Full-dose CT Research Articles

Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.

In our earlier study, we proposed a regional Markov random field type tissue-specific texture prior from previous full-dose computed tomography (FdCT) scan for current low-dose CT (LdCT) imaging, which showed clinical benefits through task-based evaluation. Nevertheless, two assumptions were made for early study. One assumption is that the center pixel has a linear relationship with its nearby neighbors and the other is previous FdCT scans of the same subject are available. To eliminate the two assumptions, we proposed a database assisted end-to-end LdCT reconstruction framework which includes a deep learning texture prior model and a multi-modality feature based candidate selection model. A convolutional neural network-based texture prior is proposed to eliminate the linear relationship assumption. And for scenarios in which the concerned subject has no previous FdCT scans, we propose to select one proper prior candidate from the FdCT database using multi-modality features. Features from three modalities are used including the subjects' physiological factors, the CT scan protocol, and a novel feature named Lung Mark which is deliberately proposed to reflect the z-axial property of human anatomy. Moreover, a majority vote strategy is designed to overcome the noise effect from LdCT scans. Experimental results showed the effectiveness of Lung Mark. The selection model has accuracy of 84% testing on 1,470 images from 49 subjects. The learned texture prior from FdCT database provided reconstruction comparable to the subjects having corresponding FdCT. This study demonstrated the feasibility of bringing clinically relevant textures from available FdCT database to perform Bayesian reconstruction of any current LdCT scan.

DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction.

To reduce the potential risk of radiation to the patient, low-dose computed tomography (LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional images using sinograms with reduced x-ray flux. The LDCT image quality is often degraded by different levels of noise depending on the low-dose protocols. The image quality will be further degraded when the patient has metallic implants, where the image suffers from additional streak artifacts along with further amplified noise levels, thus affecting the medical diagnosis and other CT-related applications. Previous studies mainly focused either on denoising LDCT without considering metallic implants or full-dose CT metal artifact reduction (MAR). Directly applying previous LDCT or MAR approaches to the issue of simultaneous metal artifact reduction and low-dose CT (MARLD) may yield sub-optimal reconstruction results. In this work, we develop a dual-domain under-to-fully-complete progressive restoration network, called DuDoUFNet, for MARLD. Our DuDoUFNet aims to reconstruct images with substantially reduced noise and artifact by progressive sinogram to image domain restoration with a two-stage progressive restoration network design. Our experimental results demonstrate that our method can provide high-quality reconstruction, superior to previous LDCT and MAR methods under various low-dose and metal settings.

Self-supervised inter- and intra-slice correlation learning for low-dose CT image restoration without ground truth

Training a convolutional neural network (CNN) to reduce noise in low-dose CT (LDCT) images typically relies on supervised learning, which requires input–target pairs of noisy LDCT and corresponding full-dose CT (FDCT) images. Although previous approaches have shown promising results in LDCT image denoising, it is difficult to acquire clinical datasets of LDCT-FDCT image pairs, which require additional and unnecessary radiation dose delivery to patients. In this paper, we propose a self-supervised learning approach to training a CNN-based denoiser with LDCT images alone. As a means of self-supervision, the proposed approach searches inter-pixel correlation of LDCT images in z-direction as well as in-plane direction. To regularize the CNN-based denoiser, thicker LDCT slices are used as image priors during the self-supervised training process in our approach. For efficient self-supervised learning, we adopt a two-stage training strategy with offline pretraining and online finetuning. The proposed approach is thoroughly evaluated with public and private clinical LDCT datasets. Both image quality measures and clinical assessments indicate that the self-supervised denoising model simultaneously reduces noise level and restores anatomical information in LDCT images from the images alone. The experimental results also show that our online finetuning scheme can improve the denoising performance of supervised learning models as well as self-supervised learning models at test time.

Submillisievert CT chest for COVID-19 patients in a rural hospital with limited resources

BackgroundThis is a secondary analysis of prospectively acquired data approved by the hospital institutional board committee. We performed a retrospective chart review of 463 patients who underwent a CT Chest for suspected COVID-19 infection between April 1st, 2020, and March 31st, 2021. Patients were grouped based on the CT chest obtained protocol: ultra-low dose or full dose. The likelihood of suspicion of COVID-19 infection was classified on a Likert scale based on the probability of pulmonary involvement. For each group, the sensitivity and specificity of CT were compared to nasopharyngeal swab as standard of reference. The median dose length product and duration of apnea were compared between both groups using two-tailed Mann–Whitney U test. The aim of this study is to share our experience of reducing radiation dose in COVID-19 patients by using an ultra-low dose CT chest protocol on a 16 row multidetector CT scan in a hospital with limited resources.ResultsTwo hundred sixty-nine patients underwent a full dose CT and 194 patients an ultra-low dose CT. In the former group, the median dose length product was 341.11 mGy*cm [Interquartile range (IQR), 239.1–443.2] and the median duration of apnea was 13.29 s [IQR, 10.85–15.73]. In the latter group, the median dose length product was 30.8 mGy*cm [IQR, 28.9–32.7] and median duration of apnea was 8.27 s [IQR, 7.69–8.85]. The sensitivity of the ultra-low dose CT was 91.2% and that of the full dose was 94%.ConclusionA 90% reduction in estimated dose and 38% reduction in apnea duration could be achieved using an ultra-low dose CT chest protocol on a 16-row MDCT without significant loss in the sensitivity of CT to detect COVID-related parenchymal involvement.

Diagnostic Value of Chest CT Images Based on Full Model Iterative Reconstruction Algorithm for Lung Cancer Patients

Objective. To evaluate the value of low-dose CT scanning and full model iteration recombinant technology peripheral lung cancer in the paper using whole model iterative reconstruction algorithm and compare iterative model-wide restructuring, reorganization part of an iterative algorithm, affecting filtered back projection image quality. Method. Fifty-two patients with peripheral lung cancer, all of whom were diagnosed by pathological biopsy, were selected for the study. All patients received three scans of low-dose chest CT, next-low-dose, and low-dose, after which the raw data of three different doses were reconstructed using filtered back-projection, iterative partial algorithm reconstitution, and reconstructed full-model iteration, respectively, and the effect of each algorithm on the processing of chest CT images of peripheral lung cancer at different doses and the diagnosis of the disease were compared after the reconstitution was completed. Results. The average effective radiation dose for the low-dose group was (0.3±0.02) mSv At each dose level, image noise objective recombinant whole iterative model < part of the reorganization of the iterative algorithm < filtered back projection, the difference was significant. In the case of lung lesions, the full-model iterative algorithm has similar evaluation power to the LD-partial iterative algorithm. When a patient’s body mass index (BMI) > 25 kg/m2, the whole model iteration reorganization image quality is reduced, but the lesions-to-noise ratio (SNR) is unaffected. Conclusion. The combination of a very low dose of recombinant iterative model as compared to full-dose low-dose chest CT dose can be reduced to 88% but does not reduce the overall image quality and can show good radiological signs of peripheral lung cancer and not affect BMI patients.

Use of full-dose contrast-enhanced CT for extrahepatic staging using Gallium-68-DOTATATE PET/CT in patients with neuroendocrine tumors.

Studies have demonstrated that positron emission tomography/computed tomography (PET/CT) with Gallium-68 (68Ga)-labeled somatostatin analogues are effective at detecting metastatic disease in neuroendocrine tumors (NET), especially extrahepatic metastases. However, PET in combination with full-dose contrast-enhanced CT (ceCT) exposes patients to higher radiation (~25 mSv). The use of non-contrast-enhanced low-dose CT (ldCT) can reduce radiation to about 10 mSv and may avoid contrast-induced side effects. This study seeks to determine whether ceCT could be omitted from NET assessments. We retrospectively compared the performance of PET/ldCT versus PET/ceCT in 54 patients (26 male, 28 female) who had undergone a 68Ga-DOTATATE PET/CT. The selection criteria were as follows: available ldCT and ceCT, histologically confirmed NET, and follow-up of at least 6 months (median, 12.6 months; range, 6.1-23.2 months). The PET/ldCT and PET/ceCT images were analyzed separately. We reviewed metastases in the lungs, bones, and lymph nodes. The results were compared with the reference standard (clinical follow-up data). The PET/ceCT scans detected 139 true-positive bone lesions compared with 140 lesions detected by the PET/ldCT scans, 106 true-positive lymph node metastases (PET/ceCT) compared with 90 metastases detected by the PET/ldCT scans, and 26 true-positive lung lesions (PET/ceCT) compared with 6 lesions detected by the PET/ldCT scans. The overall lesion-based sensitivity for full-dose PET/ceCT was 97%, specificity 86%, negative predictive value (NPV) 93%, and positive predictive value (PPV) 93%. The overall lesion-based sensitivity for PET/ldCT was 85%, specificity 73%, NPV 72%, and PPV 85%. This study presents the first evidence that ceCT should not be omitted from extrahepatic staging using 68Ga-DOTATATE PET/CT in patients with NET. ceCT alone can be used as a follow-up to reduce radiation exposure when the patient has already undergone PET/ceCT and suffers from non-DOTATATE-avid NET.

Strategies to Improve CT Dose Optimization for Hybrid PET/CT Imaging

Background: To evaluate whether current dose reduction strategies for the CT component of hybrid Positron Emission Tomography—Computed Tomography (PET/CT) systems could reduce patient dose with maintaining adequate image quality for PET/CT studies. Materials and Methods: Literature survey was initially based on the selection of keywords and years of publication to identify potentially relevant articles, then the further search was conducted on the authors and references from these articles. The abstract of each article was first appraised to decide whether the content was relevant to this research question. The articles were classified into five groups: studies on dosimetry, studies on radiation-induced diseases, studies on dose reduction methods for CT-based attenuation correction (CTAC), studies on dose reduction methods for CT localization, and studies on reducing the need for a full-dose diagnostic CT in PET/CT imaging. 58 peer-reviewed articles were selected and appraised and 29 articles were used to compose this literature review. Results: The published nuclear medicine and medical physics literature were reviewed. CT dose contributed 47% - 81% of the total effective dose of a standard PET/CT study and was associated with radiation-induced diseases. The dose reduction techniques were extracted and divided into three categories: reducing the CT dose for attenuation correction (AC) and localization, selectively localizing CT use, and reducing the need for a full-dose diagnostic CT. Conclusion: Three strategies have been demonstrated, with high potential for reducing patient dose while maintaining an adequate CT image quality, used for CTCA localization and diagnosis, respectively.

Optimizing treatment sequencing of chemotherapy for patients with rectal cancer: The KIR randomized phase II trial

BackgroundRandomized studies have shown low compliance to adjuvant chemotherapy in rectal cancer patients receiving preoperative chemotherapy and external beam radiation (CT/EBRT) with total mesorectal excision. We hypothesize that giving neoadjuvant CT before local treatment would improve CT compliance. MethodsBetween 2010–2017, 180 patients were randomized (2:1) to either Arm A (AA) with FOLFOX x6 cycles prior to high dose rate brachytherapy (HDRBT) and surgery plus adjuvant FOLFOX x6 cycles, or Arm B (AB), with neoadjuvant HDRBT with surgery and adjuvant FOLFOX x12 cycles. The primary endpoint was CT compliance to ≥85% of full-dose CT for the first six cycles. Secondary endpoints were ypT0N0, five-year disease free survival (DFS), local control and overall survival (OS). ResultsPatients were randomized to either AA (n = 120, median age (MA) 62 years) or AB (n = 60, MA 63 years). 175/180 patients completed HDRBT as planned (97.2%). In AA, two patients expired during CT; three patients post-randomization received short course EBRT because of progression under CT (n = 2, AA) or personal preference (n = 1, AB). ypT0N0 was 31% in AA and 28% in AB (p = 0.7). CT Compliance was 80% in AA and 53% in AB (p = 0.0002). Acute G3/G4 toxicity was 35.8% in AA and 27.6% in AB (p = 0.23). With a median follow-up of 48.5 months (IQR 33–72), the five-year DFS was 72.3% with AA and 68.3% with AB (p = 0.74), the five-year OS 83.8% for AA and 82.2% for AB (p = 0.53), and the five-year local recurrence was 6.3% for AA and 5.8% for AB (p = 0.71). ConclusionWe confirmed improved compliance to neoadjuvant CT in this study. Although there is no statistical difference in ypT0N0 rate, local recurrence, and DFS between the two arms, a trend towards favourable oncological outcomes is observed.

Diagnostic errors in clinical FDG-PET/CT

PurposeTo determine the frequency, types, and determinants of diagnostic errors in clinical FDG-PET/CT, based on addenda to the original report. Materials and MethodsThis retrospective study included 4,099 consecutive clinical FDG-PET/CT scans with corresponding reports that were made at a tertiary care center in an 18-month period. FDG-PET/CT reports were scrutinized for the presence of an addendum enclosing a diagnostic error. Results90 of 4,099 FDG-PET/CT reports (2.2%) contained an addendum enclosing a diagnostic error. The distribution of perceptual and cognitive errors among these 90 diagnostic errors was 54 (60.0%)/36 (40.0%). On multivariate logistic regression analysis, only low-dose FDG-PET/CT combined with concomitantly acquired and interpreted full-dose contrast-enhanced CT remained as significantly and independently associated with the presence of a diagnostic error, relative to low-dose FDG-PET/CT without concomitantly acquired and interpreted full-dose contrast-enhanced CT (odds ratio: 2.79 [95% confidence interval: 1.61-4.85], P < 0.001). Patient age, gender, hospital status, indication for FDG-PET/CT scanning, single vs. double reading (i.e. two medical imaging specialists), reader experience, and reading by a nuclear medicine physician only vs. reading by both a nuclear medicine physician and a radiologist, were not significantly and independently associated with the presence of a diagnostic error. ConclusionDiagnostic errors in clinical FDG-PET/CT based on addenda to the original report are relatively infrequent, though certainly non-negligible. Perceptual errors are slightly more frequent than cognitive errors. The availability of a concomitantly acquired and interpreted full-dose contrast-enhanced CT seems to increase diagnostic error rate. These data can be used for quality improvement and benchmarking purposes.

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

ObjectivesThe current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.MethodsIn this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE).ResultsThe radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8.ConclusionsThe results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction.Key Points• Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis.• Deep learning–based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%.• Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.

A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images.

Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient's previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This paper aims to remove the constraint of using previous data of the same patient. We investigated the feasibility of extracting the tissue-specific MRF textures from an FdCT database to reconstruct a LdCT image of another patient. This feasibility study was carried out by experiments designed as follows. We constructed a tissue-specific MRF-texture database from 3990 FdCT scan slices of 133 patients who were scheduled for lung nodule biopsy. Each patient had one FdCT scan (120 kVp/100 mAs) and one LdCT scan (120 kVp/20 mAs) prior to biopsy procedure. When reconstructing the LdCT image of one patient among the 133 patients, we ranked the closeness of the MRF-textures from the other 132 patients saved in the database and used them as the a prior knowledge. Then, we evaluated the reconstructed image quality using Haralick texture measures. For any patient within our database, we found more than eighteen patients' FdCT MRF texures can be used without noticeably changing the Haralick texture measures on the lung nodules (to be biopsied). These experimental outcomes indicate it is promising that a sizable FdCT texture database could be used to enhance Bayesian reconstructions of any incoming LdCT scans.

Full Dose CT Database Induced Reconstruction With Nonlocal Means Prior for Ultra-Low-Dose Lung CT: A Preliminary Study

Although Low-dose computed tomography (LDCT) is the most effective way for early lung cancer screening, it's still a challenge to further reduce radiation dose on the premise of ensuring image quality. Penalized weighted least-squares (PWLS) image reconstruction with nonlocal means (NLM) prior has shown excellent performance to improve the image quality for LDCT, especially when the nonlocal weights are calculated from previous full-dose CT (FDCT) image. However, the previous FDCT image of the same patient is not readily available, and registration between the LDCT and FDCT images must be considered because of the scanning misalignment. This paper proposed a new NLM prior model to reconstruct high quality LDCT image without image registering. In order to estimate the nonlocal weights of NLM prior, a database was trained from FDCT images of different patients, from which the patch samples similar to each target patch of the LDCT were extracted. Then the nonlocal weights were determined by the patch samples, and integrated into PWLS reconstruction with the priori information of local structures from FDCT. Experiments with 10mAs LDCT data have shown its superiority in reducing noise, streaking artifacts and preserving structure detail, indicating the potential of further dose reduction in ultra-LDCT lung screening.

1342P - 68Ga-PSMA-PET/CT imaging for locally advanced, recurrent and metastatic adenoid cystic carcinoma and salivary duct carcinoma

Background: Salivary gland cancer (SGC) is a rare cancer with few treatment options. Adenoid cystic carcinoma (ACC) and salivary duct carcinoma (SDC) are major subtypes of SGC which often show immunohistochemical expression of prostate-specific membrane antigen (PSMA). In prostate cancer, PSMA ligands labeled with radioisotopes such as 68Ga or 177Lu, are used for imaging or therapy. The aim of this study was to evaluate 68Ga-PSMA ligand uptake in patients with locally recurrent and/or metastatic ACC and SDC and to establish the diagnostic value of 68Ga-PSMA-PET/CT imaging compared to full-dose CT imaging. Methods:68Ga-PSMA-HBED-CC PET/CT scans were performed in 14 ACC patients and 10 SDC patients including a full-dose CT scan of the neck, chest and abdomen. 68Ga-PSMA ligand uptake was evaluated in local recurrences and metastatic lesions. Detected lesions were compared to CT results. Maximum standardized uptake values (SUVmax) were determined. The study protocol was approved by the local medical ethical committee and a written informed consent was obtained from all patients. Results: Moderate to intense PSMA ligand uptake was observed in local recurrences and distant metastases in all ACC patients and 40% of SDC patients. In addition, PSMA-PET imaging detected additional bone metastases in 2 ACC patients and additional lymph node metastases in 1 ACC patient which were not detected on a full-dose CT scan. In 1 ACC patient a local recurrence of a vulvar ACC was suspected on CT but PSMA-PET imaging was negative, which turned out to be scar tissue, eventually. Conclusions: PSMA-PET imaging showed moderate to intense ligand uptake in local recurrences and distant metastases of all ACC patients and 40% of SDC patients. Based on the high uptake, these patients may be promising candidates for 177Lu-PSMA radioligand therapy in future. Moreover, PSMA-PET imaging has added diagnostic value compared to full-dose CT imaging. Clinical trial identification: NCT03319641. Legal entity responsible for the study: Radboud university medical center, Nijmegen, the Netherlands. Funding: Adenoid Cystic Carcinoma Research Foundation (ACCRF) and the Dutch Salivary Gland Cancer Patient Platform. Disclosure: All authors have declared no conflicts of interest.

Low kV versus dual-energy virtual monoenergetic CT imaging for proven liver lesions: what are the advantages and trade-offs in conspicuity and image quality? A pilot study.

Single-energy low tube potential (SE-LTP) and dual-energy virtual monoenergetic (DE-VM) CT images both increase the conspicuity of hepatic lesions by increasing iodine signal. Our purpose was to compare the conspicuity of proven liver lesions, artifacts, and radiologist preferences in dose-matched SE-LTP and DE-VM images. Thirty-one patients with 72 proven liver lesions (21 benign, 51 malignant) underwent full-dose contrast-enhanced dual-energy CT (DECT). Half-dose images were obtained using single tube reconstruction of the dual-source SE-LTP projection data (80 or 100kV), and by inserting noise into dual-energy projection data, with DE-VM images reconstructed from 40 to 70keV. Three blinded gastrointestinal radiologists evaluated half-dose SE-LTP and DE-VM images, ranking and grading liver lesion conspicuity and diagnostic confidence (4-point scale) on a per-lesion basis. Image quality (noise, artifacts, sharpness) was evaluated, and overall image preference was ranked on per-patient basis. Lesion-to-liver contrast-to-noise ratio (CNR) was compared between techniques. Mean lesion size was 1.5±1.2cm. Across the readers, the mean conspicuity ratings for 40, 45, and 50keV half-dose DE-VM images were superior compared to other half-dose image sets (p<0.0001). Per-lesion diagnostic confidence was similar between half-dose SE-LTP compared to half-dose DE-VM images (p≥0.05; 1.19 vs. 1.24-1.32). However, SE-LTP images had less noise and artifacts and were sharper compared to DE-VM images less than 70keV (p<0.05). On a per-patient basis, radiologists preferred SE-LTP images the most and preferred 40-50keV the least (p<0.0001). Lesion CNR was also higher in SE-LTP images than DE-VM images (p<0.01). For the same applied dose level, liver lesions were more conspicuous using DE-VM compared to SE-LTP; however, SE-LTP images were preferred more than any single DE-VM energy level, likely due to lower noise and artifacts.

Acute pulmonary embolism detection with ventilation/perfusion SPECT combined with full dose CT: What is the best option?

AimTo compare diagnostic accuracy of Ventilation/Perfusion (V/P) single-photon emission computed tomography (SPECT) combined with simultaneous full-dose CT with a hybrid SPECT/CT scanner versus planar ventilation/perfusion (V/P) SPECT and CT angiography (CTA) in patients suspected with acute pulmonary embolism (PE). MethodsBetween 2009 and 2011, consecutive patients suspected of acute PE were referred for V/P SPECT/CT (reviewed board approved study). A contrast agent was administered to patients who had no contraindications. Non-contrast V/P SPECT/CT was performed on the remaining patients. All patients were followed-up for at least 3 months. ResultsA total of 314 patients were available during the study period, with the diagnosis of PE confirmed in 70 (22.29%) of them. The overall population sensitivity and specificity was 90.91% and 92.44%, respectively for V/P SPECT, 80% and 99.15%, respectively, for CTA, and 95.52% and 97.08% for V/P SPECT/CT. SPECT/CT performed better than V/P SPECT (AUC differences=0.0419, P=0.0043, 95% CI; 0.0131–0.0706) and CTA (AUC differences=0.0681, P=0.0208, 95% CI; 0.0103–0.1259)). Comparing imaging modalities when contrast agent could be administered, sensitivity and specificity increased and V/P SPECT/CT was significantly better than CTA (AUC differences=0.0681, P=0.0208, 95% CI; 0.0103–0.1259) and V/P SPECT (AUC differences=0.0659, P=0.0052, 95% CI; 0.0197–0.1121). In case of non-contrast enhancement, there was non-significant increase of specificity. Secondary findings on CT impacted patient management in 14.65% of cases. ConclusionOur study shows that combined V/P SPECT/CT scanning has a higher diagnostic accuracy for detecting acute PE than V/P SPECT and CTA alone. When feasible, V/P SPECT/CT with contrast enhancement is the best option.

Erratum to “Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images” [Mar 16 860-870

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68Ga-DOTATOC PET/CT in Patients with Iodine- and 18F-FDG-Negative Differentiated Thyroid Carcinoma and Elevated Serum Thyroglobulin.

68Ga-DOTATOC PET/CT (CT without contrast, low-dose) was performed on average 6 wk after negative 18F-FDG PET/CT (CT contrast-enhanced, full-dose) in 15 consecutive radioiodine-negative DTC patients with elevated and rising thyroglobulin. Visual assessment of 68Ga-DOTATOC PET/CT images used a 4-point scale for classification of lesions (0, no pathologic findings; 1, benign; 2, equivocal; 3, malignant). PET findings were correlated with the histologic subtype of tumor, levels of serum thyroglobulin, and morphologic findings on full-dose CT and neck ultrasound. Histology or clinical and imaging follow-up served as a reference standard. Analysis was performed on a patient and lesion basis. 68Ga-DOTATOC PET/CT was true-positive in 5 patients (10 tumor lesions) and was false-positive in 1 patient. The rate of positive 68Ga-DOTATOC PET/CT was significantly higher in poorly differentiated/oxyphilic carcinomas (4/4 patients) than in papillary (1/5) or follicular (0/6) tumors. Thyroglobulin levels tended to be higher in patients with tumor localization on 68Ga-DOTATOC PET/CT, but differences were not significant. In 2 of 5 patients with true-positive findings on 68Ga-DOTATOC PET/CT, CT alone but not ultrasound identified 2 of 10 tumor lesions, but in both patients 68Ga-DOTATOC-PET/CT revealed further tumor lesions not detected on CT alone. 68Ga-DOTATOC PET/CT should be considered in the case of negative 18F-FDG PET/CT in radioiodine-negative DTC patients with elevated and rising thyroglobulin. Imaging with 68Ga-DOTATOC appears promising especially in poorly differentiated and oxyphilic subtypes of DTC.

Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images.

Markov random field (MRF) model has been widely employed in edge-preserving regional noise smoothing penalty to reconstruct piece-wise smooth images in the presence of noise, such as in low-dose computed tomography (LdCT). While it preserves edge sharpness, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it may compromise clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodules or colon polyps. This study aims to shift the edge-preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF's neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of muscle, fat, bone, lung, etc. from previous full-dose CT (FdCT) scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of the proposed reconstruction framework, experiments using clinical patient scans were conducted. The experimental outcomes showed a dramatic gain by the a priori knowledge for LdCT image reconstruction using the commonly-used Haralick texture measures. Thus, it is conjectured that the texture-preserving LdCT reconstruction has advantages over the edge-preserving regional smoothing paradigm for texture-specific clinical applications.

Ultra-low dose CT attenuation correction for PET/CT: analysis of sparse view data acquisition and reconstruction algorithms

For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and motion correction, at the expense of increased radiation dose. We aim to minimize the dose of the CT scan, while preserving adequate image quality for the purpose of PET attenuation correction by introducing sparse view CT data acquisition.We investigated sparse view CT acquisition protocols resulting in ultra-low dose CT scans designed for PET attenuation correction. We analyzed the tradeoffs between the number of views and the integrated tube current per view for a given dose using CT and PET simulations of a 3D NCAT phantom with lesions inserted into liver and lung. We simulated seven CT acquisition protocols with {984, 328, 123, 41, 24, 12, 8} views per rotation at a gantry speed of 0.35 s. One standard dose and four ultra-low dose levels, namely, 0.35 mAs, 0.175 mAs, 0.0875 mAs, and 0.043 75 mAs, were investigated. Both the analytical Feldkamp, Davis and Kress (FDK) algorithm and the Model Based Iterative Reconstruction (MBIR) algorithm were used for CT image reconstruction. We also evaluated the impact of sinogram interpolation to estimate the missing projection measurements due to sparse view data acquisition. For MBIR, we used a penalized weighted least squares (PWLS) cost function with an approximate total-variation (TV) regularizing penalty function. We compared a tube pulsing mode and a continuous exposure mode for sparse view data acquisition. Global PET ensemble root-mean-squares-error (RMSE) and local ensemble lesion activity error were used as quantitative evaluation metrics for PET image quality.With sparse view sampling, it is possible to greatly reduce the CT scan dose when it is primarily used for PET attenuation correction with little or no measureable effect on the PET image. For the four ultra-low dose levels simulated, sparse view protocols with 41 and 24 views best balanced the tradeoff between electronic noise and aliasing artifacts. In terms of lesion activity error and ensemble RMSE of the PET images, these two protocols, when combined with MBIR, are able to provide results that are comparable to the baseline full dose CT scan. View interpolation significantly improves the performance of FDK reconstruction but was not necessary for MBIR. With the more technically feasible continuous exposure data acquisition, the CT images show an increase in azimuthal blur compared to tube pulsing. However, this blurring generally does not have a measureable impact on PET reconstructed images.Our simulations demonstrated that ultra-low-dose CT-based attenuation correction can be achieved at dose levels on the order of 0.044 mAs with little impact on PET image quality. Highly sparse 41- or 24- view ultra-low dose CT scans are feasible for PET attenuation correction, providing the best tradeoff between electronic noise and view aliasing artifacts. The continuous exposure acquisition mode could potentially be implemented in current commercially available scanners, thus enabling sparse view data acquisition without requiring x-ray tubes capable of operating in a pulsing mode.

CT pulmonary angiogram with 60% dose reduction: Influence of iterative reconstructions on image quality.

To compare the quality of low-dose CT images with sinogram affirmed iterative reconstruction (SAFIRE), and full-dose CT with filtered back projection reconstructions (FBP). Fifty pulmonary CT performed by a dual-source technique (120kVp; 110mAs) with (a) the same energy in both tubes, and (b) the distribution of reference mAs with 40% in tube A (44mAs) and 60% in tube B (66mAs). Each acquisition allowed reconstruction of: (a) full-dose images (with both tubes) with FBP reconstructions (group 1); and (b) low-dose images (from tube A) reconstructed with SAFIRE (group 2). Group 2 images presented: (a) a significant objective reduction in noise measured in the trachea on mediastinal (16.04±5.66 vs 17.66±5.84) (P=0.0284) and pulmonary (29.77±6.79 vs 37.96±9.03) (P<0.0001) images; (b) a similar subjective perception of noise and overall image quality (P=1), which was considered to be excellent in 66% (33/50) of the cases, with no influence on the detection of elementary pulmonary lesions of infiltration (98.4%; 95% CI=[96.9%-99.9%]). Despite a 60% reduction in radiation dose, the image quality with iterative reconstruction is objectively better and subjectively similar to full-dose FBP images.