p-i-n heterojunctions with BiFeO3 perovskite nanoparticles and p- and n-type oxides: photovoltaic properties.

We formed p-i-n heterojunctions based on a thin film of BiFeO3 nanoparticles. The perovskite acting as an intrinsic semiconductor was sandwiched between a p-type and an n-type oxide semiconductor as hole- and electron-collecting layer, respectively, making the heterojunction act as an all-inorganic oxide p-i-n device. We have characterized the perovskite and carrier collecting materials, such as NiO and MoO3 nanoparticles as p-type materials and ZnO nanoparticles as the n-type material, with scanning tunneling spectroscopy; from the spectrum of the density of states, we could locate the band edges to infer the nature of the active semiconductor materials. The energy level diagram of p-i-n heterojunctions showed that type-II band alignment formed at the p-i and i-n interfaces, favoring carrier separation at both of them. We have compared the photovoltaic properties of the perovskite in p-i-n heterojunctions and also in p-i and i-n junctions. From current-voltage characteristics and impedance spectroscopy, we have observed that two depletion regions were formed at the p-i and i-n interfaces of a p-i-n heterojunction. The two depletion regions operative at p-i-n heterojunctions have yielded better photovoltaic properties as compared to devices having one depletion region in the p-i or the i-n junction. The results evidenced photovoltaic devices based on all-inorganic oxide, nontoxic, and perovskite materials

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.

Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control

HARDWARE-ACCELERATED AUTOMATIC 3D NONRIGID IMAGE REGISTRATION

Software implementations of 3D nonrigid image registration, an essential tool in medical applications like radiotherapies and image-guided surgeries, run excessively slow on traditional computers. These algorithms can be accelerated using hardware methods by exploiting parallelism at different levels in the algorithm. We present here, an implementation of a free-form deformation-based algorithm on a field programmable gate array (FPGA) with a customized, parallel and pipelined architecture. We overcome the performance bottlenecks and gain speedups of up to 40x over traditional computers while achieving accuracies comparable to software implementations. In this work, we also present a method to optimize the deformation field using a gradient descent-based optimization scheme and solve the problem of mesh folding, commonly encountered during registration using free-form deformations, using a set of linear constraints. Finally, we present the use of novel dataflow modeling tools to automatically map registration algorithms to hardware like FPGAs while allowing for dynamic reconfiguration

Developments and Applications of Laser-Based and X-Ray-Based Biomedical Thermoacoustic Imaging Techniques

Thermoacoustic imaging (TAI) is one class of biomedical imaging techniques that share the same physical basis, called the thermoacoustic effect (TAE). The TAE phenomenon can be categorized as sonic waves generated following the absorption of energy/heat. In recent decades, as a result of the continuous development of radiation sources such as masers and lasers, the TAE phenomenon has been extensively utilized to achieve biomedical imaging. The hybrid modality offers high contrast and spectroscopic-based specificity image with ultrasonic spatial resolution. It shows great potential for preclinical research and clinical practice in achieving anatomical, functional, and molecular images. So far, however, TAI has not been widely adopted in clinic. The major challenges include 1) the limited imaging depth due to the applied radiation and 2) the difficulty in achieving quantitative image. The purpose of this research is to further investigate the fundamental mechanism of TAI and to broaden its applications. In the first part of this study, laser-based TAI technique, also known as photoacoustic (PA) imaging, is implemented to improve the diagnosis of Crohn’s disease, especially solving the challenge of characterizing the intestinal strictures in bowel. The feasibility of assessing the spatially varying molecular components in ex vivo intestinal strictures by obtaining PA molecular component images using a developed acoustic resolution PA microscopy system is validated. Then, the microscopy system is miniaturized to a prototype side-view scanning capsule-shaped probe and its practicability in quantitatively differentiate the intestinal disease conditions is proved by performing in vivo colonoscopy in the rabbit disease model. In the second part of this study, the potential applications of x-ray-based TAI technique, named x-ray induced acoustic (XA) imaging, are evaluated. Based on soft-tissue phantom studies, the feasibility in monitoring the position of the x-ray beam and measuring the spatially varying dose deposition is validated. These results suggested a potential application of XA imaging method as a novel in vivo dosimetric tool in external beam radiotherapy. Furthermore, an XA and ultrasound (US) dual-modality imaging system is established utilizing a commercial ultrasound unit, aiming to obtain XA image and US image simultaneously, both in real time. As demonstrated by the experiments on soft-tissue phantoms, the XA image showing the deposited radiation dose and the US image capturing the motion of target tissue can be naturally co-registered, offering a potential approach for image-guided radiotherapy.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150038/1/halei_1.pd