Parameter map error due to normal noise and aliasing artifacts in MR fingerprinting.

PURPOSE: To introduce a quantitative tool that enables rapid forecasting of T1 and T2 parameter map errors due to normal and aliasing noise as a function of the MR fingerprinting (MRF) sequence, which can be used in sequence optimization. THEORY AND METHODS: The variances of normal noise and aliasing artifacts in the collected signal are related to the variances in T1 and T2 maps through derived quality factors. This analytical result is tested against the results of a Monte-Carlo approach for analyzing MRF sequence encoding capability in the presence of aliasing noise, and verified with phantom experiments at 3 T. To further show the utility of our approach, our quality factors are used to find efficient MRF sequences for fewer repetitions. RESULTS: Experimental results verify the ability of our quality factors to rapidly assess the efficiency of an MRF sequence in the presence of both normal and aliasing noise. Quality factor assessment of MRF sequences is in agreement with the results of a Monte-Carlo approach. Analysis of MRF parameter map errors from phantom experiments is consistent with the derived quality factors, with T1 (T2 ) data yielding goodness of fit R2 ≥ 0.92 (0.80). In phantom and in vivo experiments, the efficient pulse sequence, determined through quality factor maximization, led to comparable or improved accuracy and precision relative to a longer sequence, demonstrating quality factor utility in MRF sequence design. CONCLUSION: The here introduced quality factor framework allows for rapid analysis and optimization of MRF sequence design through T1 and T2 error forecasting.

Channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study.

BACKGROUND: Objective and quantitative evaluation for low-contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d'). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d' with only 1-3 repeat scans. PURPOSE: In this work, we aim to validate the repeatability of the proposed CHO-based low-contrast evaluation on four scanner models using the ACR CT accreditation phantom. METHODS: The repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low-contrast object sizes (4-6 mm). The CHO was also applied to images created using deep learning-based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon-counting-detector (PCD)-CT. The repeatability was evaluated by the probability that a measurement would fall within the ±15% tolerance (P<15% ). RESULTS: With the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non-overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%-99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%-99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%-99.998% on scanner B. P<15% were 96.5%-97.2% for the two deep learning reconstructions and 97.0%-99.97% for the four scan/reconstruction modes on the PCD-CT. CONCLUSION: The CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the ±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning-based reconstruction and various scan/reconstruction modes on a PCD-CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

Low-contrast detectability of photon-counting-detector CT at different scan modes and image types in comparison with energy-integrating-detector CT.

Purpose: We aim to compare the low-contrast detectability of a clinical whole-body photon-counting-detector (PCD)-CT at different scan modes and image types with an energy-integrating-detector (EID)-CT. Approach: We used a channelized Hotelling observer (CHO) previously optimized for quality control purposes. An American College of Radiology CT accreditation phantom was scanned on both PCD-CT and EID-CT with 10 phantom positionings. For PCD-CT, images were generated using two scan modes, standard resolution (SR) and ultra-high-resolution (UHR); two image types, virtual monochromatic images at 70 keV and low-energy threshold (T3D); both filtered-back-projection (FBP) and iterative reconstruction (IR) reconstruction methods; and three reconstruction kernels. For each positioning, three repeated scans were acquired for each scan mode, image type, and CTDIvol of 6, 12, and 24 mGy. For EID-CT, images acquired from scans (10 positionings × 3 repeats × 3 doses) were reconstructed using the closest counterpart FBP and IR kernels. CHO was applied to calculate the index of detectability (d') on both scanners. Results: With the smooth Br44 kernel, the d' of UHR was mostly comparable with that of the SR mode (difference: -11.4% to 8.3%, p=0.020 to 0.956), and the T3D images had a higher d' (difference: 0.7% to 25.6%) than 70 keV images on PCD-CT. Compared with the EID-CT, UHR-T3D of PCD-CT had non-inferior d' (difference: -2.7% to 12.9%) with IR and non-superior d' (difference: 0.8% to 11.2%) with FBP using the Br44 kernel. PCD-CT produced higher d' than EID-CT by 61.8% to 247.1% with the sharper reconstruction kernels. Conclusions: The comparison between PCD-CT and EID-CT was significantly influenced by the reconstruction method and kernel. With a smooth kernel that is typically used in low-contrast detection tasks, the PCD-CT demonstrated low-contrast detectability that was comparable to EID-CT with IR and showed no superiority when using FBP. With the use of sharper kernels, the PCD-CT significantly outperformed EID-CT in low-contrast detectability.

Automated Web-based Software for CT Quality Control Testing of Low-contrast Detectability using Model Observers.

The Channelized Hotelling observer (CHO) is well correlated with human observer performance in many CT detection/classification tasks but has not been widely adopted in routine CT quality control and performance evaluation, mainly because of the lack of an easily available, efficient, and validated software tool. We developed a highly automated solution - CT image quality evaluation and Protocol Optimization (CTPro), a web-based software platform that includes CHO and other traditional image quality assessment tools such as modulation transfer function and noise power spectrum. This tool can allow easy access to the CHO for both the research and clinical community and enable efficient, accurate image quality evaluation without the need of installing additional software. Its application was demonstrated by comparing the low-contrast detectability on a clinical photon-counting-detector (PCD)-CT with a traditional energy-integrating-detector (EID)-CT, which showed UHR-T3D had 6.2% higher d' than EID-CT with IR (p = 0.047) and 4.1% lower d' without IR (p = 0.122).

Accurate and efficient measurement of channelized Hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom.

BACKGROUND: Current CT quality control (QC) for low-contrast detectability relies on visual inspection and measurement of contrast-to-noise ratio (CNR). However, CNR numbers become unreliable when it comes to nonlinear methods, such as iterative reconstruction (IR) and deep-learning-based techniques. Image quality metrics using channelized Hotelling observer (CHO) have been validated to be well correlated with human observer performance on phantom-based and patient-based tasks, but it has not been widely used in routine CT QC mainly because the CHO calculation typically requires a large number of repeated scans in order to provide accurate and precise estimate of index of detectability (d'). PURPOSE: The main goal of this work is to optimize channel filters and other CHO parameters and accurately estimate the low-contrast detectability with minimum number of repeated scans for the widely used American College of Radiology (ACR) CT accreditation phantom so that it can become practically feasible for routine CT QC tests. METHODS: To provide a converged d' value, an ACR phantom was repeatedly scanned 100 times at three dose levels (24, 12, and 6 mGy). Images were reconstructed with two kernels (FBP Br44 and IR Br44-3). d' as a function of number of repeated scans was determined for different number of background regions of interest (ROIs), different number of low-contrast objects, different number of slices per each object, and different channel filter options. A reference d' was established using the optimized CHO setting, and the bias of d' was quantified using the d' calculated from all 100 repeated scans. The variation of d' at each condition was estimated using a resampling method combining random subsampling among 100 repeated scans and bootstrapping of the ensembles of signal and background ROIs. RESULTS: Optimized parameters in CHO calculation were determined: two background ROIs per object, four objects per low-contrast object size, nine non-overlapping slices per object, and a 4-channel Gabor filter. The bias and uncertainty were estimated at different numbers of repeated scans using these parameters. When only one single scan was used in the CHO calculation, the bias of d' was below 6.2% and the uncertainty 15.6-19.6% for the 6, 5, and 4 mm objects, while with three repeated scans the bias was below 2.0% and uncertainty 8.7-10.9% for the three object sizes. CONCLUSION: With optimized parameter settings in CHO, efficient and accurate measurement of low-contrast detectability on the commonly used ACR phantom becomes feasible, which could potentially lead to adoption of CHO-based low-contrast evaluation in routine QC tests.

Evaluation of Low-contrast Detectability of Photon-Counting-Detector CT Using Channelized Hotelling Observer and an ACR Accreditation Phantom.

The purpose of this work is to evaluate the low-contrast detectability on a clinical whole-body photon-counting-detector (PCD)-CT scanner and compare it with an energy-integrating-detector (EID) CT scanner, using an efficient Channelized Hotelling observer (CHO)-based method previously developed and optimized on the American College of Radiology (ACR) CT accreditation phantom for routine quality control (QC) purpose. The low-contrast module of an ACR CT phantom was scanned on both the PCD-CT and EID-CT scanners, each with 10 different positionings. For PCD-CT, data were acquired at 120 kV with two major scan modes, standard resolution (SR) (collimation: 144×0.4 mm) and ultra-high-resolution (UHR) (120×0.2 mm). Images were reconstructed with two major modes: virtual monochromatic energy at 70 keV and low-energy threshold (T3D), each with filtered-backprojection (Br44) and iterative reconstruction (Br44-3) kernels. For each positioning, 3 repeated scans were acquired for each scan mode at a fixed radiation dose setting (CTDIvol = 12 mGy). For EID-CT, scans (10 positionings × 3 repeated scans) were performed at a matched CTDIvol, and images were reconstructed using the same kernels with FBP and IR. A recently developed CHO-based method dedicated for QC of low-contrast performance on the ACR phantom was applied to calculate the low-contrast detectability (d') for each scan and reconstruction condition. Results showed that there was no significant difference in low-contrast detectability (d') between the UHR mode and SR mode (p = 0.360-0.942), and the T3D reconstruction resulted in 7.7%-14.6% higher d' than 70keV (p < 0.0016). Similar detectability levels were observed on PCD-CT and EID-CT. The PCD-CT: UHR-T3D had 6.2% higher d' than EID-CT with IR (p = 0.047) and 4.1% lower d' without IR (p = 0.122).

Improvements in dose efficiency with high resolution scan modes in photon counting CT.

For the detection of very small objects, high resolution detectors are expected to provide higher dose efficiency. We assessed this impact of increased resolution on a clinical photon counting detector CT (PCD-CT) by comparing its detectability in high resolution and standard resolution (with 2×2 binning and larger focal spot) modes. A 50µm-thin metal wire was placed in a thorax phantom and scanned in both modes at three exposure levels (12, 15, and 18 mAs); acquired data were reconstructed with three reconstruction kernels (Br40, Br68, and Br76, from smooth to sharp). A scanning nonprewhitening model observer searched for the wire location within each slice independently. Detection performance was quantified as area under the exponential transform of the free response ROC curve. The high-resolution mode had the mean AUCs at 18 mAs of 0.45, 0.49, and 0.65 for Br40, Br68, and Br76, respectively, which were 2 times, 3.6 times, and 4.6 times those of the standard resolution mode. The high-resolution mode achieved greater AUC at 12 mAs than the standard resolution mode at 18 mAs for every reconstruction kernel, but improvements were larger at sharper kernels. The results are consistent with the greater suppression of noise aliasing expected at higher frequencies with high resolution CT. This work illustrates that PCD-CT can provide large dose efficiency gains for detection tasks of small, high contrast lesions.

Efficient Evaluation of Low-contrast Detectability of Deep-CNN-based CT Reconstruction Using Channelized Hotelling Observer on the ACR Accreditation Phantom.

As deep-learning-based denoising and reconstruction methods are gaining more popularity in clinical CT, it is of vital importance that these new algorithms undergo rigorous and objective image quality assessment beyond traditional metrics to ensure diagnostic information is not sacrificed. Channelized Hotelling observer (CHO), which has been shown to be well correlated with human observer performance in many clinical CT tasks, has a great potential to become the method of choice for objective image quality assessment for these non-linear methods. However, practical use of CHO beyond research labs have been quite limited, mostly due to the strict requirement on a large number of repeated scans to ensure sufficient accuracy and precision in CHO computation and the lack of efficient and widely acceptable phantom-based method. In our previous work, we developed an efficient CHO model observer for accurate and precise measurement of low-contrast detectability with only 1-3 repeated scans on the most widely used ACR accreditation phantom. In this work, we applied this optimized CHO model observer to evaluating the low-contrast detectability of a deep learning-based reconstruction (DLIR) equipped on a GE Revolution scanner. The commercially available DLIR reconstruction method showed consistent increase in low-contrast detectability over the FBP and the IR method at routine dose levels, which suggests potential dose reduction to the FBP reconstruction by up to 27.5%.

Nano-selenium and Macleaya cordata Extracts Improved Immune Function and Reduced Oxidative Damage of Sows and IUGR Piglets After Heat Stress of Sows in Late Gestation.

To investigate the effects of nano-selenium (nano-Se) and Macleaya cordata extracts (MCE) on immune function and oxidative damage of sows and intrauterine growth retardation (IUGR) piglets exposed to heat stress (HS) in large-scale farms, a 2 × 2 factorial design was adopted in this test, and the two factors were nano-Se (0, 0.50 mg/kg) and MCE (0, 500 mg/kg). A total of 80 sows ([Landrace × Yorkshire] × Duroc, parity 2) were used in a 25-day trial from day 90 of gestation to delivery with 20 replications per group and 1 sow per replication. The dietary treatments of sows were as follows: (1) CON group, basic diet (0.30 mg/kg added Se, sodium selenite); (2) Nano-Se group, basic diet (0.00 mg/kg added Se) + 0.50 mg/kg added nano-Se; (3) MCE group, basic diet (0.00 mg/kg added Se) + 500 mg/kg added MCE; and (4) Combined group, basic diet (0.00 mg/kg added Se) + 0.50 mg/kg added nano-Se and 500 mg/kg added MCE. The activities of serum SOD, CAT, and GSH-Px of sows and IUGR piglets were significantly increased in MCE group and combined group, and the MDA content was extremely decreased. There were extreme differences in serum IgG level of sows and IUGR piglets, colostrum, and serum IgM level of IUGR piglets in MCE group and combined group compared with CON group. Maternal combined diets increased greatly the levels of serum IL-10 and IFN-γ of sows and IUGR piglets, and decreased extremely the contents of serum IL-1ß and TNF-α. MCE alone or combination with nano-Se in sow diets decreased greatly mRNA level of Hsp70 and increased mRNA level of Hsp27 in sows and IUGR piglets. In conclusion, nano-Se and/or MCE can be added to sow diets for the amelioration of HS-induced oxidative damage through improving immune function.

A Web-Based Software Platform for Efficient and Quantitative CT Image Quality Assessment and Protocol Optimization.

Channelized Hotelling observer (CHO), which has been shown to be well correlated with human observer performance in many clinical CT tasks, has a great potential to become the method of choice for objective image quality assessment. However, the use of CHO in clinical CT is still quite limited, mainly due to its complexity in measurement and calculation in practice, and the lack of access to an efficient and validated software tool for most clinical users. In this work, a web-based software platform for CT image quality assessment and protocol optimization (CTPro) was introduced. A validated CHO tool, along with other common image quality assessment tools, was made readily accessible through this web platform for clinical users and researchers without the need of installing additional software. An example of its application to evaluation of convolutional-neural-network (CNN)-based denoising was demonstrated.

A high-dynamic-range digital RF-over-fiber link for MRI receive coils using delta-sigma modulation.

Coaxial cables commonly used to connect radio-frequency (RF) coil arrays with the control console of an MRI scanner are susceptible to electromagnetic coupling. As the number of RF channels increases, such coupling could result in severe heating and pose a safety concern. Non-conductive transmission solutions based on fiber-optic cables are considered to be one of the alternatives but are limited by the high dynamic range (>80 dB) of typical MRI signals. A new digital fiber-optic transmission system based on delta-sigma modulation (DSM) is developed to address this problem. A DSM-based optical link is prototyped using off-the-shelf components and bench-tested at different signal oversampling rates (OSRs). An end-to-end dynamic range (DR) of 81 dB, which is sufficient for typical MRI signals, is obtained over a bandwidth of 200 kHz, which corresponds to OSR = 50. A fully integrated custom fourth-order continuous-time DSM is designed in 180 nm CMOS technology to enable transmission of full-bandwidth MRI signals (up to 1 MHz) with an adequate DR. Initial electrical test results from this custom chip are also presented.