The Small Pixel Effect in Ultra-High-Resolution Photon-Counting CT of the Lumbar Spine

OBJECTIVES

Image acquisition in ultra-high-resolution (UHR) scan mode does not impose a dose penalty in photon-counting CT (PCCT). This study aims to investigate the dose saving potential of using UHR instead of standard-resolution PCCT for lumbar spine imaging.

MATERIALS AND METHODS

Eight cadaveric specimens were examined with 7 dose levels (5-35 mGy) each in UHR (120 × 0.2 mm) and standard-resolution acquisition mode (144 × 0.4 mm) on a first-generation PCCT scanner. The UHR images were reconstructed with 3 dedicated bone kernels (Br68 [spatial frequency at 10% of the modulation transfer function 14.5 line pairs/cm], Br76 [21.0], and Br84 [27.9]), standard-resolution images with Br68 and Br76. Using automatic segmentation, contrast-to-noise ratios (CNRs) were established for lumbar vertebrae and psoas muscle tissue. In addition, image quality was assessed subjectively by 19 independent readers (15 radiologists, 4 surgeons) using a browser-based forced choice comparison tool totaling 16,974 performed pairwise tests. Pearson's correlation coefficient ( r ) was used to analyze the relationship between CNR and subjective image quality rankings, and Kendall W was calculated to assess interrater agreement.

RESULTS

Irrespective of radiation exposure level, CNR was higher in UHR datasets than in standard-resolution images postprocessed with the same reconstruction parameters. The use of sharper convolution kernels entailed lower CNR but higher subjective image quality depending on radiation dose. Subjective assessment revealed high interrater agreement ( W = 0.86; P < 0.001) with UHR images being preferred by readers in the majority of comparisons on each dose level. Substantial correlation was ascertained between CNR and the subjective image quality ranking (all r 's ≥ 0.95; P < 0.001).

CONCLUSIONS

In PCCT of the lumbar spine, UHR mode's smaller pixel size facilitates a considerable CNR increase over standard-resolution imaging, which can either be used for dose reduction or higher spatial resolution depending on the selected convolution kernel.

Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI

BACKGROUND

For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising.

PURPOSE

To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI.

STUDY TYPE

Retrospective.

POPULATION

133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2 ).

ASSESSMENT

DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale.

STATISTICAL TESTS

Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant.

RESULTS

Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error.

DATA CONCLUSION

Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 1.

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

OBJECTIVES

Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies.

METHODS

In this study, 100 lung cancer patients underwent a contrast-enhanced ¹⁸F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional "hand-crafted" radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii).

RESULTS

In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865-0.878), SBS 35.8 (34.2-37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively).

CONCLUSION

Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.

Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study

Background

Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting.

Methods

The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model's performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients.

Results

The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92-0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996-0.999) with low bias and high precision (-0.2%±4% for axial images and 0.5%±4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of -0.5%±3%. For the external dataset, the automated TKV demonstrated bias and precision of -1%±7%.

Conclusions

Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible.Clinical Trial registry name and registration number: The German ADPKD Tolvaptan Treatment Registry (AD[H]PKD), NCT02497521.

AutoRadiomics: A Framework for Reproducible Radiomics Research

Purpose

Machine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities.

Methods

The framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web application, which offers an interactive user interface and does not require programming skills. We evaluated our method in seven different clinical applications using eight public datasets: six datasets from the recently published WORC database, and two prostate MRI datasets-Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-UCLA) and PROSTATEx.

Results

In the analyzed datasets, AutoRadiomics successfully created and optimized models using radiomics features. For WORC datasets, we achieved AUCs ranging from 0.56 for lung melanoma metastases detection to 0.93 for liposarcoma detection and thereby managed to replicate the previously reported results. No significant overfitting between training and test sets was observed. For the prostate cancer detection task, results were better in the PROSTATEx dataset (AUC = 0.73 for prostate and 0.72 for lesion mask) than in the Prostate-UCLA dataset (AUC 0.61 for prostate and 0.65 for lesion mask), with external validation results varying from AUC = 0.51 to AUC = 0.77.

Conclusion

AutoRadiomics is a robust tool for radiomic studies, which can be used as a comprehensive solution, one of the analysis steps, or an exploratory tool. Its wide applicability was confirmed by the results obtained in the diverse analyzed datasets. The framework, as well as code for this analysis, are publicly available under https://github.com/pwoznicki/AutoRadiomics.

Mapping of rRNA genes and telomeric sequences in Danube salmon (Hucho hucho) chromosomes using primed in situ labeling technique (PRINS)

In the current paper we described the application of primed in situ (PRINS) labeling approach for the chromosomal mapping of repetitive DNA sequences in Danube salmon (Hucho hucho) (2n = 82, NF = 112). PRINS was successfully performed with primers enabling amplification of 5S rRNA genes (minor rDNAs), NOR building DNA sequences (major rDNAs), and telomeric sequences. Two loci of 5S rRNA were observed on distinct chromosome pairs; the minor arrays were located interstitially on the long (q) arms of two large metacentrics (chromosomes No. 3) and the large clusters of 5S rDNAs were assigned to the short (p) arms of two subtelocentric chromosomes No. 18. Major rDNA clusters were observed on the p-arms of two submeta-subtelocentric chromosomes No. 10. These chromosomal areas were built with GC-rich chromatin what was proved in the course of chromomycin A(3) (CMA(3)) staining performed sequentially. Major and minor rDNA families were not co-localized in the Danube salmon chromosomes. The distinct hybridization signals at the ends of all the chromosomes were provided in the course of PRINS with (CCCTAA)(n) primer. The chromosomal localization of rRNA genes and telomeric DNA sequences was discussed in the context of Salmonidae karyotype evolution.

Chromosomal characteristics of rDNA in European grayling Thymallus thymallus (Salmonidae)

The chromosomal characteristics, locations and variations of two classes of ribosomal DNA (5S and 18S) were studied in European grayling karyotype (Thymallus thymallus, Salmonidae). Major rDNA sites as revealed by sequential CMA3/Ag staining and confirmed by in situ hybridization with a 18S rDNA probe were situated in two loci and were found to be polymorphic in size and displaying several distinct forms. The 5S rDNA was located by PRINS on three pairs of subtelocentric chromosomes, additional minor signal was present at the centromere of one metacentric element. 5S sites were not associated with NORs. The dosage compensation mechanism was proposed as an explanation of high frequency of lethal rDNA-deleted forms of the NOR-bearing chromosomes. Double variable pattern in the number and location of NORs supported the bi-directional evolution of salmonid rDNA loci.

Localization of 5S rRNA loci in three coregonid species (Salmonidae)

In the present study the chromosome distribution of the 5S rDNA loci and its relation to the major rDNA genes were investigated in three Coregonid species (Salmonidae): Coregonus lavaretus, Coregonus peled and Coregonus albula, a family which has experienced large karyotype rearrangements along its evolution starting from a tetraploid ancestor. 5S PRINS/CMA3 sequential staining together with previous data enabled us to locate 5S rRNA genes and nucleolar organizer regions (NORs) in the three species analyzed. PRINS revealed the 5S rDNA cluster at the distal part of the long arm of a similar submetacentric chromosome pair in the three species. Our data indicate that 5S rDNA clusters have probably conserved chromosomal location in the genus Coregonus, whereas 45S rDNA (NOR) sites are clearly differentiated, from a single locus in C. peled, to multiple loci in C. lavaretus and highly polymorphic multichromosomal location in C. albula.