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The basic performance of photon-counting detector computed tomography (PCD CT) is superior to conventional CT (energy-integrating detector CT: EID CT) because its spatial- and contrast resolution of soft tissues is higher, and artifacts are reduced. Because the X-ray photon energy separation is better with PCD CT than conventional EID-based dual-energy CT, it has the potential to improve virtual monochromatic- and virtual non-contrast images, material decomposition including quantification of the iodine distribution, and K-edge imaging. Therefore, its clinical applicability may be increased. Although the image quality of PCD CT scans is superior to that of EID CT currently, further improvement may be possible. The introduction of iterative image reconstruction and reconstruction with deep convolutional neural networks will be useful.

Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as “noise and artifact reduction”, “super resolution” and “image acquisition and reconstruction”. For each category, we present and outline the features of some studies.

Dual-energy CT, the object is scanned at two different energies, makes it possible to identify the characteristics of materials that cannot be evaluated on conventional single-energy CT images. This imaging method can be used to perform material decomposition based on differences in the material-attenuation coefficients at different energies. Dual-energy analyses can be classified as image data-based- and raw data-based analysis. The beam-hardening effect is lower with raw data-based analysis, resulting in more accurate dual-energy analysis. On virtual monochromatic images, the iodine contrast increases as the energy level decreases; this improves visualization of contrast-enhanced lesions. Also, the application of material decomposition, such as iodine- and edema images, increases the detectability of lesions due to diseases encountered in daily clinical practice. In this review, the minimal essentials of dual-energy CT scanning are presented and its usefulness in daily clinical practice is discussed.

Although quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image features. Nevertheless, rapidly growing artificial intelligence technology is overcoming obstacles. We previously reported the fine-tuned apodized phase-contrast microscopy system to capture high-resolution, label-free images of organelle dynamics in unstained living cells (Shimasaki, K. et al. (2024). Cell Struct. Funct., 49: 21–29). We here showed machine learning-based segmentation models for subcellular targeted objects in phase-contrast images using fluorescent markers as origins of ground truth masks. This method enables accurate segmentation of organelles in high-resolution phase-contrast images, providing a practical framework for studying cellular dynamics in unstained living cells.Key words: label-free imaging, organelle dynamics, apodized phase contrast, deep learning-based segmentation

Cell biologists have long sought the ability to observe intracellular structures in living cells without labels. This study presents procedures to adjust a commercially available apodized phase-contrast (APC) microscopy system for better visualizing the dynamic behaviors of various subcellular organelles in living cells. By harnessing the versatility of this technique to capture sequential images, we could observe morphological changes in cellular geometry after virus infection in real time without probes or invasive staining. The tune-up APC microscopy system is a highly efficient platform for simultaneously observing the dynamic behaviors of diverse subcellular structures with exceptional resolution.

Background: This study aimed to assess the clinical relevance of three-dimensional occupational stress (job stressor score [A score], psychological and physical stress response score [B score], and social support for workers score [C score]) of the Brief Job Stress Questionnaire (BJSQ) in the national stress check program in Japan to irregular menstruation.Methods: The present retrospective cohort study included 2,078 female employees aged 19–45 years who had both annual health checkups and the BJSQ between April 2019 and March 2022 in a national university in Japan. The outcome was self-reported irregular menstruation measured at annual health checkups until March 2023. A dose-dependent association between BJSQ scores and incidence of irregular menstruation was examined using Cox proportional hazards models to calculate multivariable-adjusted hazard ratios (HRs) of four quantile (0–49% [Q0–49], 50–74% [Q50–74], 75–89% [Q75–89], and 90–100% [Q90–100]) of the BJSQ scores.Results: During 2.0 years of the median observational period, 257 (12.4%) women reported irregular menstruation. B score, not A or C scores, was identified as a significant predictor of irregular menstruation (adjusted HR of A, B, and C scores per 1 standard deviation: 1.06 [95% confidence interval CI, 0.89–1.27], 1.35 [95% CI, 1.15–1.57], and 0.93 [95% CI, 0.80–1.08], respectively). Women with higher B score had a significantly higher risk of irregular menstruation in a dose-dependent manner (adjusted HR of Q0–49, Q50–74, Q75–89, and Q90–100: 1.00 [reference], 1.38 [95% CI, 1.00–1.90], 1.48 [95% CI, 1.00–2.18], and 2.18 [95% CI, 1.38–3.43], respectively).Conclusion: Psychological and physical stress response predicted irregular menstruation.

•Multiband (MB) sequences allow for faster sampling rates.•MB sequences may interfere with detecting brain responses in mesolimbic regions.•Gustatory stimuli would elicite relatively gradual brain response.•It is unclear if faster sampling rates is nessessary to detect gustatory response.•A conventional sampling rate without MB would be beneficial for gustatory response. Recent technical developments have led to the invention of multiband functional magnetic resonance imaging (fMRI) sequences that allow for faster sampling rates. However, some studies have highlighted problems with these sequences, leading to a decreased temporal signal-to-noise ratio (tSNR). In addition, this temporal noise may interfere with detecting reward-related responses in mesolimbic regions. The blood-oxygen-level-dependent signal utilized in the majority of fMRI measurements is relatively slow. Furthermore, the cerebral response to gustatory stimuli would also be relatively slow. Therefore, given the temporal noise issues with multiband sequences, it is unclear whether multiband sequences are necessary for fMRI studies using gustatory stimuli. We thus conducted an fMRI experiment using a gustatory stimulus to investigate the effects of multiband sequences and increased sampling rates on statistical outcome measures. A single-band sequence with a repetition time (TR) of 2 s of phantom fMRI data and gustatory fMRI data from the gustatory regions exhibited the highest tSNR, although the tSNR of this sequence of gustatory fMRI was not statistically different from tSNR of multiband sequences with a TR of 2 s in any of the selected region of interests. Conventional general linear model analysis of fMRI showed that single-band sequences are more advantageous than multiband sequences for detecting brain responses to gustatory stimuli in the primary gustatory cortex. In addition, a Bayesian data comparison showed that data derived from a single-band sequence with a TR of 2 s was optimal for inferring neuronal connectivity in gustatory processing. Therefore, a conventional single-band sequence with a TR of 2 s is more appropriate for fMRI with gustatory stimuli. Image acquisition sequences should be selected aligned with the study objectives and target brain regions.

Purpose Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the commercially available deep-learning-based lung nodule detection (DL-LND) using LDCT images at Japanese lung cancer screening (LCS). Materials and methods Included were 43 patients with suspected lung cancer on LDCT images and pathologically confirmed lung cancer. The reference standard for nodules whose diameter exceeded 4 mm was set by a radiologist who referred to the reports of two other radiologists reading the LDCT images. After we applied commercially available DL-LND to the LDCT images, the radiologist reviewed all nodules detected by DL-LND. When he failed to identify an existing nodule, it was also included in the reference standard. To validate the performance of DL-LND, the sensitivity for lung nodules and lung cancer, the positive-predictive value (PPV) for lung nodules, and the mean number of false-positive (FP) nodules per CT scan were recorded. Results The radiologist detected 97 nodules including 43 lung cancers and missed 3 solid nodules detected by DL-LND. A total of 100 nodules was included in the reference standard. DL-LND detected 396 nodules including 40 lung cancers. The sensitivity for the 100 nodules was 96.0%; the PPV was 24.2% (96/396). The mean number of FP nodules per CT scan was 7.0; sensitivity for lung cancer was 93.0% (40/43). DL-LND missed three lung cancers; 2 of these were atypical pulmonary cysts. Conclusion We externally verified that the sensitivity for lung nodules and lung cancer by DL-LND was very high. However, its low PPV and the increased FP nodules remains a serious drawback of DL-LND.

PurposePostmortem CT (PMCT) is used widely to identify the cause of death. However, its diagnostic performance in cases of natural death from out-of-hospital cardiac arrest (OHCA) may be unsatisfactory because the cause tends to be cardiogenic and cannot be detected on PMCT images. We retrospectively investigated the diagnostic performance of PMCT in the diagnosis of natural death from OHCA and compared it to that of unnatural death.Materials and methodsOur series included 450 cases; 336 were natural- and 114 were unnatural death cases. Between 2018 and 2022 all underwent non-contrast PMCT to identify the cause of death. Two radiologists reviewed the PMCT images and categorized them as diagnostic (PMCT alone sufficient to determine the cause of death), suggestive (the cause of death was suggested but additional information was needed), and non-diagnostic (the cause of death could not be determined on PMCT images). The diagnostic performance of PMCT was defined by the percentage of diagnosable and suggestive cases and compared between natural- and unnatural death cases. Interobserver agreement for the cause of death on PMCT images was also assessed with the Cohen kappa coefficient of concordance.ResultsThe diagnostic performance of PMCT for the cause of natural- and unnatural deaths from OHCA was 30.3% and 66.6%, respectively (p < 0.01). The interobserver agreement for the cause of natural- and unnatural deaths on PMCT images was very good with kappa value 0.92 and 0.96, respectively.ConclusionAs PMCT identified the cause of natural death by OHCA in only 30% of cases, its diagnostic performance must be improved.

ObjectivesDeep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).MethodsOur retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared.ResultsThe image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality.ConclusionsDLR improved the quality of abdominal U-HRCT images.Key Points• The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen.• Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.