Barbara Arora

Comparison of T2 and FLAIR imaging for target delineation in high grade gliomas

BackgroundFLAIR and T2 weighted MRIs are used based on institutional preference to delineate high grade gliomas and surrounding edema for radiation treatment planning. Although these sequences have inherent physical differences there is limited data on the clinical and dosimetric impact of using either or both sequences.Methods40 patients with high grade gliomas consecutively treated between 2002 and 2008 of which 32 had pretreatment MRIs with T1, T2 and FLAIR available for review were selected for this study. These MRIs were fused with the treatment planning CT. Normal structures, clinical tumor volume (CTV) and planning tumor volume (PTV) were then defined on the T2 and FLAIR sequences. A Venn diagram analysis was performed for each pair of tumor volumes as well as a fractional component analysis to assess the contribution of each sequence to the union volume. For each patient the tumor volumes were compared in terms of total volume in cubic centimeters as well as anatomic location using a discordance index. The overlap of the tumor volumes with critical structures was calculated as a measure of predicted toxicity. For patients with MRI documented failures, the tumor volumes obtained using the different sequences were compared with the recurrent gross tumor volume (rGTV).ResultsThe FLAIR CTVs and PTVs were significantly larger than the T2 CTVs and PTVs (p < 0.0001 and p = 0.0001 respectively). Based on the discordance index, the abnormality identified using the different sequences also differed in location. Fractional component analysis showed that the intersection of the tumor volumes as defined on both T2 and FLAIR defined the majority of the union volume contributing 63.6% to the CTV union and 82.1% to the PTV union. T2 alone uniquely identified 12.9% and 5.2% of the CTV and PTV unions respectively while FLAIR alone uniquely identified 25.7% and 12% of the CTV and PTV unions respectively. There was no difference in predicted toxicity to normal structures using T2 or FLAIR. At the time of analysis, 26 failures had occurred of which 19 patients had MRIs documenting the recurrence. The rGTV correlated best with the FLAIR CTV but the percentage overlap was not significantly different from that with T2. There was no statistical difference in the percentage overlap with the rGTV and the PTVs generated using either T2 or FLAIR.ConclusionsAlthough both T2 and FLAIR MRI sequences are used to define high grade glial neoplasm and surrounding edema, our results show that the volumes generated using these techniques are different and not interchangeable. These differences have bearing on the use of intensity modulated radiation therapy (IMRT) and highly conformal treatment as well as on future clinical trials where the bias of using one technique over the other may influence the study outcome.

Parameters favorable to intraprostatic radiation dose escalation in men with localized prostate cancer.

PURPOSE To identify , within the framework of a current Phase I trial, whether factors related to intraprostatic cancer lesions (IPLs) or individual patients predict the feasibility of high-dose intraprostatic irradiation. METHODS AND MATERIALS Endorectal coil MRI scans of the prostate from 42 men were evaluated for dominant IPLs. The IPLs, prostate, and critical normal tissues were contoured. Intensity-modulated radiotherapy plans were generated with the goal of delivering 75.6 Gy in 1.8-Gy fractions to the prostate, with IPLs receiving a simultaneous integrated boost of 3.6 Gy per fraction to a total dose of 151.2 Gy, 200% of the prescribed dose and the highest dose cohort in our trial. Rectal and bladder dose constraints were consistent with those outlined in current Radiation Therapy Oncology Group protocols. RESULTS Dominant IPLs were identified in 24 patients (57.1%). Simultaneous integrated boosts (SIB) to 200% of the prescribed dose were achieved in 12 of the 24 patients without violating dose constraints. Both the distance between the IPL and rectum and the hip-to-hip patient width on planning CT scans were associated with the feasibility to plan an SIB (p = 0.002 and p = 0.0137, respectively). CONCLUSIONS On the basis of this small cohort, the distance between an intraprostatic lesion and the rectum most strongly predicted the ability to plan high-dose radiation to a dominant intraprostatic lesion. High-dose SIB planning seems possible for select intraprostatic lesions.

Intra- and inter-radiation therapist reproducibility of daily isocenter verification using prostatic fiducial markers

BackgroundWe sought to determine the intra- and inter-radiation therapist reproducibility of a previously established matching technique for daily verification and correction of isocenter position relative to intraprostatic fiducial markers (FM).Materials and methodsWith the patient in the treatment position, anterior-posterior and left lateral electronic images are acquired on an amorphous silicon flat panel electronic portal imaging device. After each portal image is acquired, the therapist manually translates and aligns the fiducial markers in the image to the marker contours on the digitally reconstructed radiograph. The distances between the planned and actual isocenter location is displayed. In order to determine the reproducibility of this technique, four therapists repeated and recorded this operation two separate times on 20 previously acquired portal image datasets from two patients. The data were analyzed to obtain the mean variability in the distances measured between and within observers.ResultsThe mean and median intra-observer variability ranged from 0.4 to 0.7 mm and 0.3 to 0.6 mm respectively with a standard deviation of 0.4 to 1.0 mm. Inter-observer results were similar with a mean variability of 0.9 mm, a median of 0.6 mm, and a standard deviation of 0.7 mm. When using a 5 mm threshold, only 0.5% of treatments will undergo a table shift due to intra or inter-observer error, increasing to an error rate of 2.4% if this threshold were reduced to 3 mm.ConclusionWe have found high reproducibility with a previously established method for daily verification and correction of isocenter position relative to prostatic fiducial markers using electronic portal imaging.

A dosimetric comparison of four treatment planning methods for high grade glioma

BackgroundHigh grade gliomas (HGG) are typically treated with a combination of surgery, radiotherapy and chemotherapy. Three dimensional (3D) conformal radiotherapy treatment planning is still the main stay of treatment for these patients. New treatment planning methods suggest better dose distributions and organ sparing but their clinical benefit is unclear. The purpose of the current study was to compare normal tissue sparing and tumor coverage using four different radiotherapy planning methods in patients with high grade glioma.MethodsThree dimensional conformal (3D), sequential boost IMRT, integrated boost (IB) IMRT and Tomotherapy (TOMO) treatment plans were generated for 20 high grade glioma patients. T1 and T2 MRI abnormalities were used to define GTV and CTV with 2 and 2.5 cm margins to define PTV1 and PTV2 respectively.ResultsThe mean dose to PTV2 but not to PTV1 was less then 95% of the prescribed dose with IB and IMRT plans. The mean doses to the optic chiasm and the ipsilateral globe were highest with 3D plans and least with IB plans. The mean dose to the contralateral globe was highest with TOMO plans. The mean of the integral dose (ID) to the brain was least with the IB plan and was lower with IMRT compared to 3D plans. The TOMO plans had the least mean D10 to the normal brain but higher mean D50 and D90 compared to IB and IMRT plans. The mean D10 and D50 but not D90 were significantly lower with the IMRT plans compared to the 3D plans.ConclusionNo single treatment planning method was found to be superior to all others and a personalized approach is advised for planning and treating high-grade glioma patients with radiotherapy. Integral dose did not reflect accurately the dose volume histogram (DVH) of the normal brain and may not be a good indicator of delayed radiation toxicity.

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°. 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±0.1mm. 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±0.09mm for CT/CT images, 0.03±0.07mm for MR/MR images, and 0.03±0.35mm 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. PACS numbers: 87.57.nj, 87.57.nm, 87.57.‐N, 87.57.‐s

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.

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 Varians 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. Varians 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 kQclin,Qmsrfclin,fmsr 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. PACS number(s): 87.55.k, 87.55.QrThe 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 Varians 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. Varians 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 kQclin,Qmsrfclin,fmsr 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. PACS number(s): 87.55.k, 87.55.Qr.

Registering Molecular Imaging Information into Anatomic Images with Improved Spatial Accuracy

To make molecular imaging useful in the clinic, accurate image registration must be done to correlate nano-scale events to macro-scale anatomy. The 3D volumetric image registration technique uses visual and quantitative measures to identify the most homogeneous color distribution on a volumetric anatomical landmark. Four phantom PET/CT images were acquired with 5.0 plusmn 0.1 mm shift interval. The image registration shift was compared with the positioning shift. An accuracy of 0.1deg and 0.1 mm was achieved. Cranial PET/CT images from 39 patients were examined. It was found that the average head motion was 0.5-1deg and 1-3 mm, even with a stringent head holder. This small but significant misalignment is beyond the capability of conventional visual-based fusion methods used clinically. The 100 mum accuracy is a step forward to register molecular activities to anatomy for high precision interventions

A feasibility study of image registration using volumetrically classified, motion-free bony landmarks in thoracic 4DCT images for image-guided patient setup

As rigid image registration becomes unreliable in the presence of involuntary organ motion, we present a novel approach to register CT images using stable bony landmarks for image-guided patient setup. Using 3D Volumetric Image Registration (3DVIR) technique, bony anatomy is volumetrically-classified as registration landmark, while soft tissues are ignored. Based on 4DCT, it was found that the spine, posterior ribs and clavicles do not move with respiration and remain registered throughout the breathing cycle. However, mutual information based registration produces an error of 1–2 mm due to moving soft tissues. It is suggested that the 3DVIR can improve image-guided setup.

SU‐E‐I‐51: Metal Artifact Reduction in CT Using Deformable Tissue‐Class Modeling

PURPOSE The purpose of this work is to present a novel approach to reduce metal artifacts in CT using deformable tissue-class modeling. The tissue-class model is generated by combining information from both original corrupted image slice (original slice) with metal artifacts, and its neighboring image slice (reference slice) in the same scan, but without metal artifacts. Missing or corrupted information in the original slice is estimated from the reference slice. METHODS The proposed method consists of four major steps. (1) Reference slice is deformed to the original slice using diffeomorphic demons registration algorithm. (2) Strong bright streak including metal objects, and dark streak artifacts are segmented respectively, by applying the basic connected threshold method on the difference image between the original and deformed reference image. (3) Pixel intensities of strong bright and dark streaks in original slice are replaced by those of corresponding pixels in deformed reference slice. The k-means clustering algorithm is then utilized to segment the original slice into four tissue classes: air, soft tissue, normal tissue, and bone. This tissue-class model is forward projected to produce a model sinogram. (4) Corrupted projection data in the sinogram of the original slice is substituted by corresponding segments in the model sinogram. The completed sinogram is then reconstructed with the filtered back-projection to produce the corrected image. RESULTS The proposed method has been tested on clincal patient data with dental fillings, prostate fiducial markers. Both qualitative and quantitative analysis indicate that image quality has been improved considerably after correction, and the proposed method outperforms the standard linear-interpolation based method, and the method using tissue-class modelling on the original slice only. CONCLUSION A novel method for metal artifact reduction in CT has been developed. The method is capable of reducing bright and dark streaks caused by metal objects in CT.