A quality assurance protocol for diffusion tensor imaging using the head phantom from American College of Radiology.
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ABSTRACT:
SUBMITTER: Wang ZJ
PROVIDER: S-EPMC3145225 | biostudies-other | 2011 Jul
REPOSITORIES: biostudies-other
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Project description:PurposeTo design and commission a head and neck (H&N) anthropomorphic phantom that the Imaging and Radiation Oncology Core Houston (IROC-H) can use to verify the quality of intensity-modulated proton therapy H&N treatments for institutions participating in National Cancer Institute-sponsored clinical trials.Materials and methodsThe phantom design was based on a generalized oropharyngeal tumor, including critical H&N structures (parotid glands and spinal cord). Radiochromic film and thermoluminescent dosimeter (TLD)-100 capsules were embedded in the phantom and used to evaluate dose delivery. A spot-scanning treatment plan with typical clinical constraints for H&N cancer was created by using the Eclipse analytic algorithm. The treatment plan was approved by a radiation oncologist and the phantom was irradiated 4 times. The measured dose distribution using a ±7%/4 mm gamma analysis (85% of pixels passing) and point doses were compared with the treatment planning system calculations. The prescribed target dose was 6 Gy (RBE) with 646.2 cGy (RBE) and 648.6 cGy (RBE) planned to the superior and inferior TLD, respectively.ResultsFor point dosimetry, the average measured-to-calculated dose ratios were 0.984 and 0.986 for the superior and inferior target TLD, respectively. Dose values for the superior and inferior target TLDs were 636.1 cGy and 639.6 cGy, respectively. For the relative dose comparison, the pixel passing rates for the axial and sagittal films, respectively, were 95.5% and 94.2% for trial 1, 97.3% and 93.2% for trial 2, 93.4% and 90.0% for trial 3, and 96.2% and 92.7% for trial 4.ConclusionThe anthropomorphic H&N phantom was successfully designed so that TLD measured-to-calculated ratios were within IROC-H's 7% acceptance criteria, 1.6% and 1.4% lower than expected for the superior and inferior target TLDs, respectively. All trials passed the 85% pixel passing criteria established at IROC-H for the relative dose comparison performed when using a gamma index of ±7%/4 mm.
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Project description:BackgroundDiffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.MethodsAll diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.ResultsTSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.ConclusionAutomated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.
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Project description:BackgroundEvaluation of changes in tumor size from images acquired by ultrasound (US), computed tomography (CT) or magnetic resonance imaging (MRI) is a common measure of cancer chemotherapy efficacy. Tumor size measurement based on either the World Health Organization (WHO) criteria or the Response Evaluation Criteria in Solid Tumors (RECIST) is the only imaging biomarker for anti-cancer drug testing presently approved by the United States Food and Drug Administration (FDA). The aim of this paper was to design and test a quality assurance phantom with the capability of monitoring tumor size changes with multiple preclinical imaging scanners (US, CT and MRI) in order to facilitate preclinical anti-cancer drug testing.MethodsThree phantoms (Gammex/UTHSCSA Mark 1, Gammex/UTHSCSA Mark 2 and UTHSCSA multimodality tumor measurement phantom) containing tumor-simulating test objects were designed and constructed. All three phantoms were scanned in US, CT and MRI devices. The size of test objects in the phantoms was measured from the US, CT and MRI images. RECIST, WHO and volume analyses were performed.ResultsThe smaller phantom size, simplified design and better test object CT contrast of the UTHSCSA multimodality tumor measurement phantom allowed scanning of the phantom in preclinical US, CT and MRI scanners compared with only limited preclinical scanning capability of Mark 1 and Mark 2 phantoms. For all imaging modalities, RECIST and WHO errors were reduced for UTHSCSA multimodality tumor measurement phantom (≤1.69 ± 0.33%) compared with both Mark 1 (≤ -7.56 ± 6.52%) and Mark 2 (≤ 5.66 ± 1.41%) phantoms. For the UTHSCSA multimodality tumor measurement phantom, measured tumor volumes were highly correlated with NIST traceable design volumes for US (R2 = 1.000, p < 0.0001), CT (R2 = 0.9999, p < 0.0001) and MRI (R2 = 0.9998, p < 0.0001).ConclusionsThe UTHSCSA multimodality tumor measurement phantom described in this study can potentially be a useful quality assurance tool for verifying radiologic assessment of tumor size change during preclinical anti-cancer therapy testing with multiple imaging modalities.
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Project description:PurposeTo determine radiation dose indexes for computed tomography (CT) performed with renal colic protocols in the United States, including frequency of reduced-dose technique usage and any institutional-level factors associated with high or low dose indexes.Materials and methodsThe Dose Imaging Registry (DIR) collects deidentified CT data, including examination type and dose indexes, for CT performed at participating institutions; thus, the DIR portion of the study was exempt from institutional review board approval and was HIPAA compliant. CT dose indexes were examined at the institutional level for CT performed with a renal colic protocol at institutions that contributed at least 10 studies to the registry as of January 2013. Additionally, patients undergoing CT for renal colic at a single institution (with institutional review board approval and informed consent from prospective subjects and waiver of consent from retrospective subjects) were studied to examine individual renal colic CT dose index patterns and explore relationships between patient habitus, demographics, and dose indexes. Descriptive statistics were used to analyze dose indexes, and linear regression and Spearman correlations were used to examine relationships between dose indexes and institutional factors.ResultsThere were 49 903 renal colic protocol CT examinations conducted at 93 institutions between May 2011 and January 2013. Mean age ± standard deviation was 49 years ± 18, and 53.9% of patients were female. Institutions contributed a median of 268 (interquartile range, 77-699) CT studies. Overall mean institutional dose-length product (DLP) was 746 mGy ⋅ cm (effective dose, 11.2 mSv), with a range of 307-1497 mGy ⋅ cm (effective dose, 4.6-22.5 mSv) for mean DLPs. Only 2% of studies were conducted with a DLP of 200 mGy ⋅ cm or lower (a "reduced dose") (effective dose, 3 mSv), and only 10% of institutions kept DLP at 400 mGy ⋅ cm (effective dose, 6 mSv) or less in at least 50% of patients.ConclusionReduced-dose renal protocol CT is used infrequently in the United States. Mean dose index is higher than reported previously, and institutional variation is substantial.
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