
lesions; hard and soft, malignant and benign, located in the prostate's central gland or in the peripheral zone.
We noticed the 2D DP method was able to generate low-noise elastograms using two frames belonging to the
same compression or relaxation part of the palpation excitation, even at compression rates up to 10%. Good
preliminary results were validated by pathology findings, and also by in vivo and ex vivo MR imaging. We also
evaluated the use of ultrasound elastography for imaging cavernous nerves; here we present data from animal