2 resultados para Biomedical engineering|Nuclear physics|Particle physics
em Duke University
Resumo:
Knowledge-based radiation treatment is an emerging concept in radiotherapy. It
mainly refers to the technique that can guide or automate treatment planning in
clinic by learning from prior knowledge. Dierent models are developed to realize
it, one of which is proposed by Yuan et al. at Duke for lung IMRT planning. This
model can automatically determine both beam conguration and optimization ob-
jectives with non-coplanar beams based on patient-specic anatomical information.
Although plans automatically generated by this model demonstrate equivalent or
better dosimetric quality compared to clinical approved plans, its validity and gener-
ality are limited due to the empirical assignment to a coecient called angle spread
constraint dened in the beam eciency index used for beam ranking. To eliminate
these limitations, a systematic study on this coecient is needed to acquire evidences
for its optimal value.
To achieve this purpose, eleven lung cancer patients with complex tumor shape
with non-coplanar beams adopted in clinical approved plans were retrospectively
studied in the frame of the automatic lung IMRT treatment algorithm. The primary
and boost plans used in three patients were treated as dierent cases due to the
dierent target size and shape. A total of 14 lung cases, thus, were re-planned using
the knowledge-based automatic lung IMRT planning algorithm by varying angle
spread constraint from 0 to 1 with increment of 0.2. A modied beam angle eciency
index used for navigate the beam selection was adopted. Great eorts were made to assure the quality of plans associated to every angle spread constraint as good
as possible. Important dosimetric parameters for PTV and OARs, quantitatively
re
ecting the plan quality, were extracted from the DVHs and analyzed as a function
of angle spread constraint for each case. Comparisons of these parameters between
clinical plans and model-based plans were evaluated by two-sampled Students t-tests,
and regression analysis on a composite index built on the percentage errors between
dosimetric parameters in the model-based plans and those in the clinical plans as a
function of angle spread constraint was performed.
Results show that model-based plans generally have equivalent or better quality
than clinical approved plans, qualitatively and quantitatively. All dosimetric param-
eters except those for lungs in the automatically generated plans are statistically
better or comparable to those in the clinical plans. On average, more than 15% re-
duction on conformity index and homogeneity index for PTV and V40, V60 for heart
while an 8% and 3% increase on V5, V20 for lungs, respectively, are observed. The
intra-plan comparison among model-based plans demonstrates that plan quality does
not change much with angle spread constraint larger than 0.4. Further examination
on the variation curve of the composite index as a function of angle spread constraint
shows that 0.6 is the optimal value that can result in statistically the best achievable
plans.
Resumo:
Scatter in medical imaging is typically cast off as image-related noise that detracts from meaningful diagnosis. It is therefore typically rejected or removed from medical images. However, it has been found that every material, including cancerous tissue, has a unique X-ray coherent scatter signature that can be used to identify the material or tissue. Such scatter-based tissue-identification provides the advantage of locating and identifying particular materials over conventional anatomical imaging through X-ray radiography. A coded aperture X-ray coherent scatter spectral imaging system has been developed in our group to classify different tissue types based on their unique scatter signatures. Previous experiments using our prototype have demonstrated that the depth-resolved coherent scatter spectral imaging system (CACSSI) can discriminate healthy and cancerous tissue present in the path of a non-destructive x-ray beam. A key to the successful optimization of CACSSI as a clinical imaging method is to obtain anatomically accurate phantoms of the human body. This thesis describes the development and fabrication of 3D printed anatomical scatter phantoms of the breast and lung.
The purpose of this work is to accurately model different breast geometries using a tissue equivalent phantom, and to classify these tissues in a coherent x-ray scatter imaging system. Tissue-equivalent anatomical phantoms were designed to assess the capability of the CACSSI system to classify different types of breast tissue (adipose, fibroglandular, malignant). These phantoms were 3D printed based on DICOM data obtained from CT scans of prone breasts. The phantoms were tested through comparison of measured scatter signatures with those of adipose and fibroglandular tissue from literature. Tumors in the phantom were modeled using a variety of biological tissue including actual surgically excised benign and malignant tissue specimens. Lung based phantoms have also been printed for future testing. Our imaging system has been able to define the location and composition of the various materials in the phantom. These phantoms were used to characterize the CACSSI system in terms of beam width and imaging technique. The result of this work showed accurate modeling and characterization of the phantoms through comparison of the tissue-equivalent form factors to those from literature. The physical construction of the phantoms, based on actual patient anatomy, was validated using mammography and computed tomography to visually compare the clinical images to those of actual patient anatomy.