Background
Reducing Magnetic resonance imaging (MRI) scan time has been an important issue for clinical applications. In order to reduce MRI scan time, imaging acceleration was made possible by ...undersampling k‐space data. This is achieved by leveraging additional spatial information from multiple, independent receiver coils, thereby reducing the number of sampled k‐space lines.
Purpose
The aim of this study is to develop a deep‐learning method for parallel imaging with a reduced number of auto‐calibration signals (ACS) lines in noisy environments.
Methods
A cycle interpolator network is developed for robust reconstruction of parallel MRI with a small number of ACS lines in noisy environments. The network estimates missing (unsampled) lines of each coil data, and these estimated missing lines are then utilized to re‐estimate the sampled k‐space lines. In addition, a slice aware reconstruction technique is developed for noise‐robust reconstruction while reducing the number of ACS lines. We conducted an evaluation study using retrospectively subsampled data obtained from three healthy volunteers at 3T MRI, involving three different slice thicknesses (1.5, 3.0, and 4.5 mm) and three different image contrasts (T1w, T2w, and FLAIR).
Results
Despite the challenges posed by substantial noise in cases with a limited number of ACS lines and thinner slices, the slice aware cycle interpolator network reconstructs the enhanced parallel images. It outperforms RAKI, effectively eliminating aliasing artifacts. Moreover, the proposed network outperforms GRAPPA and demonstrates the ability to successfully reconstruct brain images even under severe noisy conditions.
Conclusions
The slice aware cycle interpolator network has the potential to improve reconstruction accuracy for a reduced number of ACS lines in noisy environments.
Raki is a traditional Turkish drink. It is obtained by distillation process after suma (alcoholic water) obtained from raisins is sweetened with anise (pinpinella anisum). The quality of raki depends ...on the quality of the grapes used in the production process and technological differences. In this study, in order to ensure consumer food safety, 10 different raki samples in Turkey were thawed by microwave cracking system, and their trace element content was examined. Differential pulse polarography (DPP), which has high selectivity and sensitivity, was used for the measurements. Appropriate measurement conditions were determined for each element. Validation of the method was done with a known sample. The detection limit of the method was determined as 0.1 μgg-1. The amounts of Fe, Cu, Pb, Cd, Cr, As, Ni, Zn, Se elements in the raki samples were determined as approximately 2-80 μgg-1.
Purpose
To evaluate an iterative learning approach for enhanced performance of robust artificial‐neural‐networks for k‐space interpolation (RAKI), when only a limited amount of training data ...(auto‐calibration signals ACS) are available for accelerated standard 2D imaging.
Methods
In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI iRAKI), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in vivo datasets from the fastMRI neuro database with different contrast settings.
Results
For limited training data (18 and 22 ACS lines for R = 4 and R = 5, respectively), iRAKI outperforms standard RAKI by reducing residual artifacts and yields better noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. Additionally, iRAKI shows better performance than both GRAPPA and standard RAKI in case of pre‐scan calibration with varying contrast between training‐ and undersampled data.
Conclusion
RAKI benefits from the iterative learning approach, which preserves the noise suppression feature, but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.
Priporočila za obravnavo rakov glave in vratu (RGV) v Sloveniji sledijo priporočilom in usmeritvam, povzetim v publikaciji neprofitne mreže 30 vodilnih severnoameriških inštitucij za obravnavo raka, ...National Comprehensive Cancer Network, in hkrati upoštevajo obstoječe zmožnosti slovenskega zdravstvenega sistema. Smernice predstavljajo poenoteno mnenje vseh štirih najpomembnejših deležnikov na področju obravnave rakov glave in vratu v državi: Klinike za otorinolaringologijo in cervikofacialno kirurgijo, Kliničnega oddelka za maksilofacialno in oralno kirurgijo ter Stomatološke klinike UKC Ljubljana, Klinike za otorinolaringologijo, cervikalno in maksilofacialno kirurgijo UKC Maribor ter Onkološkega inštituta Ljubljana.
Purpose
Simultaneous multi‐slice acquisitions are essential for modern neuroimaging research, enabling high temporal resolution functional and high‐resolution q‐space sampling diffusion acquisitions. ...Recently, deep learning reconstruction techniques have been introduced for unaliasing these accelerated acquisitions, and robust artificial‐neural‐networks for k‐space interpolation (RAKI) have shown promising capabilities. This study systematically examines the impacts of hyperparameter selections for RAKI networks, and introduces a novel technique for training data generation which is analogous to the split‐slice formalism used in slice‐GRAPPA.
Methods
RAKI networks were developed with variable hyperparameters and with and without split‐slice training data generation. Each network was trained and applied to five different datasets including acquisitions harmonized with Human Connectome Project lifespan protocol. Unaliasing performance was assessed through L1 errors computed between unaliased and calibration frequency‐space data.
Results
Split‐slice training significantly improved network performance in nearly all hyperparameter configurations. Best unaliasing results were achieved with three layer RAKI networks using at least 64 convolutional filters with receptive fields of 7 voxels, 128 single‐voxel filters in the penultimate RAKI layer, batch normalization, and no training dropout with the split‐slice augmented training dataset. Networks trained without the split‐slice technique showed symptoms of network over‐fitting.
Conclusions
Split‐slice training for simultaneous multi‐slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to further improvements in unaliasing performance.
Pred kratkim je bilo v EU registrirano novo tarčno zdravilo LynparzaTM (olaparib) za zdravljenje bolnic z recidivnim seroznim karcinomom jajčnikov, jajcevodov ali primarnim peritonealnim seroznim ...karcinomom (PPSC), ki imajo znano mutacijo v genih BRCA1/2 (somatsko mutacijo ali mutacijo zarodnih celic). Olaparib je bil registriran na osnovi podanalize raziskave faze II (9), v kateri so imele bolnice z mutacijov genih BRCA1/2, ki so bile zdravljene z olaparibom, za 7 mesecev daljše preživetje brez napredovanja bolezni, kot bolnice, ki niso bile zdravljene z olaparibom (11 mesecev proti 4 mesecem); razlika je bila statistično značilna (HR 0,18;p < 0,00001). Razlik v celokupnem preživetju bolnic med skupinama ni bilo. Olaparib je peroralno zdravilo v obliki kapsul, kar omogoča, da ga lahko bolnice jemljejo doma. Po doslej znanih podatkih sta bila najpogostejša neželena učinka blaga slabost in utrujenost. Pri tem je 25 % bolnic prejemalo olaparib dve leti ali več, prekinitev zdravljenja zaradi neželenih učinkov pa je bila redka (le pri 9 % bolnic), kar kaže na to, da je zdravljenje z olaparibom večinoma dobro prenosljivo, to pa predstavlja izrazito prednost v primerjavi s kemoterapijo, ki je bila doslej edina možnost zdravljenja recidivnega raka jajčnikov (9).
Turkish Rakı is one of the most consumed alcoholic beverages in Turkey. The chemical and metal contamination of alcoholic beverages is a threat to human health and lowers the quality of the product. ...Ethyl carbamate and furfural are carcinogenic to animals, and have been classified as Group 2A and 3 agents respectively by the International Agency for Research on Cancer. Factors, such as raw materials, process time and storage, affect metal concentrations in beverages. Limits for chemicals and metals in Rakı have not yet been established by the Turkish agency. The present study aimed to evaluate the chemical and metal contamination of commercial Turkish Rakı. Thirty-seven different types of Turkish Rakı were purchased from local markets in Turkey. Ethyl carbamate (EC) and furfural (FR) levels were measured by Gas Chromatography-Mass Spectroscopy (GC-MS). Arsenic, copper, lead, and zinc levels were measured by Inductively Coupled Plasma–Optical Emission Spectrometry (ICP-OES). None of the Rakı samples contained EC. Furfural was not detected in commercial Rakı samples but only in illegal Rakı samples. Ethanol and methanol levels complied with Turkish regulation. Our data shows that commercial Rakı from Turkey was contaminated by very low amounts of arsenic (<LOD–0.02 mg/L), copper (0.05–0.41 mg/L), lead (< LOD–0.03 mg/L) and zinc (0.01–0.39 mg/L).
•A study about authentication of a traditional aniseed flavoured spirit, raki.•Mid-IR spectroscopy is succesfully used in detection of methanol adulteration of raki.•Quantification of adulteration of ...raki is also achieved with the same technique in combination with chemometrics.
Consumption of traditional aniseed alcoholic beverage, raki, adulterated with methanol results in deaths, therefore, its detection is an important issue. In this study, mid-infrared spectra of pure and methanol adulterated (0.5–10% (vol/vol)) raki samples were collected with an attenuated total reflectance attachment of a Fourier-transform infrared spectrometer. Principal component analysis was used to discriminate pure and adulterated raki samples, then, a partial least square model was constructed to determine the adulterant methanol content in raki using mid-IR spectral data. A minimum threshold level of 0.5% methanol in raki samples was successfully detected. A good prediction model for determination of methanol adulteration ratio in raki samples was also constructed (R2=0.98 and RPD=8.35).
This paper proposes an image reconstruction method for simultaneous multi-slice imaging (SMS) by combining the virtual conjugate coil (VCC) technology and robust artificial-neural-networks for ...k-space interpolation (RAKI). This method can effectively improve the reconstruction quality, and is named VIRGINIA (VIRtual conjuGate coIls Neural-networks InterpolAtion). VIRGINIA utilizes the complex conjugate symmetry property of the virtual coil concept to generate virtual coil data for training, and obtains better image quality by applying the trained network to the original aliased SMS data. With experimental data, the VIRGINIA method was compared to other reconstruction methods (i.e., RAKI only and slice-GRAPPA) in terms of quantitative indices such as structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). The results demonstrated that, under some certain slice-acceleration factors, VIRGINIA produced better reconstruction quality than those obtainable by Slice-GR
V članku je opisana zgodovina klasifikacije vmesne skupine ščitničnih rakov, ki so uvrščeni med zrele oblike ščitničnega raka (WDTC) in nezrele oblike (ATC). Ta vmesna skupina se imenuje slabo ...diferencirani rak ščitnice (PDTC). Tumorji iz te skupine povzročajo slab potek bolezni z lokalno ponovitvijo bolezni ali oddaljenimi zasevki ali z obojim, medtem ko je pri največji skupini dobro diferenciranih rakov ščitnice, ki predstavljajo veliko večino rakov ščitnice, napoved poteka bolezni večinoma veliko ugodnejša. Po poteku so slabo diferencirani raki ščitnice podobni nezreli obliki ščitničnega raka, vendar pa je ob izbranem zdravljenju pričakovati znatno boljše preživetje kot pri anaplastičnem raku. Opisane so nekatere patološke značilnosti slabo diferenciranih rakov in osnovna klinična slika. Prikazani so tudi 3 primeri bolnikov, od tega 2 bolnic z izjemnim potekom in 1 bolnika s pričakovanim potekom bolezni. Nakazane so tudi nove možnosti sistemskega zdravljenja raka ščitnice.
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