Yu, Lifeng

Purpose To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. Acquisition and Validation Methods The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low‐dose noncontrast chest scans acquired to screen high‐risk patients for pulmonary nodules, and contrast‐enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise‐insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. Data Format and Usage Notes Projection datasets were converted into the previously developed DICOM‐CT‐PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM‐CT‐PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive ( https://doi.org/10.7937/9npb‐2637 ). Potential Applications This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task‐based diagnostic performance.

Background Low tube potential-high tube current computed tomography (CT) imaging allows reduction in iodine-based contrast dose and may extend the benefit of routine contrast-enhanced CT exams to patients at risk of nephrotoxicity. Purpose To determine the ability of an iodine contrast reduction algorithm to maintain diagnostic image quality for contrast-enhanced abdominal CT. Material and Methods CT exams with iodine contrast reduction were prescribed for patients at risk for renal dysfunction. The iodine contrast reduction algorithm combines weight-based contrast volume reduction with patient width-based low tube potential selection and bolus-tracking. Control exams with routine iodine dose were selected based on weight, width, and scan protocol. Three radiologists evaluated image quality and diagnostic confidence using a 4-point scale (<2 acceptable). Another radiologist assessed contrast reduction indications and measured portal vein and liver contrast-to-noise ratios. Results Forty-six contrast reduction algorithm and control exams were compared (mean creatinine 1.6 vs. 1.2 mg/dL, P ≤ 0.0001). Thirty-nine contrast reduction patients had an eGFR <60 mL/min/1.73m 2 and 15 had single or transplanted kidney. Mean iodine contrast dose was lower in the contrast reduction group (20.9 vs. 39.4 g/mL, P < 0.0001). Diagnostic confidence was rated as acceptable in 95% (131/138) of contrast reduction and 100% of control exams (1.18–1.28 vs. 1.02–1.13, respectively; P > 0.06). Liver attenuation and contrast-to-noise ratio (CNR) were similar ( P = 0.08), but portal vein attenuation and CNR were lower with contrast-reduction (mean 176 vs. 198 HU, P = 0.02; 13 vs. 16, P = 0.0002). Conclusion This size-based contrast reduction algorithm using low kV and bolus tracking reduced iodine contrast dose by 50%, while achieving acceptable image quality in 95% of exams.

PurposeFiltering measured projections with a particular convolutional kernel is an essential step in analytic reconstruction of computed tomography (CT) images. A tradeoff between noise and spatial resolution exists for different choices of reconstruction kernel. In a clinical setting, this often requires producing multiple images reconstructed with different kernels for a single CT exam, which increases the burden of computation, networking, archival, and reading. We address this problem by training a deep convolutional neural network (CNN) to synthesize multiple input images into a single output image which exhibits low noise while also preserving features in images reconstructed with the sharpest kernels.MethodsA CNN architecture consisting of repeated blocks of residual units containing a total of 20 convolutional layers was used to combine features. The CNN inputs consisted of two images produced with different reconstruction kernels, one smooth and one sharp, which were stacked in the channel dimension. The network was trained using supervised learning with both full‐dose and simulated quarter‐dose abdominal CT images. After training, the performance was evaluated using a reserved set of full‐dose scans that were not used for network optimization. Noise reduction performance was measured by comparing root mean square (RMS) measurements in uniform regions. Spatial resolution was compared using line profiles of anatomic features.ResultsFor the regions tested, the synthetic images feature noise levels slightly below those of the smooth input images, while maintaining the resolution of anatomic details found in the sharp input images.ConclusionsA deep CNN can be used combine features from CT images reconstructed with different kernels to produce a single synthesized image series that exhibits both low noise and high spatial resolution. This approach has implications for improving image quality, reducing radiation dose, and simplifying the clinical workflow for CT imaging.

PurposeTo evaluate the quantitative accuracy and dose efficiency of simultaneous imaging of two contrast agents using dual‐energy computed tomography (DECT), two imaging tasks each representing one potential clinical application were investigated in a phantom study: biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth.MethodsTo separate and quantify mixtures of two contrast agents using a single DECT scan, mixed iodine and gadolinium samples were prepared with the contrast enhancement values corresponding to the late arterial (iodine) and the portal‐venous (gadolinium) phase for biphasic liver imaging. Mixed iodine and bismuth samples were prepared mimicking the arterial (iodine) and the enteric (bismuth) enhancement for small bowel imaging. For comparison to the reference condition of performing two single‐energy CT (SECT) scans, contrast samples were prepared separately to mimic separate scans in the arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water tank and scanned using a third‐generation dual‐source DECT scanner with three tube potential pairs: 80/Sn150, 90/Sn150, and 100/Sn150 kV, all with default dose partitioning between two x‐ray beams to acquire DECT data. The same scanner operated in a single‐energy mode acquired SECT data (120 kV). Total radiation dose (CTDIvol) was matched for the single‐scan DECT and the two‐scan SECT protocols. The DECT protocol was followed by a generic image‐based three‐material decomposition method to determine the material‐specific images, based on which concentrations of each basis material were quantified and noise levels were measured. To compare with the SECT images directly acquired with the SECT protocol, the concentration values in each contrast‐specific image were converted to CT numbers at 120 kV (i.e., virtual SECT (vSECT) images). The noise level and noise power spectra differences between the SECT and vSECT images were compared to evaluate the dose efficiency of the single‐scan DECT protocol. The impact of dose partitioning in the DECT protocol on quantitative dual‐contrast imaging performance was also studied.ResultsFor each imaging task, contrast materials were accurately quantified against the nominal concentrations using the DECT data with strong correlation (R2 ≥ 0.98 for both imaging tasks). Compared to the SECT protocol, the DECT protocol was not dose efficient. With the optimal x‐ray tube potential pair 80/Sn150 kV, the noise level in vSECT images increased by 401%/488% (arterial/portal‐venous) for the biphasic liver imaging task and by 10%/41% (arterial/enteric) for the small bowel imaging task compared to that in SECT images. The corresponding radiation dose increase is 2410%/3357% for the biphasic liver imaging task and 21%/99% for the small bowel imaging task, respectively, to achieve the same noise as that in SECT images. This could be improved by adjusting the dose partitioning in DECT.ConclusionsDECT can be used to simultaneously separate and quantify two contrast materials. However, compared to a two‐scan SECT protocol, much higher radiation dose is needed in a single‐scan DECT protocol to achieve the same image noise, especially for tasks involving the dual contrast of iodine and gadolinium.

PurposeThe aim of this study was to demonstrate that a low helical pitch causes increased photon starvation artifacts at ultra‐low‐dose CT.MethodsA cylindrical water phantom with a diameter of 30 cm was scanned on two different generation CT scanners: a 64‐slice scanner (Sensation 64, Siemens Healthcare) and a 192‐slice scanner (Somatom Force, Siemens Healthcare) at multiple effective mAs levels (mAs/pitch = 200, 100, 50, 25, and 12). The corresponding CTDIvol values were 4.1, 2.0, 1.0, 0.5 mGy, on the 64‐slice scanner and 3.8, 1.9, 1.0, 0.5 mGy on the 192‐slice scanner, for the selected effective mAs values. For each dose setting, the scan was repeated at four helical pitches: 1.2, 0.9, 0.6, and the lowest achievable pitch on each scanner. The tube current was automatically adjusted by the scanner so that the effective mAs, and thus CTDIvol, were kept the same for different pitches. All CT data sets were reconstructed with a slice thickness of 3mm and a medium smooth kernel. Images acquired at the same dose level but different helical pitches were visually inspected to assess photon starvation artifacts and noise levels.ResultsAt the same radiation dose, image noise increased with the decreasing helical pitch. The increase was more severe on the old‐generation 64‐slice scanner. Photon starvation artifacts were evident at 200 effective mAs on the 64‐slice scanner at 80 kV. On the 192‐slice scanner there was no visible photon starvation artifacts at both 200 and 50 effective mAs (CTDIvol = 4.1 mGy and 1.0 mGy, respectively); nor was there a visible impact from the lower helical pitch. Only when the dose was lowered to be extremely low (~0.26 mGy, achievable at 70 kV), did photon starvation artifacts become evident.ConclusionsA low helical pitch may increase image noise and photon starvation artifacts compared to a higher pitch for the same dose level, particularly at ultra‐low dose CT.