Head CT: Toward Making Full Use of the Information the X-Rays Give

Contrast quality control for segmentation task based on deep learning models-Application to stroke lesion in CT imaging

Introduction

Although medical imaging plays a crucial role in stroke management, machine learning (ML) has been increasingly used in this field, particularly in lesion segmentation. Despite advances in acquisition technologies and segmentation architectures, one of the main challenges of subacute stroke lesion segmentation in computed tomography (CT) imaging is image contrast.

Methods

To address this issue, we propose a method to assess the contrast quality of an image dataset with a ML trained model for segmentation. This method identifies the critical contrast level below which the medical-imaging model fails to learn meaningful content from images. Contrast measurement relies on the Fisher's ratio, estimating how well the stroke lesion is contrasted from the background. The critical contrast is found-thanks to the following three methods: Performance, graphical, and clustering analysis. Defining this threshold improves dataset design and accelerates training by excluding low-contrast images.

Results

Application of this method to brain lesion segmentation in CT imaging highlights a Fisher's ratio threshold value of 0.05, and training validation of a new model without these images confirms this with similar results with only 60% of the training data, resulting in an almost 30% reduction in initial training time. Moreover, the model trained without the low-contrast images performed equally well with all images when tested on another database.

Discussion

This study opens discussion with clinicians concerning the limitations, areas for improvement, and strategies for enhancing datasets and training models. While the methodology was only applied to stroke lesion segmentation in CT images, it has the potential to be adapted to other tasks.

Age- and sex-adjusted CT-based reference values for temporal muscle thickness, cross-sectional area and radiodensity

Muscle mass has been traditionally assessed by measuring paraspinal muscle areas at the level of the third lumbar vertebra on computed tomography (CT). Neurological or neurosurgical patients seldom undergo CT scans of the lumbar region. Instead, temporal muscle thickness (TMT), cross-sectional area (TMA) and radiodensity measured from head CT scans are readily available measures of muscle mass and quality in these patient cohorts. The purpose of this retrospective study was to establish CT-based reference values for TMT, TMA and radiodensity for each decade of age from 0 to 100 years normalized by age and sex, and to define cut-off values for subjects at risk for sarcopenia as defined by the European Working Group on Sarcopenia in Older People (EWGSOP). Subjects diagnosed with a concussion at the Oulu University Hospital between January 2014 and December 2022 (n = 9254) were identified to obtain a reference population. Subjects with significant pre-existing co-morbidities were excluded. TMT, TMA and radiodensity were measured, measurement reliability was quantified, and sex-adjusted reference values were calculated for each age decade. Quantile regression was used to model age-related changes in muscle morphomics. A total of 500 subjects [250 (50.0%) males] with a mean age of 49.2 ± 27.9 years were evaluated. Inter- and intra-observer reliability was almost perfect for TMT and TMA, and substantial-to-almost perfect for radiodensity. The mean TMT, TMA and radiodensity were 5.2 ± 1.9 mm, 284 ± 159 mm2 and 44.6 ± 17.7HU, respectively. The cut-off values for reduced TMT, TMA and radiodensity for males/females using the European Working Group on Sarcopenia in Older People compliant criteria were ≤ 4.09 mm/≤3.44 mm, ≤ 166 mm2/≤156 mm2, and ≤ 35.5HU/≤35.2HU, respectively. We described a standardized CT-based TMT and TMA measurement protocol practical for clinical use with almost perfect reliability. Using the protocol, we produced quantile regression models for the detection of reduced TMT, TMA and radiodensity at the lowest 5th, 10th, 20th, 30th, 40th and 50th percentiles as well as the EWGSOP compliant criteria cut-off values for reduced muscle mass to facilitate generalizable radiological sarcopenia research.

Assessment of image quality and establishment of local acceptable quality dose for computed tomography based on patient effective diameter

Purpose

We aim to develop modified clinical indication (CI)-based image quality scoring criteria (IQSC) for assessing image quality (IQ) and establishing acceptable quality doses (AQDs) in adult computed tomography (CT) examinations, based on CIs and patient sizes.

Approach

CT images, volume CT dose index (CTDI vol ), and dose length product (DLP) were collected retrospectively between September 2020 and September 2021 for eight common CIs from two CT scanners at a central hospital in the Kingdom of Bahrain. Using the modified CI-based IQSC and a Likert scale (0 to 4), three radiologists assessed the IQ of each examination. AQDs were then established as the median value ofCTDI vol and DLP for images with an average score of 3 and compared to national diagnostic reference levels (NDRLs).

Results

Out of 581 examinations, 60 were excluded from the study due to average scores above or below 3. The established AQDs were lower than the NDRLs for all CIs, except AQDs / CTDI vol for oncologic follow-up for large patients (28 versus 26 mGy) in scanner A, besides abdominal pain for medium patients (16 versus 15 mGy) and large patients (34 versus 27 mGy), and diverticulitis/appendicitis for medium patients (15 versus 12 mGy) and large patients (33 versus 30 mGy) in scanner B, indicating the need for optimization.

Conclusions

CI-based IQSC is crucial for IQ assessment and establishing AQDs according to patient size. It identifies stations requiring optimization of patient radiation exposure.

Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and Hemorrhagic Stroke: Algorithm Development and Validation

BACKGROUND

Neuroimaging is the gold-standard diagnostic modality for all patients suspected of stroke. However, the unstructured nature of imaging reports remains a major challenge to extracting useful information from electronic health records systems. Despite the increasing adoption of natural language processing (NLP) for radiology reports, information extraction for many stroke imaging features has not been systematically evaluated.

OBJECTIVE

In this study, we propose an NLP pipeline, which adopts the state-of-the-art ClinicalBERT model with domain-specific pretraining and task-oriented fine-tuning to extract 13 stroke features from head computed tomography imaging notes.

METHODS

We used the model to generate structured data sets with information on the presence or absence of common stroke features for 24,924 patients with strokes. We compared the survival characteristics of patients with and without features of severe stroke (eg, midline shift, perihematomal edema, or mass effect) using the Kaplan-Meier curve and log-rank tests.

RESULTS

Pretrained on 82,073 head computed tomography notes with 13.7 million words and fine-tuned on 200 annotated notes, our HeadCT_BERT model achieved an average area under receiver operating characteristic curve of 0.9831, F1-score of 0.8683, and accuracy of 97%. Among patients with acute ischemic stroke, admissions with any severe stroke feature in initial imaging notes were associated with a lower probability of survival (P<.001).

CONCLUSIONS

Our proposed NLP pipeline achieved high performance and has the potential to improve medical research and patient safety.

Medical image fusion quality assessment based on conditional generative adversarial network

Multimodal medical image fusion (MMIF) has been proven to effectively improve the efficiency of disease diagnosis and treatment. However, few works have explored dedicated evaluation methods for MMIF. This paper proposes a novel quality assessment method for MMIF based on the conditional generative adversarial networks. First, with the mean opinion scores (MOS) as the guiding condition, the feature information of the two source images is extracted separately through the dual channel encoder-decoder. The features of different levels in the encoder-decoder are hierarchically input into the self-attention feature block, which is a fusion strategy for self-identifying favorable features. Then, the discriminator is used to improve the fusion objective of the generator. Finally, we calculate the structural similarity index between the fake image and the true image, and the MOS corresponding to the maximum result will be used as the final assessment result of the fused image quality. Based on the established MMIF database, the proposed method achieves the state-of-the-art performance among the comparison methods, with excellent agreement with subjective evaluations, indicating that the method is effective in the quality assessment of medical fusion images.

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

In recent years, there have been major advances in deep learning algorithms for image recognition in traumatic brain injury (TBI). Interest in this area has increased due to the potential for greater objectivity, reduced interpretation times and, ultimately, higher accuracy. Triage algorithms that can re-order radiological reading queues have been developed, using classification to prioritize exams with suspected critical findings. Localization models move a step further to capture more granular information such as the location and, in some cases, size and subtype, of intracranial hematomas that could aid in neurosurgical management decisions. In addition to the potential to improve the clinical management of TBI patients, the use of algorithms for the interpretation of medical images may play a transformative role in enabling the integration of medical images into precision medicine. Acute TBI is one practical example that can illustrate the application of deep learning to medical imaging. This review provides an overview of computational approaches that have been proposed for the detection and characterization of acute TBI imaging abnormalities, including intracranial hemorrhage, skull fractures, intracranial mass effect, and stroke.