General Method for the Synthesis of Functionalized Tetrabenzo[8]circulenes.

Functionalized derivatives of the saddle-shaped molecule tetrabenzo[8]circulene were successfully synthesized through a Diels-Alder/oxidative cyclodehydrogenation approach. This methodology improves on our previously reported synthesis, affording products containing both electron-rich and electron-poor functional groups from readily available starting materials in a more efficient manner. The optoelectronic effects that result from the introduction of this functionality are presented and briefly discussed.

Output factor comparison of Monte Carlo and measurement for Varian TrueBeam 6 MV and 10 MV flattening filter-free stereotactic radiosurgery system.

The dose measurements of the small field sizes, such as conical collimators used in stereotactic radiosurgery (SRS), are a significant challenge due to many factors including source occlusion, detector size limitation, and lack of lateral electronic equilibrium. One useful tool in dealing with the small field effect is Monte Carlo (MC) simulation. In this study, we report a comparison of Monte Carlo simulations and measurements of output factors for the Varian SRS system with conical collimators for energies of 6 MV flattening filter-free (6 MV) and 10 MV flattening filter-free (10 MV) on the TrueBeam accelerator. Monte Carlo simulations of Varian's SRS system for 6 MV and 10 MV photon energies with cones sizes of 17.5 mm, 15.0 mm, 12.5 mm, 10.0 mm, 7.5 mm, 5.0 mm, and 4.0 mm were performed using EGSnrc (release V4 2.4.0) codes. Varian's version-2 phase-space files for 6 MV and 10 MV of TrueBeam accelerator were utilized in the Monte Carlo simulations. Two small diode detectors Edge (Sun Nuclear) and Small Field Detector (SFD) (IBA Dosimetry) were applied to measure the output factors. Significant errors may result if detector correction factors are not applied to small field dosimetric measurements. Although it lacked the machine-specific kfclin,fmsrQclin,Qmsr correction factors for diode detectors in this study, correction factors were applied utilizing published studies conducted under similar conditions. For cone diameters greater than or equal to 12.5 mm, the differences between output factors for the Edge detector, SFD detector, and MC simulations are within 3.0% for both energies. For cone diameters below 12.5 mm, output factors differences exhibit greater variations.

Synthesis and structural data of tetrabenzo[8]circulene.

In 1976, the first attempted synthesis of the saddle-shaped molecule [8]circulene was reported. The next 37 years produced no advancement towards the construction of this complicated molecule. But remarkably, over the last six months, a flurry of progress has been made with two groups reporting independent and strikingly different strategies for the synthesis of [8]circulene derivatives. Herein, we present a third synthetic method, in which we target tetrabenzo[8]circulene. Our approach employs a Diels-Alder reaction and a palladium-catalyzed arylation reaction as the key steps. Despite calculations describing the instability of [8]circulene, coupled with the reported instability of synthesized derivatives of the parent molecule, the addition of four fused benzenoid rings around the periphery of the molecule provides a highly stable structure. This increased stability over the parent [8]circulene was predicted by using Clar's theory of aromatic sextets and is a result of the compound becoming fully benzenoid upon incorporation of these additional rings. The synthesized compound exhibits remarkable stability under ambient conditions-even at elevated temperatures-with no signs of decomposition over several months. The solid-state structure of this compound is significantly twisted compared to the calculated structure primarily as a result of crystal-packing forces in the solid state. Despite this contortion from the lowest-energy structure, a range of structural data is presented confirming the presence of localized aromaticity in this large polycyclic aromatic hydrocarbon.

Parallel fuzzy connected image segmentation on GPU.

PURPOSE: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. METHODS: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. RESULTS: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. CONCLUSIONS: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

Patient-specific CT dosimetry calculation: a feasibility study.

Current estimation of radiation dose from computed tomography (CT) scans on patients has relied on the measurement of Computed Tomography Dose Index (CTDI) in standard cylindrical phantoms, and calculations based on mathematical representations of "standard man". Radiation dose to both adult and pediatric patients from a CT scan has been a concern, as noted in recent reports. The purpose of this study was to investigate the feasibility of adapting a radiation treatment planning system (RTPS) to provide patient-specific CT dosimetry. A radiation treatment planning system was modified to calculate patient-specific CT dose distributions, which can be represented by dose at specific points within an organ of interest, as well as organ dose-volumes (after image segmentation) for a GE Light Speed Ultra Plus CT scanner. The RTPS calculation algorithm is based on a semi-empirical, measured correction-based algorithm, which has been well established in the radiotherapy community. Digital representations of the physical phantoms (virtual phantom) were acquired with the GE CT scanner in axial mode. Thermoluminescent dosimeter (TLDs) measurements in pediatric anthropomorphic phantoms were utilized to validate the dose at specific points within organs of interest relative to RTPS calculations and Monte Carlo simulations of the same virtual phantoms (digital representation). Congruence of the calculated and measured point doses for the same physical anthropomorphic phantom geometry was used to verify the feasibility of the method. The RTPS algorithm can be extended to calculate the organ dose by calculating a dose distribution point-by-point for a designated volume. Electron Gamma Shower (EGSnrc) codes for radiation transport calculations developed by National Research Council of Canada (NRCC) were utilized to perform the Monte Carlo (MC) simulation. In general, the RTPS and MC dose calculations are within 10% of the TLD measurements for the infant and child chest scans. With respect to the dose comparisons for the head, the RTPS dose calculations are slightly higher (10%-20%) than the TLD measurements, while the MC results were within 10% of the TLD measurements. The advantage of the algebraic dose calculation engine of the RTPS is a substantially reduced computation time (minutes vs. days) relative to Monte Carlo calculations, as well as providing patient-specific dose estimation. It also provides the basis for a more elaborate reporting of dosimetric results, such as patient specific organ dose volumes after image segmentation.

Barriers and facilitators to implementing telehealth services during the COVID-19 pandemic: A qualitative analysis of interviews with cystic fibrosis care team members.

BACKGROUND: The COVID-19 pandemic forced cystic fibrosis (CF) care programs to rapidly shift from in-person care delivery to telehealth. Our objective was to provide a qualitative exploration of facilitators and barriers to: 1) implementing high-quality telehealth and 2) navigating reimbursement for telehealth services. METHODS: We used data from the 2020 State of Care CF Program Survey (n=286 U.S. care programs) administered in August-September to identify two cohorts of programs, with variation in telehealth quality (n=12 programs) and reimbursement (n=8 programs). We conducted focus groups and semi-structured interviews with CF program directors and coordinators in December 2020, approximately 9 months from onset of the pandemic. We used the Consolidated Framework for Implementation Research to identify facilitators and barriers of implementation, and inductive thematic analysis to identify facilitators and barriers of reimbursement. RESULTS: Factors differentiating programs with greater and lower perceived telehealth quality included telehealth characteristics (perceived advantage over in-person care, cost, platform quality); external influences (needs and resources of those served by the CF program), characteristics of the CF program (compatibility with workflows, relative priority, available resources); characteristics of team members (individual stage of change), and processes for implementation (engaging patients and teams). Reimbursement barriers included documentation to optimize billing; reimbursement of multi-disciplinary team members, remote monitoring, and telephone-only telehealth; and lower volume of patients. CONCLUSIONS: A number of factors are associated with successful implementation and reimbursement of telehealth. Future efforts should provide guidance and incentives that support telehealth delivery and infrastructure, share best practices across CF programs, and remove barriers.

Correction of motion-induced misalignment in co-registered PET/CT and MRI (T1/T2/FLAIR) head images for stereotactic radiosurgery.

The purpose was to evaluate and correct the co-registration of diagnostic PET/CT and MRI/MRI images for stereotactic radiosurgery (SRS) using 3D volumetric image registration (3DVIR). The 3DVIR utilizes the homogeneity of color distribution over a volumetric anatomical landmark as the registration criterion with submillimeter accuracy. Fifty-three PET/CT and MRI (T1, T2 and FLAIR) image sets of patients with brain lesions were acquired sequentially from a hybrid PET/CT or an MRI scanner with common diagnostic head holding devices. Twenty-five sets of head 18F-FDG-PET/CT images were scanned over a 10-minute interval and 14 whole-body sets were scanned over a 30-minute interval. Fourteen sets of MRI images were acquired, and each 3-modal image set (T1, T2 and FLAIR) was scanned in sequence at time 0, ~5 and ~20 minutes. The misalignments in these "co-registered" images were evaluated and corrected using the 3DVIR. Using the head immobilization devices commonly found in diagnostic PET/CT and MRI/MRI imaging, 80%-100% of these "co-registered" images were identified as misaligned. For PET/CT, the magnitude of misalignment was 0.4° ± 0.5° and 0.7 ± 0.4 mm for 10-minute scans, and 0.8° ± 1.2° and 2.7 ± 1.7 mm for 30-minute scans. For MRI/MRI, the magnitude was 0.2° ± 0.4° and 0.3 ± 0.2 mm for 5-minute scan intervals, and 1.1° ± 0.7° and 1.2 ± 1.4 mm for 20-minute intervals. Small, but significant, misalignment is present in the co-registered diagnostic PET/CT and MRI/MRI images and can be corrected in SRS treatment planning using the volumetric image registration for improved target localization within the clinical error tolerance.

Automated glioma grading on conventional MRI images using deep convolutional neural networks.

PURPOSE: Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non-invasively distinguishing low-grade (Grades II and III) glioma (LGG) and high-grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs). METHODS: All MRI images have been preprocessed first by rigid image registration and intensity inhomogeneity correction. Both proposed methods consist of two steps: (a) three-dimensional (3D) brain tumor segmentation based on a modification of the popular U-Net model; (b) tumor classification on segmented brain tumor. In the first method, the slice with largest area of tumor is determined and the state-of-the-art mask R-CNN model is employed for tumor grading. To improve the performance of the grading model, a two-dimensional (2D) data augmentation has been implemented to increase both the amount and the diversity of the training images. In the second method, denoted as 3DConvNet, a 3D volumetric CNNs is applied directly on bounding image regions of segmented tumor for classification, which can fully leverage the 3D spatial contextual information of volumetric image data. RESULTS: The proposed schemes were evaluated on The Cancer Imaging Archive (TCIA) low grade glioma (LGG) data, and the Multimodal Brain Tumor Image Segmentation (BraTS) Benchmark 2018 training datasets with fivefold cross validation. All data are divided into training, validation, and test sets. Based on biopsy-proven ground truth, the performance metrics of sensitivity, specificity, and accuracy are measured on the test sets. The results are 0.935 (sensitivity), 0.972 (specificity), and 0.963 (accuracy) for the 2D Mask R-CNN based method, and 0.947 (sensitivity), 0.968 (specificity), and 0.971 (accuracy) for the 3DConvNet method, respectively. In regard to efficiency, for 3D brain tumor segmentation, the program takes around ten and a half hours for training with 300 epochs on BraTS 2018 dataset and takes only around 50 s for testing of a typical image with a size of 160 × 216 × 176. For 2D Mask R-CNN based tumor grading, the program takes around 4 h for training with around 60 000 iterations, and around 1 s for testing of a 2D slice image with size of 128 × 128. For 3DConvNet based tumor grading, the program takes around 2 h for training with 10 000 iterations, and 0.25 s for testing of a 3D cropped image with size of 64 × 64 × 64, using a DELL PRECISION Tower T7910, with two NVIDIA Titan Xp GPUs. CONCLUSIONS: Two effective glioma grading methods on conventional MRI images using deep convolutional neural networks have been developed. Our methods are fully automated without manual specification of region-of-interests and selection of slices for model training, which are common in traditional machine learning based brain tumor grading methods. This methodology may play a crucial role in selecting effective treatment options and survival predictions without the need for surgical biopsy.

Quantitative prediction of respiratory tidal volume based on the external torso volume change: a potential volumetric surrogate.

An external respiratory surrogate that not only highly correlates with but also quantitatively predicts internal tidal volume should be useful in guiding four-dimensional computed tomography (4DCT), as well as 4D radiation therapy (4DRT). A volumetric surrogate should have advantages over external fiducial point(s) for monitoring respiration-induced motion of the torso, which deforms in synchronization with a patient-specific breathing pattern. This study establishes a linear relationship between the external torso volume change (TVC) and lung air volume change (AVC) by validating a proposed volume conservation hypothesis (TVC = AVC) throughout the respiratory cycle using 4DCT and spirometry. Fourteen patients' torso 4DCT images and corresponding spirometric tidal volumes were acquired to examine this hypothesis. The 4DCT images were acquired using dual surrogates in ciné mode and amplitude-based binning in 12 respiratory stages, minimizing residual motion artifacts. Torso and lung volumes were calculated using threshold-based segmentation algorithms and volume changes were calculated relative to the full-exhalation stage. The TVC and AVC, as functions of respiratory stages, were compared, showing a high correlation (r = 0.992 +/- 0.005, p < 0.0001) as well as a linear relationship (slope = 1.027 +/- 0.061, R(2) = 0.980) without phase shift. The AVC was also compared to the spirometric tidal volumes, showing a similar linearity (slope = 1.030 +/- 0.092, R(2) = 0.947). In contrast, the thoracic and abdominal heights measured from 4DCT showed relatively low correlation (0.28 +/- 0.44 and 0.82 +/- 0.30, respectively) and location-dependent phase shifts. This novel approach establishes the foundation for developing an external volumetric respiratory surrogate.

A novel analytical approach to the prediction of respiratory diaphragm motion based on external torso volume change.

An analytical approach to predict respiratory diaphragm motion should have advantages over a correlation-based method, which cannot adapt to breathing pattern changes without re-calibration for a changing correlation and/or linear coefficient. To quantitatively calculate the diaphragm motion, a new expandable 'piston' respiratory (EPR) model was proposed and tested using 4DCT torso images of 14 patients. The EPR model allows two orthogonal lung motions (with a few volumetric constraints): (1) the lungs expand (DeltaV(EXP)) with the same anterior height variation as the thoracic surface, and (2) the lungs extend (DeltaV(EXT)) with the same inferior distance as the volumetrically equivalent 'piston' diaphragm. A volume conservation rule (VCR) established previously (Li et al 2009 Phys. Med. Biol. 54 1963-78) was applied to link the external torso volume change (TVC) to internal lung volume change (LVC) via lung air volume change (AVC). As the diaphragm moves inferiorly, the vacant space above the diaphragm inside the rib cage should be filled by lung tissue with a volume equal to DeltaV(EXT) (=LVC-DeltaV(EXP)), while the volume of non-lung tissues in the thoracic cavity should conserve. It was found that DeltaV(EXP) accounted for 3-24% of the LVC in these patients. The volumetric shape of the rib cage, characterized by the variation of cavity volume per slice over the piston motion range, deviated from a hollow cylinder by -1.1% to 6.0%, and correction was made iteratively if the variation is >3%. The predictions based on the LVC and TVC (with a conversion factor) were compared with measured diaphragm displacements (averaged from six pivot points), showing excellent agreements (0.2 +/- 0.7 mm and 0.2 +/- 1.2 mm, respectively), which are within clinically acceptable tolerance. Assuming motion synchronization between the piston and points of interest along the diaphragm, point motion was estimated but at higher uncertainty ( approximately 10% +/- 4%). This analytical approach provides a patient-independent technique to calculate the patient-specific diaphragm motion, using the anatomical and respiratory volumetric constraints.

Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments.

Registration is critical for image-based treatment planning and image-guided treatment delivery. Although automatic registration is available, manual, visual-based image fusion using three orthogonal planar views (3P) is always employed clinically to verify and adjust an automatic registration result. However, the 3P fusion can be time consuming, observer dependent, as well as prone to errors, owing to the incomplete 3-dimensional (3D) volumetric image representations. It is also limited to single-pixel precision (the screen resolution). The 3D volumetric image registration (3DVIR) technique was developed to overcome these shortcomings. This technique introduces a 4th dimension in the registration criteria beyond the image volume, offering both visual and quantitative correlation of corresponding anatomic landmarks within the two registration images, facilitating a volumetric image alignment, and minimizing potential registration errors. The 3DVIR combines image classification in real-time to select and visualize a reliable anatomic landmark, rather than using all voxels for alignment. To determine the detection limit of the visual and quantitative 3DVIR criteria, slightly misaligned images were simulated and presented to eight clinical personnel for interpretation. Both of the criteria produce a detection limit of 0.1 mm and 0.1 degree. To determine the accuracy of the 3DVIR method, three imaging modalities (CT, MR and PET/CT) were used to acquire multiple phantom images with known spatial shifts. Lateral shifts were applied to these phantoms with displacement intervals of 5.0+/-0.1 mm. The accuracy of the 3DVIR technique was determined by comparing the image shifts determined through registration to the physical shifts made experimentally. The registration accuracy, together with precision, was found to be: 0.02+/-0.09 mm for CT/CT images, 0.03+/-0.07 mm for MR/MR images, and 0.03+/-0.35 mm for PET/CT images. This accuracy is consistent with the detection limit, suggesting an absence of detectable systematic error. This 3DVIR technique provides a superior alternative to the 3P fusion method for clinical applications.

Brain tumor segmentation using holistically nested neural networks in MRI images.

PURPOSE: Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. METHODS: Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. RESULTS: The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 h for training with 50,000 iterations, and approximately 30 s for testing of a typical MRI image in the BRATS 2013 dataset with a size of 160 × 216 × 176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. CONCLUSIONS: An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy.

Primary tumor cells resected from cancer patients and decorated with a novel form of CD80 protein serve as effective antigen-presenting cells for the induction of autologous T cell immune responses ex vivo.

Vaccination with autologous tumor cells genetically modified to express costimulatory molecules has shown utility for cancer immunotherapy in preclinical and limited clinical settings. Given the complicated nature of gene therapy, a practical alternative approach has been designed that relies on modification of the cell membrane with biotin and its "decoration" with a chimeric protein composed of the functional portion of human CD80 and core streptavidin (CD80-SA). We tested whether primary tumor cells resected from cancer patients can be decorated with CD80-SA and whether such cells serve as antigen-presenting cells (APCs) to generate autologous T cell responses ex vivo. Tumors and peripheral blood lymphocytes (PBLs) were collected from 14 lung, 9 colon, and 2 breast "treatment-naive" cancer patients presenting various clinical stages of the disease. Tumors were mechanically processed, irradiated, decorated with CD80-SA or control streptavidin (SA) protein, and used as APCs in ex vivo autologous T cell-proliferative and cytotoxicity assays. All tumor samples were modified with CD80-SA, albeit with various degrees of decoration ranging from 21.8 to 100%. CD80- SA-decorated cells generated significant proliferative responses in autologous T cells from 9 of 16 evaluable patients (p < 0.05). Proliferative responses were CD80-SA specific and heterogeneous, with stimulation indices ranging from 0.25 to 45. In 15 of 15 evaluable patients, CD80-SA-specific cytotoxic T cell responses against autologous tumors were generated, 11 of which were significant, with specific killing ranging from 5 to 70%. Taken together, these data demonstrate that primary tumor cells can be effectively decorated with CD80-SA and that such cells serve as APCs to induce autologous antitumor T cell responses.

Elimination of Cerenkov interference in a fibre-optic-coupled radiation dosemeter.

An optical fibre point dosemeter based on the gated detection of the luminescence from a Cu(1+)-doped fused quartz detector effectively eliminated errors due to Cerenkov radiation and native fibre fluorescence. The gated optical fibre dosemeter overcomes serious problems faced by scintillation and optically stimulated luminescence approaches to optical fibre point dosimetry. The dosemeter was tested using an external beam radiotherapy machine that provided pulses of 6 MV X rays. Gated detection was used to discriminate the signal collected during the radiation pulses, which included contributions from Cerenkov radiation and native fibre fluorescence, from the signal collected between the radiation pulses, which contained only the long-lived luminescence from the Cu(1+)-doped fused quartz detector. Gated detection of the luminescence provided accurate, real-time dose measurements that were linear with absorbed dose, independent of dose rate and that were accurate for all field sizes studied.

A novel 3D volumetric voxel registration technique for volume-view-guided image registration of multiple imaging modalities.

PURPOSE: To provide more clinically useful image registration with improved accuracy and reduced time, a novel technique of three-dimensional (3D) volumetric voxel registration of multimodality images is developed. METHODS AND MATERIALS: This technique can register up to four concurrent images from multi-modalities with volume view guidance. Various visualization effects can be applied, facilitating global and internal voxel registration. Fourteen computed tomography/magnetic resonance (CT/MR) image sets and two computed tomography/positron emission tomography (CT/PET) image sets are used. For comparison, an automatic registration technique using maximization of mutual information (MMI) and a three-orthogonal-planar (3P) registration technique are used. RESULTS: Visually sensitive registration criteria for CT/MR and CT/PET have been established, including the homogeneity of color distribution. Based on the registration results of 14 CT/MR images, the 3D voxel technique is in excellent agreement with the automatic MMI technique and is indicatory of a global positioning error (defined as the means and standard deviations of the error distribution) using the 3P pixel technique: 1.8 degrees +/- 1.2 degrees in rotation and 2.0 +/- 1.3 (voxel unit) in translation. To the best of our knowledge, this is the first time that such positioning error has been addressed. CONCLUSION: This novel 3D voxel technique establishes volume-view-guided image registration of up to four modalities. It improves registration accuracy with reduced time, compared with the 3P pixel technique. This article suggests that any interactive and automatic registration should be safe-guarded using the 3D voxel technique.

Inverse treatment planning based on MRI for HDR prostate brachytherapy.

PURPOSE: To develop and optimize a technique for inverse treatment planning based solely on magnetic resonance imaging (MRI) during high-dose-rate brachytherapy for prostate cancer. METHODS AND MATERIALS: Phantom studies were performed to verify the spatial integrity of treatment planning based on MRI. Data were evaluated from 10 patients with clinically localized prostate cancer who had undergone two high-dose-rate prostate brachytherapy boosts under MRI guidance before and after pelvic radiotherapy. Treatment planning MRI scans were systematically evaluated to derive a class solution for inverse planning constraints that would reproducibly result in acceptable target and normal tissue dosimetry. RESULTS: We verified the spatial integrity of MRI for treatment planning. MRI anatomic evaluation revealed no significant displacement of the prostate in the left lateral decubitus position, a mean distance of 14.47 mm from the prostatic apex to the penile bulb, and clear demarcation of the neurovascular bundles on postcontrast imaging. Derivation of a class solution for inverse planning constraints resulted in a mean target volume receiving 100% of the prescribed dose of 95.69%, while maintaining a rectal volume receiving 75% of the prescribed dose of <5% (mean 1.36%) and urethral volume receiving 125% of the prescribed dose of <2% (mean 0.54%). CONCLUSION: Systematic evaluation of image spatial integrity, delineation uncertainty, and inverse planning constraints in our procedure reduced uncertainty in planning and treatment.

Radioiodine fallout and breast-feeding.

The rationale for suspension of breast-feeding after exposure to radioiodine in fallout is presented.

MRI-guided HDR prostate brachytherapy in standard 1.5T scanner.

PURPOSE: Magnetic resonance imaging (MRI) provides superior visualization of the prostate and surrounding anatomy, making it the modality of choice for imaging the prostate gland. This pilot study was performed to determine the feasibility and dosimetric quality achieved when placing high-dose-rate prostate brachytherapy catheters under MRI guidance in a standard "closed-bore" 1.5T scanner. METHODS AND MATERIALS: Patients with intermediate-risk and high-risk localized prostate cancer received MRI-guided high-dose-rate brachytherapy boosts before and after a course of external beam radiotherapy. Using a custom visualization and targeting program, the brachytherapy catheters were placed and adjusted under MRI guidance until satisfactory implant geometry was achieved. Inverse treatment planning was performed using high-resolution T(2)-weighted MRI. RESULTS: Ten brachytherapy procedures were performed on 5 patients. The median percentage of volume receiving 100% of prescribed minimal peripheral dose (V(100)) achieved was 94% (mean, 92%; 95% confidence interval, 89-95%). The urethral V(125) ranged from 0% to 18% (median, 5%), and the rectal V(75) ranged from 0% to 3.1% (median, 0.3%). In all cases, lesions highly suspicious for malignancy could be visualized on the procedural MRI, and extracapsular disease was identified in 2 patients. CONCLUSION: High-dose-rate prostate brachytherapy in a standard 1.5T MRI scanner is feasible and achieves favorable dosimetry within a reasonable period with high-quality image guidance. Although the procedure was well tolerated in the acute setting, additional follow-up is required to determine the long-term safety and efficacy of this approach.

Outcomes and toxicities of stereotactic body radiation therapy for non-spine bone oligometastases.

PURPOSE: Stereotactic body radiation therapy (SBRT) is being applied more widely for oligometastatic disease. This technique is now being used for non-spine bony metastases in addition to liver, spine, and lung. However, there are few studies examining the toxicity and outcomes of SBRT for non-spine bone metastases. METHODS AND MATERIALS: Between 2008 and 2012, 74 subjects with oligometastatic non-spine bony metastases of varying histologies were treated at the Mayo Clinic with SBRT. A total of 85 non-spine bony sites were treated. Median local control, overall survival, and progression-free survival were described. Acute toxicity (defined as toxicity <90 days) and late toxicity (defined as toxicity ≥90 days) were reported and graded as per standardized Common Toxicity Criteria for Adverse Events 4.0 criteria. RESULTS: The median age of patients treated was 60 years. The most common histology was prostate cancer (31%) and most patients had fewer than 3 sites of disease at the time of simulation (64%). Most of the non-spine bony sites lay within the pelvis (65%). Dose and fractionation varied but the most common prescription was 24 Gy/1 fraction. Local recurrence occurred in 7 patients with a median time to failure of 2.8 months. Local control was 91.8% at 1 year. With a median follow-up of 7.6 months, median SBRT specific overall survival and progression-free survival were 9.3 months and 9.7 months, respectively. Eighteen patients developed acute toxicity (mostly grade 1 and 2 fatigue and acute pain flare); 9 patients developed grade 1-2 late toxicities. Two patients developed pathologic fractures but both were asymptomatic. There were no late grade 3 or 4 toxicities. CONCLUSIONS: Stereotactic body radiation therapy is a feasible and tolerable treatment for non-spine bony metastases. Longer follow-up will be needed to accurately determine late effects.

GPU-based relative fuzzy connectedness image segmentation.

PURPOSE: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. METHODS: The most common FC segmentations, optimizing an [script-l](∞)-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. RESULTS: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. CONCLUSIONS: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.