Background: Determining malignancy of prostate pathological samples is important for treatment planning of prostate cancer. Traditionally, this is performed by expert pathologists who evaluate the structure of prostate glands in the biopsy samples. However, this is a subjective task due to inter-and intraobserver differences among pathologists. Also, it is time-consuming and difficult to some extent. Therefore, automatic determination of malignancy of prostate pathological samples is of interest.Methods: A texture-based technique is first used to segment the prostate glands in the image. Features related to size and shape of these glands are then extracted and combined to generate an index, which is proportional to malignancy of cancer. A linear classifier is employed to classify the specimens into benign (low potential for malignancy) and malignant.Results: The leave-one-out technique is employed to evaluate the method using two datasets. The first has 91 images with similar magnifications and illuminations while the second has 199 images with different magnifications and illuminations. In the experiments, accuracies of about 98 and 95% have been achieved for these two datasets, respectively.Conclusions: An image analysis approach is employed to evaluate prostate pathological images. Experimental results show that the proposed method can successfully classify the prostate biopsy samples into benign and malignant. They also show that the proposed method is robust to variations in magnification and illumination. q

Purpose The growing use of magnetic resonance imaging (MRI) as a substitute for computed tomography-based treatment planning requires the development of effective algorithms to generate electron density maps for treatment planning and patient setup verification. The purpose of this work was to develop a method to synthesize computerized tomography (CT) for MR-only radiotherapy of head and neck cancer patients. Methods The algorithm is based on registration of multiple patient datasets containing both MRI and CT images (a ”multi-atlas” algorithm). Twelve matched pairs of good quality CT and MRI scans (those without apparent motion and blurring artifacts) were selected from a pool of head and neck cancer patients to form the atlas. All atlas MRI scans were pre-processed to reduce scanner- and patient-induced intensity inhomogeneities and to standardize their intensity histograms. Atlas CT and MRIs were co-registered using a novel bone-to-air replacement technique applied to the CT scans that improves the similarity between CTs and MRIs and facilitates the registration process. For each new patient, all atlas MRIs are deformed initially onto the new patients’ MRI. We introduce a generalized registration error (GRE) metric that automatically measures the goodness of local registration between MRI pairs. The final synthetic CT value at each point is a nonlinear GRE-weighted average of the atlas CTs. For evaluation, the leave-one-out technique was used for synthetic CT generation and the mean absolute error (MAE) between the original and synthetic CT was computed over the entire CT image. The impact of our proposed CT-MR registration scheme on the accuracy of the final synthetic CT was also studied. The original treatment plans were also recomputed on the new synthetic CTs and dose-volume histogram metrics were compared. In addition, the two-dimensional (2D) gamma analysis at 1%/1mm and 2%/2 mm dose difference/distance to agreement was also performed to study the dose distribution at the isocenter. Results MAE error (± standard deviation) between the original and the synthetic CTs was 64 ± 10, 113 ± 12, and 130 ± 28 Hounsfield Unit (HU) for the entire image, air, and bone regions, respectively. Our results showed that our proposed bone-suppression based CT-MR fusion and GRE-weighted strategy could lower the overall MAE error between the original and synthetic CTs by ~69% and ~34%, respectively. Dose re-calculation comparison showed highly consistent results between plans based on the synthetic vs. the original CTs. The 2D gamma analysis revealed the pass rate of 95.44 ± 2.5 and 99.36 ± 0.71 for 1%/1mm and 2%/2 mm criteria, respectively. Due to local registration weighting, the method is robust with respect to MRI imaging artifacts. Conclusion We developed a novel image analysis technique to synthesize CT for head and neck anatomy. Novel methods were introduced to accurately register atlas CTs and MRIs as well as to weight the final electron density maps using local registration goodness estimates. The resulting...

Purpose To develop an approach for computer-aided detection (CAD) of small brain metastases in post-Gd T1-weighted magnetic resonance imaging (MRI). Method A set of unevenly spaced 3D spherical shell templates was optimized to localize brain metastatic lesions by cross-correlation analysis with MRI. Theoretical and simulation analyses of effects of lesion size and shape heterogeneity were performed to optimize the number and size of the templates and the cross-correlation thresholds. Also, effects of image factors of noise and intensity variation on the performance of the CAD system were investigated. A nodule enhancement strategy to improve sensitivity of the system and a set of criteria based upon the size, shape and brightness of lesions were used to reduce false positives. An optimal set of parameters from the FROC curves was selected from a training dataset, and then the system was evaluated on a testing dataset including 186 lesions from 2753 MRI slices. Reading results from two radiologists are also included. Results Overall, a 93.5% sensitivity with 0.024 of intra-cranial false positive rate (IC-FPR) was achieved in the testing dataset. Our investigation indicated that nodule enhancement was very effective in improving both sensitivity and specificity. The size and shape criteria reduced the IC-FPR from 0.075 to 0.021, and the brightness criterion decreases the extra-cranial FPR from 0.477 to 0.083 in the training dataset. Readings from the two radiologists had sensitivities of 60% and 67% in the training dataset and 70% and 80% in the testing dataset for the metastatic lesions <5 mm in diameter. Conclusion Our proposed CAD system has high sensitivity and fairly low FPR for detection of the small brain metastatic lesions in MRI compared to the previous work and readings of neuroradiologists. The potential of this method for assisting clinical decision- making warrants further evaluation and improvements.

Purpose We aimed to develop a hippocampal vascular injury surrogate marker for early prediction of late neurocognitive dysfunction in patients receiving brain radiotherapy (RT). Methods and Materials 27 patients (17 males and 10 females, age 31–80 years) were enrolled in an IRB-approved prospective longitudinal study. Patients were diagnosed with a low-grade glioma or benign tumor and treated by 3-D conformal or intensity-modulated RT with a median dose of 54 Gy (50.4–59.4 Gy in 1.8-Gy fractions). Six dynamic-contrast enhanced MRI scans were performed from pre-RT to 18-month post-RT, and quantified for vascular parameters related to blood-brain barrier permeability, Ktrans, and the fraction of blood plasma volume, Vp. The temporal changes in the means of hippocampal Ktrans and Vp after starting RT were modeled by integrating the dose effects with age, sex, hippocampal laterality and presence of tumor/edema near a hippocampus. Finally, the early vascular dose-response in hippocampi was correlated with neurocognitive dysfunction 6 and 18-months post-RT. Results The Ktrans mean increased significantly from pre-RT to 1-month post-RT (p<0.0004), which significantly depended on sex (p<0.0007) and age (p<0.00004), with the dose-response more pronounced in older females. Also, the vascular dose-response in the left hippocampus of females correlated significantly with changes in memory function at 6 (r = −0.95, p<0.0006) and 18-months (r = −0.88, p<0.02) post-RT. Conclusions The early hippocampal vascular dose-response could be a predictor of late neurocognitive dysfunction. A personalized hippocampus sparing strategy may be considered in the future.

PurposeTo investigate the potential of an atlas‐based approach in generation of synthetic CT for pelvis anatomy.MethodsTwenty‐three matched pairs of computed tomography (CT) and magnetic resonance imaging (MRI) scans were selected from a pool of prostate cancer patients. All MR scans were preprocessed to reduce scanner‐ and patient‐induced intensity inhomogeneities and to standardize their intensity histograms. Ten (training dataset) of 23 pairs were then utilized to construct the coregistered CT‐MR atlas. The synthetic CT for a new patient is generated by appropriately weighting the deformed atlas of CT‐MR onto the new patient MRI. The training dataset was used as an atlas to generate the synthetic CT for the rest of the patients (test dataset). The mean absolute error (MAE) between the deformed planning CT and synthetic CT was computed over the entire CT image, bone, fat, and muscle tissues. The original treatment plans were also recomputed on the new synthetic CTs and dose–volume histogram metrics were compared. The results were compared with a commercially available synthetic CT Software (MRCAT) that is routinely used in our clinic.Results MAE errors (±SD) between the deformed planning CT and our proposed synthetic CTs in the test dataset were 47 ± 5, 116 ± 12, 36 ± 6, and 47 ± 5 HU for the entire image, bone, fat, and muscle tissues respectively. The MAEs were 65 ± 5, 172 ± 9, 43 ± 7, and 42 ± 4 HU for the corresponding tissues in MRCAT CT. The dosimetric comparison showed consistent results for all plans using our synthetic CT, deformed planning CT and MRCAT CT.ConclusionWe investigated the potential of a multiatlas approach to generate synthetic CT images for the pelvis. Our results demonstrate excellent results in terms of HU value assignment compared to the original CT and dosimetric consistency.

This paper presents a new algorithm for Gleason grading of pathological images of prostate. Structural features of the glands are extracted and used in a tree-structured (TS) algorithm to classify the images into five Gleason grades of 1 to 5. In this algorithm the image is first segmented to locate the glandular regions using texture features and a K-means clustering algorithm. The glands are then labeled from the glandular regions. In each stage of the proposed TS algorithm, shape and intensity-based features of the glands are extracted and used in a linear classifier to classify the image into two groups. Despite some proposed methods in the literature which use only texture features, this technique uses the features like roundness and shape distribution, which are related to the structure of the glands in each grade and are independent of the magnification. The proposed method is therefore robust to illumination and magnification variations. To evaluate the performance of the proposed method, we use two datasets. Data set 1 contains 91 images with similar magnifications and illuminations. Data set 2 contains 199 images with different magnifications and illuminations. Using leave-one-out technique, we achieve 95% and 85% accuracy for dataset 1 and 2, respectively.

Summary Dose painting of the physiological imaging-defined subvolumes of the tumors by intensity-modulated radiotherapy is hypothesized to lead to a better outcome than distributing a uniform dose within a target volume defined by anatomic imaging. We developed a general method to delineate the subvolumes of a tumor based upon multiple physiological imaging and tested their complementary roles for assessment of therapy response. Purpose To develop an image analysis framework to delineate the physiological imaging-defined subvolumes of a tumor in relating to treatment response and outcome. Materials and Methods Our proposed approach delineates the subvolumes of a tumor based upon its heterogeneous distributions of physiological imaging parameters. The method assigns each voxel a probabilistic membership function belonging to the physiological parameter classes defined in a sample of tumors, and then calculates the related subvolumes in each tumor. We applied our approach to regional cerebral blood volume (rCBV) and Gd-DTPA transfer constant (Ktrans) images of patients who had brain metastases and were treated by whole brain radiotherapy (WBRT). Forty-five lesions were included in the analysis. Changes in the rCBV (or Ktrans)-defined subvolumes of the tumors from pre RT to 2 weeks (2W) after the start of WBRT were evaluated for differentiation of responsive, stable and progressive tumors using Mann-Whitney U test. Performance of the newly developed metrics for predicting tumor response to WBRT was evaluated by Receiver Operating Characteristic (ROC) analysis. Results The percentage decrease in the high-CBV defined subvolumes of the tumors from pre-RT to 2W was significantly greater in the group of responsive tumors than the group of stable and progressive ones (p<0.007). The change in the high-CBV defined subvolumes of the tumors from pre-RT to 2W was a predictor for post-RT response significantly better than change in the gross tumor volume observed during the same time interval (p=0.012), suggesting the physiological change occurs prior to the volumetric change. Also, Ktrans did not add significant discriminatory information for assessing response with respect to rCBV. Conclusion The physiological imaging-defined subvolumes of the tumors delineated by our method could be a candidate for boost target, for which further development and evaluation is warranted.

Purpose To assess whether an increase in a subvolume of intrahepatic tumor with elevated arterial perfusion during radiation therapy (RT) predicts tumor progression post RT. Methods and Materials Twenty patients with unresectable intrahepatic cancers undergoing RT were enrolled in a prospective IRB-approved study. Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) were performed prior to RT (pre-RT), after delivering ~60% of the planned dose (mid-RT) and one month after completion of RT to quantify hepatic arterial perfusion. The arterial perfusions of the tumors at pre-RT were clustered into low-normal and elevated perfusion by a fuzzy clustering-based method, and the tumor subvolumes with elevated arterial perfusion were extracted from the hepatic arterial perfusion images. The percentage changes in the tumor subvolumes and means of arterial perfusion over the tumors from pre-RT to mid-RT were evaluated for predicting tumor progression post-RT. Results Of the 24 tumors, 6 tumors in 5 patients progressed 5–21 months after RT completion. Neither tumor volumes nor means of tumor arterial perfusion at pre-RT were predictive of treatment outcome. The mean arterial perfusion over the tumors increased significantly at mid-RT in progressive tumors comparing to the responsive ones (p=0.006). From pre-RT to mid-RT, the responsive tumors had a decrease in the tumor subvolumes with elevated arterial perfusion (median: −14%, range: −75% – 65%), while the progressing tumors had an increase of the subvolumes (median: 57%, range: −7% – 165%) (p=0.003). Receiver operating characteristic (ROC) analysis of the percentage change in the subvolume for predicting tumor progression post-RT had an area under the curve (AUC) of 0.90. Conclusion The increase in the subvolume of the intrahepatic tumor with elevated arterial perfusion during RT has the potential to be a predictor for tumor progression post-RT. The tumor subvolume could be a radiation boost candidate for response-driven adaptive RT.