Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study.

AIM: To evaluate whether deep learning reconstruction (DLR) can accelerate the acquisition of magnetic resonance imaging (MRI) sequences of the knee for clinical use. MATERIALS AND METHODS: Using a 1.5-T MRI scanner, sagittal fat-suppressed T2-weighted imaging (fs-T2WI), coronal proton density-weighted imaging (PDWI), and coronal T1-weighted imaging (T1WI) were performed. DLR was applied to images with a number of signal averages (NSA) of 1 to obtain 1DLR images. Then 1NSA, 1DLR, and 4NSA images were compared subjectively, and by noise (standard deviation of intra-articular water or medial meniscus) and contrast-to-noise ratio between two anatomical structures or between an anatomical structure and intra-articular water. RESULTS: Twenty-seven healthy volunteers (age: 40.6 ± 11.9 years) were enrolled. Three 1DLR image sequences were obtained within 200 s (approximately 12 minutes for 4NSA image). According to objective evaluations, PDWI 1DLR images showed the smallest noise and significantly higher contrast than 1NSA and 4NSA images. For fs-T2WI, smaller noise and higher contrast were observed in the order of 4NSA, 1DLR, and 1NSA images. According to the subjective analysis, structure visibility, image noise, and overall image quality were significantly better for PDWI 1DLR than 1NSA images; moreover, the visibility of the meniscus and bone, image noise, and overall image quality were significantly better for 1DLR than 4NSA images. Fs-T2WI and T1WI 1DLR images showed no difference between 1DLR and 4NSA images. CONCLUSION: Compared to PDWI 4NSA images, PDWI 1DLR images were of higher quality, while the quality of fs-T2WI and T1WI 1DLR images was similar to that of 4NSA images.

Impact of deep learning on radiologists and radiology residents in detecting breast cancer on CT: a cross-vendor test study.

AIM: To investigate the effect of deep learning on the diagnostic performance of radiologists and radiology residents in detecting breast cancers on computed tomography (CT). MATERIALS AND METHODS: In this retrospective study, patients undergoing contrast-enhanced chest CT between January 2010 and December 2020 using equipment from two vendors were included. Patients with confirmed breast cancer were categorised as the training (n=201) and validation (n=26) group and the testing group (n=30) using processed CT images from either vendor. The trained deep-learning model was applied to test group patients with (30 females; mean age = 59.2 ± 15.8 years) and without (19 males, 21 females; mean age = 64 ± 15.9 years) breast cancer. Image-based diagnostic performance of the deep-learning model was evaluated with the area under the receiver operating characteristic curve (AUC). Two radiologists and three radiology residents were asked to detect malignant lesions by recording a four-point diagnostic confidence score before and after referring to the result from the deep-learning model, and their diagnostic performance was evaluated using jackknife alternative free-response receiver operating characteristic analysis by calculating the figure of merit (FOM). RESULTS: The AUCs of the trained deep-learning model on the validation and test data were 0.976 and 0.967, respectively. After referencing with the result of the deep learning model, the FOMs of readers significantly improved (reader 1/2/3/4/5: from 0.933/0.962/0.883/0.944/0.867 to 0.958/0.968/0.917/0.947/0.900; p=0.038). CONCLUSION: Deep learning can help radiologists and radiology residents detect breast cancer on CT.

Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images.

AIM: To evaluate 1.5 T magnetic resonance imaging (MRI) brain images with denoising procedures using deep learning-based reconstruction (dDLR) relative to the original 1.5 and 3 T images. MATERIALS AND METHODS: Eleven volunteers underwent MRI at 3 and 1.5 T. Two-dimensional fast spin-echo T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR) imaging and diffusion-weighted imaging (DWI) sequences were performed. The dDLR method was applied to the 1.5 T data (dDLR-1.5 T), then the image quality of the dDLR-1.5 T data relative to the original 1.5 T and 3 T data was qualitatively and quantitatively assessed based on the structure similarity (SSIM) index; the signal-to-noise ratios (SNRs) of the grey matter (GM) and white matter (WM); and the contrast-to-noise ratios (CNRs) between the GM and WM (CNRgm-wm) and between the striatum (ST) and WM (CNRst-wm). RESULTS: The perceived image quality, and SNRs and CNRs were significantly higher for the dDLR-1.5 T images versus the 1.5 T images for all sequences and almost comparable or even superior to those of the 3 T images. For DWI, the SNRs and CNRst-wm were significantly higher for the dDLR-1.5 T images versus the 3 T images. CONCLUSION: The dDLR technique improved the image quality of 1.5 T brain MRI images. With respect to qualitative and quantitative measurements, the denoised 1.5 T brain images were almost equivalent or even superior to the 3 T brain images.

Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest.

RATIONALE AND OBJECTIVES: To investigate the impact of random survival forest (RSF) classifier trained by radiomics features over the prediction of the overall survival of patients with resectable hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The dynamic computed tomography data of 127 patients (97 men, 30 women; mean age, 68 years) newly diagnosed with resectable HCC were retrospectively analyzed. After manually setting the region of interest to include the tumor within the slice at its maximum diameter, texture analyses were performed with or without a Laplacian of Gaussian filter. Using the extracted 96 histogram based texture features, RSFs were trained using 5-fold cross-validation to predict the individual risk for each patient on disease free survival (DFS) and overall survival (OS). The associations between individual risk and DFS or OS were evaluated using Kaplan-Meier analysis. The effects of the predicted individual risk and clinical variables upon OS were analyzed using a multivariate Cox proportional hazards model. RESULTS: Among the 96 histogram based texture features, RSF extracted 8 of high importance for DFS and 15 for OS. The RSF trained by these features distinguished two patient groups with high and low predicted individual risk (P=1.1×10-4 for DFS, 4.8×10-7 for OS). Based on the multivariate Cox proportional hazards model, high predicted individual risk (hazard ratio=1.06 per 1% increase, P=8.4×10-8) and vascular invasion (hazard ratio=1.74, P=0.039) were the only unfavorable prognostic factors. CONCLUSIONS: The combination of radiomics analysis and RSF might be useful in predicting the prognosis of patients with resectable HCC.

Calcium-dependent association of annexin VI, protein kinase C alpha, and neurocalcin alpha on the raft fraction derived from the synaptic plasma membrane of rat brain.

A membrane microdomain enriched in cholesterol and sphingolipids or so called "raft" region was found to contain many signal transducing proteins such as GPI-anchored proteins, trimeric G proteins and protein tyrosine kinases. Because brain-derived raft contains two calmodulin-binding proteins, GAP-43 and NAP-22 as the major protein components, the raft domain is assumed to be important in the Ca(2+)-signaling. In this study, we analyzed protein components showing Ca(2+)-dependent binding to the raft of synaptic plasma membrane from rat brain. SDS-PAGE analysis of the protein components in the EGTA eluate from the raft prepared in the presence of Ca(2+)-ions showed the elution of 80 kDa, 68 kDa, 22 kDa, and 21 kDa proteins. These proteins were identified as protein kinase C alpha (80 kDa) and annexin VI (68 kDa) from the partial amino-acid sequencing, and neurocalcin alpha (22 kDa) and calmodulin (21 kDa) with western blotting and electrophoretic mobilities in the presence or absence of Ca(2+) ions. Further immunoblotting experiments showed the Ca(2+)-dependent association of conventional, but not non-conventional, subtypes of PKC to the raft.

Impaired proliferative response and low interleukin-2 production in patients with lymphoma.

1. The proliferative response of phytohemagglutinin-activated lymphocytes from patients with Hodgkin's and non-Hodgkin's lymphoma was compared to their interleukin-2 (IL-2) production. 2. Impairment in the lymphoproliferative response paralleled a reduction in IL-2 production. 3. Suppressor cells and serum factors which depressed the proliferative response of the patients' lymphocytes inhibited IL-2 production. 4. Enhanced prostaglandin synthesis was one of the factors causing impairment of the proliferative response and the diminished IL-2 production in lymphocytes from patients with lymphoma. 5. A partial restoration of patients' cellular responses in vitro was achieved by adding conditioned media containing IL-1 or IL-2.

Roughness measurement using a shearing interference microscope.

A shearing interference microscope has high image stability even under vibration. This paper proposes a method of rms roughness measurement of smooth surfaces using shearing interference microscope images which include interference fringes. It makes use of the fact that, when the shearing length is extended in comparison with the autocorrelation length of the rough surface, the shearing interference image has a close relationship with the rms roughness of the measured surface. Principle and theory are discussed as well as the result of measurements of precision-finish surfaces 20-100-A rms.

Improvement of chemical analysis of antibiotics. V. A simple method for the analysis of tetracyclines using reversed-phase high-performance liquid chromatography.

An isocratic high-performance liquid chromatographic method for the determination of tetracyclines is described using a mobile phase containing oxalic acid and C8- and C18-modified silica gel columns. For good separations of tetracyclines, oxalic acid concentrations of above 0.01 and 0.2 M respectively for parent tetracyclines (group I) and impurities in tetracycline (group II) are required. The optimum pH of the aqueous oxalic acids solution in the mobile phase is 2.0. The combinations of the C8-modified silica gel column with methanol-acetonitrile-0.01 M aqueous oxalic acid solution pH 2.0 (1:1.5:5) and the C18-modified silica gel column with methanol-acetonitrile-0.2 M aqueous oxalic acid solution pH 2.0(1:1:3.5) gave satisfactory results for groups I and II, respectively.

Linfocitos e biotransformacao de drogas. / lymphocytes and biotransformation of drugs

Os autores apresentam alguns aspectos da biotransformacao de drogas, relatanto particularmente sobre as propriedades de aril-hidrocarboneto hidroxilase (AHH) linfocitaria que e uma das enzimas constituintes do sistema oxigenase de funcao mista dos microssomas. As prespectivas quanto a aplicacao da cultura de linfocitos humanos como modelo experimental, para o diagnostico de neoplasias e os problemas metodologicos sao relatados

Hand-mirror lymphocytes in infectious mononucleosis.

The identification of a morphologically unique lymphocyte in the peripheral blood of patients with infectious mononucleosis (IM) led to additional studies. Examination of Wright-stained smears of EDTA anticoagulated blood obtained from 25 patients with IM at presentation revealed significantly increased (p less than 0.0001) percentages of hand-mirror lymphocytes (HML) (mean 9.2%) compared with normal controls (mean 2.7%) or controls with nonspecific viral syndromes (mean 2.2%). Follow-up blood samples obtained on 10 of these patients demonstrated a marked increase in the HML count (mean 12.1%) that coincided with the onset of recovery. E-rosette separation of Ficoll-Hypaque-derived peripheral blood lymphocytes from 5 patients with early recovery IM showed the HML to be present almost exclusively in the T-cell population, representing about 25% of the T cells. Identical procedures on 5 controls showed less than 5% HML in the T-cell sample. Cytochemistries supported a T-cell derivation for HML. Electron microscopic examination of HML in IM demonstrated that these cells have unique ultrastructural features that may be related to functions of cellular attachment and cytotoxicity.

Ligand-activated T cell growth factor-induced proliferation: absorption of T cell growth factor by activated T cells.

The fate in culture of the T cell growth factor (TCGF), which is required for continued growth of human cultured T cells (CTC) in vitro, was studied. TCGF activity was stable for 7 days at 37 degrees C. However, it was no longer detectable after incubation with actively growing CTC at 37 degrees C for 3 days. This loss of TCGF activity also occurred quite rapidly and was detectable within 1 hr of incubation of 0.3 ml supernatant with 2 to 5 x 10(7) CTC at 23 degrees C. 2 x 10(8) mononuclear peripheral blood leukocytes were not effective in removing TCGF activity, and incubation with similar numbers of cells from B and T cell lines had no effect. Three-day-old concanavalin A and phytohemagglutinin blasts were very reactive with TCGF, so that 10(7) or 2 x 10(7) cells consistently removed TCGF activity. These experiments suggested specific absorption of TCGF by activated T cells, and led us to develop a model of ligand-activated TCGF-induced proliferation of T cells: Ligands induce production of TCGF by T-producer cells and deliver a first signal to the T-responder cells. This causes a receptor for TCGF to appear on T-responder cells. Only then does TCGF deliver the obligatory second signal that is needed to drive the T-responder cells into proliferation.