Correcting statistical CT number biases without access to raw detector counts: Applications to high spatial resolution photon counting CT imaging

BACKGROUND

Due to the nonlinear nature of the logarithmic operation and the stochastic nature of photon counts (N), sinogram data of photon counting detector CT (PCD-CT) are intrinsically biased, which leads to statistical CT number biases. When raw counts are available, nearly unbiased statistical estimators for projection data were developed recently to address the CT number bias issue. However, for most clinical PCD-CT systems, users' access to raw detector counts is limited. Therefore, it remains a challenge for end users to address the CT number bias issue in clinical applications.

PURPOSE

To develop methods to correct statistical biases in PCD-CT without requiring access to raw PCD counts.

METHODS

(1) The sample variance of air-only post-log sinograms was used to estimate air-only detector counts,N ¯ 0 $\bar{N}_0$ . (2) If the post-log sinogram data, y, is available, then N of each detector pixel was estimated usingN = N ¯ 0 e - y $N = \bar{N}_0 \, \mathrm{e}^{-y}$ . Once N was estimated, a closed-form analytical bias correction was applied to the sinogram. (3) If a patient's post-log sinogram data are not archived, a forward projection of the bias-contaminated CT image was used to perform a first-order bias correction. Both the proposed sinogram domain- and image domain-based bias correction methods were validated using experimental PCD-CT data.

RESULTS

Experimental results demonstrated that both sinogram domain- and image domain-based bias correction methods enabled reduced-dose PCD-CT images to match the CT numbers of reference-standard images within [-5, 5] HU. In contrast, uncorrected reduced-dose PCD-CT images demonstrated biases ranging from -25 to 55 HU, depending on the material. No increase in image noise or spatial resolution degradation was observed using the proposed methods.

CONCLUSIONS

CT number bias issues can be effectively addressed using the proposed sinogram or image domain method in PCD-CT, allowing PCD-CT acquired at different radiation dose levels to have consistent CT numbers desired for quantitative imaging.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence

Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non-COVID-19 pneumonia on chest radiographs. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. Materials and Methods In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non-COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non-COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. Conclusion CV19-Net was able to differentiate coronavirus disease 2019-related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.

Abstract 55: Comparison of Sequential Multi-Detector CT and Cone-Beam CT Perfusion Maps in 54 Subjects With an Acute Ischemic Stroke

Introduction: Time from diagnostic imaging to groin puncture highly correlates with outcome and often accounts for delays between hospital arrival and EVT. Our study comparing image quality and information content of MDCTP and CBCTP provides feasibility data for selected AIS patients to go straight to the angio-suite for comprehensive imaging and treatment.

Methods: AIS patients eligible for EVT underwent MDCTP, then a CBCTP study on arrival in angio-suite. Of 939 admitted June 2017-April 2019, 226 (24%) received EVT. Of these 54 (35%) were enrolled to receive additional CBCTP imaging. Inability to obtain consent and co-morbidities were major causes for non-enrollment. Times from the start of MDCTP to angio-suite and from angio-suite arrival to first arterial image were recorded. Acquired CBCTP data were reconstructed and processed with an in-house toolbox. MDCTP and CBCTP data were matched for slice thickness and angulation and were processed using RAPID CTP (iSchemaView, Inc.). The rCBF, rCBV, MTT, tMAX maps were randomized to generate 3 unique evaluation sets. 3 neuroradiologists scored diagnostic image quality, artifacts, mismatch pattern detection and EVT indication using 5-point Likert scales. Stroke laterality was compared with the clinical standard for diagnostic accuracy.

Results: Accuracies for stroke diagnosis are 97% [95%, 97%] with MDCTP and 92% [90%, 95%] with CBCTP. Cohen’s Kappa between observers is 0.90 for MDCTP-based diagnosis and 0.89 for CBCTP-based diagnosis. Scores of CBCTP to make the stroke diagnosis, detect mismatch pattern, and make treatment decision were non-inferior to corresponding scores for MDCTP (alpha=0.05) within 10% of the whole score range. Subjective scores of MDCTP for image quality and artifacts were slightly superior to those of CBCTP (1.8 vs. 2.3, p<0.01).

Conclusions: In this study, a direct to angio-suite workflow provided non-inferior perfusion imaging for AIS patient triage while saving nearly one hour per patient.

Use of the reciprocating gait orthosis in myelodysplasia

There are advantages to an upright posture and ambulation in pediatric myelodysplasia patients. The reciprocating gait orthosis (RGO) is a useful tool to enable this activity in selected individuals. We evaluated the long-term usage, pitfalls, and contraindications of this orthosis. Mean daily usage was 6.9 h/day, requiring an average of 10 min to don or doff; all patients required assistance. Obesity, advanced age, lack of patient or family motivation, scoliosis, and spasticity were significant negative factors in long-term usage of the RGO. Good upper extremity strength, trunk balance, previous standing or walking, and active hip flexion were important positive variables. Eleven of 21 patients did not persist with long-term usage of this orthosis, averaging 25.8 months of usage. An energy study was performed on three patients, comparing the efficiency of reciprocating gait and swing-through gait. All three were more energy efficient and two were faster with the swing-through gait but each preferred the reciprocating pattern. Care should be taken when selecting patients for RGO usage because not all children with myelomeningocele are able to function effectively with this orthosis.

Evaluation of the pendant oxygen-conserving nasal cannula during exercise

There is much recent evidence that patients with chronic pulmonary disease who are hypoxemic benefit from continuous therapy with oxygen. These benefits include reduction in symptoms of cor pulmonale, reduction in mortality, and improvement in quality of life. Oxygen therapy is very expensive, and steady-flow delivery of oxygen is wasteful, since almost the entire benefit of the oxygen presented to the patient occurs at the very beginning of inspiration. We previously described a conserver nasal cannula (CNC) which stores oxygen during exhalation for delivery during subsequent inspirations. The CNC achieves adequate arterial oxygen saturation (SaO2) at one fourth to one half of the flow in liters of steady-flow oxygen delivery. Because some patients found the mustache configuration objectionable, a pendant nasal cannula (PNC) was designed, displacing the reservoir off of the face and onto the anterior wall of the chest. While both cannulas require some breathing by nose to function, the PNC is more esthetically acceptable. No studies with exercise have been reported using the PNC. We evaluated the PNC during treadmill exercise in ten subjects with chronic obstructive pulmonary disease and hypoxemia on exercise. We compared the PNC with steady-flow oxygen during steady level treadmill walking sufficient to cause oxygen desaturation while breathing room air at oxygen presentations of 0.5 through 3.0 L/min. At comparable workloads the SaO2 achieved by PNC required one third of the oxygen flow required by steady-flow oxygen to achieve an equivalent SaO2. These differences were statistically significant (p less than 0.01). We conclude that the PNC provides effective delivery of oxygen during exercise, as well as at rest, while minimizing oxygen flow rate and thus substantially reducing the economic burden normally associated with supplemental oxygen delivery.