The “cardiac neglect”: a gentle reminder to radiologists interpreting contrast-enhanced abdominal MDCT

Myocardial infarction (MI) may be visible on contrast-enhanced multidetector computed tomography (MDCT) scans of the abdomen. In the previous literature, potentially missed MI in abdominal MDCTs was not perceived as an issue in radiology. This retrospective single-center study assessed the frequency of detectable myocardial hypoperfusion in contrast-enhanced abdominal MDCTs. We identified 107 patients between 2006 and 2022 who had abdominal MDCTs on the same day or the day before a catheter-proven or clinically evident diagnosis of MI. After reviewing the digital patient records and applying the exclusion criteria, we included 38 patients, with 19 showing areas of myocardial hypoperfusion. All MDCT studies were non ECG-gated. The delay between the MDCT examination and MI diagnosis was shorter in studies with myocardial hypoperfusion (7.4±6.5 hours and 13.8±12.5 hours) but not statistically significant p=0.054. Only 2 of 19 (11%) of these pathologies had been noted in the written radiology reports. The most common cardinal symptom was epigastric pain (50%), followed by polytrauma (21%). STEMI was significantly more common in cases of myocardial hypoperfusion p=0.009. Overall, 16 of 38 (42%) patients died because of acute MI. Based on extrapolations using local MDCT rates, we estimate several thousand radiologically missed MI cases worldwide per year

Combining targeted and systematic prostate biopsy improves prostate cancer detection and correlation with the whole mount histopathology in biopsy naïve and previous negative biopsy patients

OBJECTIVE: Guidelines for previous negative biopsy (PNB) cohorts with a suspicion of prostate cancer (PCa) after positive multiparametric (mp) magnetic-resonance-imaging (MRI) often favour the fusion-guided targeted prostate-biopsy (TB) only approach for Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions. However, recommendations lack direct biopsy performance comparison within biopsy naïve (BN) vs. PNB patients and its prognostication of the whole mount pathology report (WMPR), respectively. We suppose, that the combination of TB and concomitant TRUS-systematic biopsy (SB) improves the PCa detection rate of PI-RADS 2, 3, 4 or 5 lesions and the International Society of Urological Pathology (ISUP)-grade predictability of the WMPR in BN- and PNB patients. METHODS: Patients with suspicious mpMRI, elevated prostate-specific-antigen and/or abnormal digital rectal examination were included. All PI-RADS reports were intramurally reviewed for biopsy planning. We compared the PI-RADS score substratified TB, SB or combined approach (TB/SB) associated BN- and PNB-PCa detection rate. Furthermore, we assessed the ISUP-grade variability between biopsy cores and the WMPR. RESULTS: According to BN (n = 499) vs. PNB (n = 314) patients, clinically significant (cs) PCa was detected more frequently by the TB/SB approach (62 vs. 43%) than with the TB (54 vs. 34%) or SB (57 vs. 34%) (all p < 0.0001) alone. Furthermore, we observed that the TB/SB strategy detects a significantly higher number of csPCa within PI-RADS 3, 4 or 5 reports, both in BN and PNB men. In contrast, applied biopsy techniques were equally effective to detect csPCa within PI-RADS 2 lesions. In case of csPCa diagnosis the TB approach was more often false-negative in PNB patients (BN 11% vs. PNB 19%; p = 0.02). The TB/SB technique showed in general significantly less upgrading, whereas a higher agreement was only observed for the total and BN patient cohort. CONCLUSION: Despite csPCa is more frequently found in BN patients, the TB/SB method always detected a significantly higher number of csPCa within PI-RADS 3, 4 or 5 reports of our BN and PNB group. The TB/SB strategy predicts the ISUP-grade best in the total and BN cohort and in general shows the lowest upgrading rates, emphasizing its value not only in BN but also PNB patients

Thermometry of red blood cell concentrate: magnetic resonance decoding warm up process.

PURPOSE: Temperature is a key measure in human red blood cell concentrate (RBC) quality control. A precise description of transient temperature distributions in RBC units removed from steady storage exposed to ambient temperature is at present unknown. Magnetic resonance thermometry was employed to visualize and analyse RBC warm up processes, to describe time courses of RBC mean, surface and core temperatures by an analytical model, and to determine and investigate corresponding model parameters. METHODS: Warm-up processes of 47 RBC units stored at 1-6°C and exposed to 21.25°C ambient temperature were investigated by proton resonance frequency thermometry. Temperature distributions were visualized and analysed with dedicated software allowing derivation of RBC mean, surface and core temperature-time courses during warm up. Time-dependence of mean temperature was assumed to fulfil a lumped capacitive model of heat transfer. Time courses of relative surface and core temperature changes to ambient temperature were similarly assumed to follow shifted exponential decays characterized by a time constant and a relative time shift, respectively. RESULTS: The lumped capacitive model of heat transfer and shifted exponential decays described time-dependence of mean, surface and core temperatures close to perfect (mean R(2) were 0.999±0.001, 0.996±0.004 and 0.998±0.002, respectively). Mean time constants were τmean = 55.3±3.7 min, τsurface = 41.4±2.9 min and τcore = 76.8±7.1 min, mean relative time shifts were Δsurface = 0.07±0.02 and Δcore = 0.04±0.01. None of the constants correlated significantly with temperature differences between ambient and storage temperature. CONCLUSION: Lumped capacitive model of heat transfer and shifted exponential decays represent simple analytical formulas to describe transient mean, surface and core temperatures of RBC during warm up, which might be a helpful tool in RBC temperature monitoring and quality control. Independence of constants on differences between ambient and storage temperature suggests validity of models for arbitrary storage and ambient temperatures

Prediction of relative mean and core temperatures from relative surface temperature.

<p>Dependence of relative mean and core temperature differences <i>θ</i>_mean and <i>θ</i>_core on relative surface temperature differences <i>θ</i>_surface (solid lines) together with corresponding uncertainties (dotted lines) during warm up.</p

Experimental setup.

<p>(a) PRF calibration measurements were performed with RBC stored in a plastic cup. RBC temperature was derived from the calibration thermometer T (tip diameter = 5 mm) positioned at the center of RBC using a thermometer holder TH. A reference phantom RP was positioned in a plastic grid. To avoid thermal cooling of RP by RBC, the wall of the grid next to RBC was isolated by air cushion plastic. (b) RBC units were mounted in upright position in a two-layer plastic frame F (Lego®) with minimum distance of 1–2 mm. For investigation, RBC units were plugged to an adjustment unit AU (Lego®) fixed in a 12 channel head coil adapted with 2 reference phantoms RP kept at MR investigation room temperature <i>T</i>_ambient throughout experiments.</p

Time dependence of relative temperatur differences.

<p>Average time courses (solid lines) and corresponding uncertainties (dotted lines) of relative temperature differences of mean, surface and core temperature to ambient temperature of RBC during warm up. Recalculation of relative temperature difference scale to temperature scale at the right hand side was done (for convinience) for <i>T</i>_storage = 3.6°C and <i>T</i>_ambient = 21.25°C.</p

Determination of time courses of surface temperature and core temperature

<p>. For temperatures <i>T</i>₁, <i>T</i>₂, …, <i>T</i>_n time series of volume fractions <i>vol</i>(<i>T</i>₁), <i>vol</i>(<i>T</i>₂), …, <i>vol</i>(<i>T</i>_n) were derived from time-resolved temperature maps and interpolated by cubic splines. Times when tangents at half maximum of the respective volume fraction crosses 0 are interpreted as the times, when surface temperature <i>T</i>_surface = <i>T</i>₁, <i>T</i>₂, …, <i>T</i>_n, times when they cross 1 are interpreted as the times, when core temperature <i>T</i>_core = <i>T</i>₁, <i>T</i>₂, …, <i>T</i>_n.</p

Typical RBC warm up temperature distribution.

<p>Color encoded time-resolved temperature maps of RBC withdrawn from 3.6°C storage temperature exposed to 21.25°C ambient temperature. Corresponding mean, surface and core temperatures are indicated.</p

Influence of storage temperature on the prediction of mean and core temperatures from surface temperautre.

<p>Dependence of (a) mean temperature <i>T</i>_mean and (b) core temperature <i>T</i>_core on surface temperature <i>T</i>_surface (solid lines) together with corresponding uncertainties (dotted lines) for the “extreme” storage temperatures <i>T</i>_storage of 1°C and 6°C and ambient temperature <i>T</i>_ambient = 21.25°C. Mean values and standard deviations of respective quotients of time constants and shifts were used for calculations as these quantities did not depend significantly on storage temperature. Results for surface, mean and core temperatures of 10°C are emphasized (solid lines).</p