Spatial Resolution Fidelity Comparison Between Energy Integrating and Deep Silicon Photon Counting CT: Implications for Pulmonary Imaging.

PURPOSE: We investigated spatial resolution loss away from isocenter for a prototype deep silicon photon-counting detector (PCD) CT scanner and compare with a clinical energy-integrating detector (EID) CT scanner. MATERIALS AND METHODS: We performed three scans on a wire phantom at four positions (isocenter, 6.7, 11.8, and 17.1 cm off isocenter). The acquisition modes were 120 kV EID CT, 120 kV high-definition (HD) EID CT, and 120 kV PCD CT. HD mode used double the projection view angles per rotation as the "regular" EID scan mode. The diameter of the wire was calculated by taking the full width of half max (FWHM) of a profile drawn over the radial and azimuthal directions of the wire. Change in wire diameter appearance was assessed by calculating the ratio of the radial and azimuthal diameter relative to isocenter. t tests were used to make pairwise comparisons of the wire diameter ratio with each acquisition and mean ratios' difference from unity. RESULTS: Deep silicon PCD CT had statistically smaller (P<0.05) changes in diameter ratio for both radial and azimuthal directions compared with both regular and HD EID modes and was not statistically different from unity (P<0.05). Maximum increases in FWMH relative to isocenter were 36%, 12%, and 1% for regular EID, HD EID, and deep silicon PCD, respectively. CONCLUSION: Deep silicon PCD CT exhibits less change in spatial resolution in both the radial and azimuthal directions compared with EID CT.

Ultra-high-resolution spectral silicon-based photon-counting detector CT for coronary CT angiography: Initial results in a dynamic phantom.

BACKGROUND: Recent improvements in CT detector technology have led to smaller detector pixels resolving frequencies beyond 20 lp/cm and enabled ultra-high-resolution CT. Silicon-based photon-counting detector (PCD) CT is one such technology that promises improved spatial and spectral resolution. However, when the detector pixel sizes are reduced, the impact of cardiac motion on CT images becomes more pronounced. Here, we investigated the effects cardiac motion on the image quality of a clinical prototype Si-PCD scanner in a dynamic heart phantom. METHODS: A series of 3D-printed vessels were created to simulate coronary arteries with diameter in the 1-3.5 â€‹mm range. Four coronary stents were set inside the d â€‹= â€‹3.5 â€‹mm vessels and all vessels were filled with contrast agents and were placed inside a dynamic cardiac phantom. The phantom was scanned in motion (60 bpm) and at rest on a prototype clinical Si-PCD CT scanner in 8-bin spectral UHR mode. Virtual monoenergetic images (VMI) were generated at 70 â€‹keV and CT number accuracy and effective spatial resolution (blooming) of rest and motion VMIs were compared. RESULTS: Linear regression analysis of CT numbers showed excellent agreement (r â€‹> â€‹0.99) between rest and motion. We did not observe a significant difference (p â€‹> â€‹0.48) in estimating free lumen diameters. Differences in in-stent lumen diameter and stent strut thickness were non-significant with maximum mean difference of approximately 70 â€‹µm. CONCLUSION: We found no significant degradation in CT number accuracy or spatial resolution due to cardiac motion. The results demonstrate the potential of spectral UHR coronary CT angiography enabled by Si-PCD.

CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT.

BACKGROUND. Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. OBJECTIVE. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers. METHODS. A phantom with polyethylene rings of varying sizes (mimicking patient sizes) as well as inserts of different materials was scanned on an EID CT scanner in single-energy (SE) mode (120-kV images) and in rapid-kilovoltage-switching dual-energy (DE) mode (70-keV images) and on a prototype deep silicon PCD CT scanner (70-keV images). ROIs were placed to measure the CT numbers of the materials. Slopes of CT number as a function of object size were computed. Materials' ideal CT number at 70 keV was computed using the National Institute of Standards and Technology XCOM Photon Cross Sections Database. The root mean square error (RMSE) between measured and ideal numbers was calculated across object sizes. RESULTS. Slope (expressed as Hounsfield units per centimeter) was significantly closer to zero (i.e., less variation in CT number as a function of size) for PCD CT than for SE EID CT for air (1.2 vs 2.4 HU/cm), water (-0.3 vs -1.0 HU/cm), iodine (-1.1 vs -4.5 HU/cm), and bone (-2.5 vs -10.1 HU/cm) and for PCD CT than for DE EID CT for air (1.2 vs 2.8 HU/cm), water (-0.3 vs -1.0 HU/cm), polystyrene (-0.2 vs -0.9 HU/cm), iodine (-1.1 vs -1.9 HU/cm), and bone (-2.5 vs -6.2 HU/cm) (p < .05). For all tested materials, PCD CT had the smallest RMSE, indicating CT numbers closest to ideal numbers; specifically, RMSE (expressed as Hounsfield units) for SE EID CT, DE EID CT, and PCD CT was 32, 44, and 17 HU for air; 7, 8, and 3 HU for water; 9, 10, and 4 HU for polystyrene; 31, 37, and 13 HU for iodine; and 69, 81, and 20 HU for bone, respectively. CONCLUSION. For numerous materials, deep silicon PCD CT, in comparison with SE EID CT and DE EID CT, showed lower CT number variability as a function of size and CT numbers closer to ideal numbers. CLINICAL IMPACT. Greater reliability of CT numbers for PCD CT is important given the dependence of diagnostic pathways on CT numbers.

Photon count rates estimated from 1980 clinical CT scans.

BACKGROUND: All photon counting detectors have a characteristic count rate over which their performance degrades. Degradation in the clinical setting takes the form of increased noise, reduced material quantification accuracy, and image artifacts. Count rate is a function of patient attenuation, beam filtration, scanner geometry, and X-ray technique. PURPOSE: To guide protocol and technology development in the photon counting space, knowledge of clinical count rates spanning the complete range of clinical indications and patient sizes is needed. In this paper, we use clinical data to characterize the range of computed tomography (CT) count rates. METHODS: We retrospectively gathered 1980 patient exams spanning the entire body (head/neck/chest/abdomen/extremity) and sampled 36 951 axial image slices. We assigned the tissue labels air/lung/fat/soft tissue/bone to each voxel for each slice using CT number thresholds. We then modeled four different bowtie filters, 70/80/100/120/140 kV spectra, and a range of mA values. We forward-projected each slice to obtain detector-incident count rates, using the geometry of a GE Revolution Apex scanner. Our analysis divided the detector into thirds: the central one-third, one-third of the detector split into two equal regions adjacent to the central third, and the final one-third divided equally between the outer detector edges. We report the 99th percentile of counts to mimic the upper limits of count rates making passing through a patient as a function of patient water equivalent diameter. We also report the percentage of patient scans, by body region, over different count rate thresholds for all combinations of bowtie and beam energy. RESULTS: For routine exam types, we recorded count rates of approximately 3.5 × 108  counts/mm2 /s in the torso, extremities, and brain. For neck scans, we observed count rates near 6 × 108  counts/mm2 /s. Our simulations of 1000 mA, appropriately mimicking the mA needs for fast pediatric, fast thoracic, and cardiac scanning, resulted in count rates of over 10 × 108  counts/mm2 /s for the torso, extremities, and brain. At 1000 mA, for the neck region, we observed count rates close to 2 × 109  counts/mm2 /s. Importantly, we saw only a small change in maximum count rate needs over patient size, which we attribute to patient mis-positioning with respect to the bowtie filters. As expected, combinations of kV and bowtie filter with higher beam energies and wider/less attenuating bowtie fluence profiles lead to higher count rates relative to lower energies. The 99th-50th percentile count rate changed the most for the torso region, with a maximum variation of 3.9 × 108 to 1.2 × 107  counts/mm2 /s. The head/neck/extremity regions had less than a 50% change in count rate from the 99th to 50th percentiles. CONCLUSIONS: Our results are the first to use a large patient cohort spanning all body regions to characterize count rates in CT. Our results should be useful in helping researchers understand count rates as a function of body region and mA for various combinations of bowtie filter designs and beam energies. Our results indicate clinical rates >1 × 109  counts/mm2 /s, but they do not predict the image quality impact of using a detector with lower characteristic count rates.