No abstract

Recently dynamic, time-resolved three-dimensional computed tomography angiography (CTA) has been introduced to the neurological imaging community. However, the radiation dose delivered to patients in time-resolved CTA protocol is a high and potential risk associated with the ionizing radiation dose. Thus, minimizing the radiation dose is highly desirable for time-resolved CTA. In order to reduce the radiation dose delivered during dynamic, contrast-enhanced CT applications, we introduce here the CT formulation of HighlY constrained back PRojection (HYPR) imaging. We explore the radiation dose reduction approaches of both acquiring a reduced number of projections for each image and lowering the tube current used during acquisition. We then apply HYPR image reconstruction to produce image sets at a reduced patient dose and with low image noise. Numerical phantom experiments and retrospective analysis of in vivo canine studies are used to assess the accuracy and quality of HYPR reduced dose image sets and validate our approach. Experimental results demonstrated that a factor of 6–8 times radiation dose reduction is possible when the HYPR algorithm is applied to time-resolved CTA exams.

Perfusion nuclear medicine study has a statistically significantly lower effective and breast-absorbed dose (P<0.0001) when compared with CTPA. Similarly, the fetal-absorbed dose for pulmonary scintigraphy has a statistically lower dose (P=0.0010) when compared with CTPA, even if the ventilation component of pulmonary scintigraphy is performed, although these values are so small that they are unlikely to be clinically significant.

No abstract

Background: To develop a deep learning (DL) method based on multiphase, contrast-enhanced (CE) magnetic resonance imaging (MRI) to distinguish Liver Imaging Reporting and Data System (LI-RADS) grade 3 (LR-3) liver tumors from combined higher-grades 4 and 5 (LR-4/LR-5) tumors for hepatocellular carcinoma (HCC) diagnosis.Methods: A total of 89 untreated LI-RADS-graded liver tumors (35 LR-3, 14 LR-4, and 40 LR-5) were identified based on the radiology MRI interpretation reports. Multiphase 3D T1-weighted gradient echo imaging was acquired at six time points: pre-contrast, four phases immediately post-contrast, and one hepatobiliary phase after intravenous injection of gadoxetate disodium. Image co-registration was performed across all phases on the center tumor slice to correct motion. A rectangular tumor box centered on the tumor area was drawn to extract subset tumor images for each imaging phase, which were used as the inputs to a convolutional neural network (CNN). The pre-trained AlexNet CNN model underwent transfer learning using liver MRI data for LI-RADS tumor grade classification. The output probability number closer to 1 or 0 indicated a higher possibility of being combined LR-4/LR-5 tumor or LR-3 tumor, respectively. Five-fold cross validation was used for training (60% dataset), validation (20%) and testing processes (20%).Results: The DL CNN model for LI-RADS grading using inputs of multiphase liver MRI data acquired at three time points (pre-contrast, arterial, and washout phase) achieved a high accuracy of 0.90, sensitivity of 1.0, precision of 0.835, and AUC of 0.95 with reference to the expert human radiologist report. The CNN output of probability provided radiologists a confidence level of the model's grading for each liver lesion.Conclusions: An AlexNet CNN model for LI-RADS grading of liver lesions provided diagnostic performance comparable to radiologists and offered valuable clinical guidance for differentiating intermediate LR-3 liver lesions from more-likely malignant LR-4/LR-5 lesions in HCC diagnosis.

No abstract

These results indicate that, if validated for other measurement tools and scanners, lung nodule volume measurements from scans acquired and reconstructed with significantly different acquisition and reconstruction techniques can be reliably compared.

Background: To assess the volumetric measurement of small (≤1 cm) nonsolid nodules with computed tomography (CT), focusing on the interaction of state of the art iterative reconstruction (IR) methods and dose with nodule densities, sizes, and shapes. Results: Density had the most important effect on measurement error followed by the interaction of density with nodule size. The nonsolid −630 HU nodules had high accuracy and precision at levels comparable to solid (−10 HU) nonsolid, regardless of reconstruction method and with CTDI vol as low as 0.6 mGy. PB was <5% and <11% for the 10-and 5-mm in nominal diameter −630 HU nodules respectively, and RC was <5% and <12% for the same nodules. For nonsolid −800 HU nodules, PB increased to <11% and <30% for the 10-and 5-mm nodules respectively, whereas RC increased slightly overall but varied widely across dose and reconstruction algorithms for the 5-mm nodules. Model-based IR improved measurement accuracy for the 5-mm, low-density (−800, −630 HU) nodules. For other nodules the effect of reconstruction method was small. Dose did not affect volumetric accuracy and only affected slightly the precision of 5-mm nonsolid nodules.Conclusions: Reasonable values of both accuracy and precision were achieved for volumetric measurements of all 10-mm nonsolid nodules, and for the 5-mm nodules with −630 HU or higher density, when derived from scans acquired with below screening dose levels as low as 0.6 mGy and regardless of reconstruction algorithm.