The operation of the bowtie filter in x-ray CT is correct if the object being scanned is properly centered in the scanner's field-of-view. Otherwise, the dose delivered to the patient and image noise will deviate from optimal setting. We investigate the effect of miscentering on image noise and surface dose on three commercial CT scanners. Six cylindrical phantoms with different size and material were scanned on each scanner. The phantoms were positioned at 0, 2, 4 and 6 cm below the isocenter of the scanner's field-of-view. Regression models of surface dose and noise were produced as a function of miscentering magnitude and phantom's size. 480 patients were assessed using the calculated regression models to estimate the influence of patient miscentering on image noise and patient surface dose in seven imaging centers. For the 64-slice CT scanner, the maximum increase of surface dose using the CTDI-32 phantom was 13.5%, 33.3% and 51.1% for miscenterings of 2, 4 and 6 cm, respectively. The analysis of patients' scout scans showed miscentering of 2.2 cm in average below the isocenter. An average increase of 23% and 7% was observed for patient dose and image noise, respectively. The maximum variation in patient miscentering derived from the comparison of imaging centers using the same scanner was 1.6 cm. Patient miscentering may substantially

We aimed to investigate whether short dynamic PET imaging started at injection, complemented with routine clinical acquisition at 60-min post-injection (static), can achieve reliable kinetic analysis. Methods: Dynamic and static 18F-2-fluoro-2-deoxy-D-glucose (FDG) PET data were generated using realistic simulations to assess uncertainties due to statistical noise as well as bias. Following image reconstructions, kinetic parameters obtained from a 2-tissue-compartmental model (2TCM) were estimated, making use of the static image, and the time duration of dynamic PET data were incrementally shortened. We also investigated, in the first 2-min, different frame sampling rates, towards optimized dynamic PET imaging. Kinetic parameters from shortened dynamic datasets were additionally estimated for 9 patients (15 scans) with liver metastases of colorectal cancer, and were compared with those derived from full dynamic imaging using correlation and Passing-Bablok regression analyses. Results: The results showed that by reduction of dynamic scan times from 60-min to as short as 5-min, while using static data at 60-min post-injection, bias and variability stayed comparable in estimated kinetic parameters. Early frame samplings of 5, 24 and 30 s yielded highest biases compared to other schemes. An early frame sampling of 10 s generally kept both bias and variability to a minimum. In clinical studies, strong correlation (r ≥ 0.97, P < 0.0001) existed between all kinetic parameters in full vs. shortened scan protocols. Conclusions: Shortened 5-min dynamic scan, sampled as 12 × 10 + 6 × 30 s, followed by 3-min static image at 60min post-injection, enables accurate and robust estimation of 2TCM parameters, while enabling generation of SUV estimates.

This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (v ) for Model 1; v and volume transfer constant (K ) for Model 2; v , K and rate constant (k ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, k and v (K /k ) of Model 3, and K of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS, allows the construction of a DCE-MRI-based fuzzy integrated predictor for the prediction of the survival of patients with GBM.

A novel technique is proposed considering tissue-specific dose kernels in the dose calculation algorithm. This algorithm potentially enables patient-specific dosimetry and improves estimation of the average absorbed dose of Y in a tumor located in lung, bone, and soft tissue interface by 6.98% compared with the conventional methods.

No abstract