magnetic resonance imaging AND T1

MRI T1 weighted sequence short TR short TE definition

magnetic resonance imaging AND FLAIR

MRI T1 T2 FLAIR short TR TE CSF signal radiology reference

magnetic resonance imaging AND T2

T2 weighted MRI typical TR TE long TR long TE values

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MRI sequence parameters T1 T2 FLAIR DWI ADC STIR reference TR TE TI

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mkdir -p "/tmp/workspace/mri-sequence-reference/src" "/tmp/workspace/mri-sequence-reference/output" && python -c "import reportlab; print(reportlab.Version)"

python - <<'PY' from pathlib import Path p=Path('/tmp/workspace/mri-sequence-reference/src/create_mri_reference.py') p.write_text(r'''from reportlab.lib import colors from reportlab.lib.pagesizes import A4 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.enums import TA_CENTER, TA_LEFT from reportlab.lib.units import mm from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, KeepTogether from reportlab.pdfbase.pdfmetrics import stringWidth OUT = '/tmp/workspace/mri-sequence-reference/output/MRI_Sequence_Quick_Reference.pdf' W, H = A4 margin = 11*mm navy = colors.HexColor('#12324A') teal = colors.HexColor('#087E8B') light_teal = colors.HexColor('#E8F5F6') blue = colors.HexColor('#EAF2F8') light_gray = colors.HexColor('#F4F6F7') grid = colors.HexColor('#B9C7D1') ink = colors.HexColor('#17212B') muted = colors.HexColor('#4D5E6A') styles = getSampleStyleSheet() def ps(name, size, leading=None, color=ink, font='Helvetica', align=TA_LEFT, spaceAfter=0): return ParagraphStyle(name, parent=styles['Normal'], fontName=font, fontSize=size, leading=leading or size*1.2, textColor=color, alignment=align, spaceAfter=spaceAfter) title = ps('Title', 18, 21, navy, 'Helvetica-Bold', TA_CENTER) subtitle = ps('Sub', 8.2, 10, muted, 'Helvetica', TA_CENTER) section = ps('Section', 9.2, 11, colors.white, 'Helvetica-Bold') header = ps('Header', 7.15, 8.5, colors.white, 'Helvetica-Bold', TA_CENTER) cell = ps('Cell', 6.85, 8.2, ink) cell_center = ps('CellC', 6.85, 8.2, ink, 'Helvetica', TA_CENTER) small = ps('Small', 7.0, 8.5, muted) key = ps('Key', 7.0, 8.5, ink) footer = ps('Foot', 6.2, 7.5, muted, 'Helvetica', TA_CENTER) def P(text, st=cell): return Paragraph(text, st) def section_bar(label): t=Table([[P(label, section)]], colWidths=[W-2*margin], rowHeights=[5.7*mm]) t.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),navy),('LEFTPADDING',(0,0),(-1,-1),3.2*mm),('RIGHTPADDING',(0,0),(-1,-1),3*mm),('TOPPADDING',(0,0),(-1,-1),1.3*mm),('BOTTOMPADDING',(0,0),(-1,-1),1.2*mm)])) return t doc=SimpleDocTemplate(OUT, pagesize=A4, leftMargin=margin, rightMargin=margin, topMargin=9*mm, bottomMargin=8*mm) story=[] story += [Paragraph('MRI SEQUENCE QUICK REFERENCE', title), Spacer(1, 1.1*mm), Paragraph('Typical adult diagnostic MRI values. Protocols vary by scanner, field strength, anatomy, vendor, and clinical question.', subtitle), Spacer(1, 3.0*mm)] story.append(section_bar('CORE SEQUENCES')) story.append(Spacer(1,1.4*mm)) cols=[23*mm, 30*mm, 28*mm, 39*mm, 35*mm, 26*mm] data=[ [P('Sequence',header),P('Typical parameters',header),P('Signal pattern',header),P('Main use',header),P('High-yield findings',header),P('Fast cue',header)], [P('<b>T1W</b>\nspin echo / GRE'),P('Short TR + short TE\nSE: TR 400-800 ms\nTE <30 ms'),P('Fat bright\nCSF dark\nWM > GM'),P('Anatomy, marrow/fat, pre- and post-gadolinium imaging'),P('Subacute methemoglobin can be bright; enhancing lesions brighten after contrast'),P('<b>“T1 = fat”</b>')], [P('<b>T2W</b>\nusually FSE'),P('Long TR + long TE\nTR >2000 ms\nTE >80 ms\n(FSE often TE >60 ms)'),P('Water/CSF bright\nGM > WM'),P('Edema, fluid, inflammation, general lesion detection'),P('Edema, cysts, many tumors and demyelinating lesions are bright'),P('<b>“T2 = water”</b>')], [P('<b>T2-FLAIR</b>\nfluid-attenuated IR'),P('Long TR + long TE\nTI about 1700-2500 ms\n(1.5 T range)'),P('CSF suppressed dark\nEdema/gliosis bright'),P('Brain lesions adjacent to CSF'),P('MS plaques, gliosis, infarct-related edema, cortical/periventricular lesions'),P('<b>“T2 + dark CSF”</b>')], [P('<b>DWI</b>\nwith ADC map'),P('Diffusion sensitization\nCommon b: 0 and 1000 s/mm²\nNo single TR/TE rule'),P('Restricted diffusion:\nDWI bright + ADC low'),P('Acute ischemic stroke; cellular lesions; pus'),P('Acute infarct, abscess, epidermoid, some hypercellular tumors'),P('<b>“Bright DWI, dark ADC”</b>')], [P('<b>ADC</b>\nquantitative diffusion map'),P('Derived from DWI\nValues in ×10⁻³ mm²/s'),P('Restriction is dark\nFacilitated diffusion bright'),P('Confirm true restriction and avoid T2 shine-through'),P('Cytotoxic edema has reduced ADC in acute infarction'),P('<b>“ADC confirms”</b>')], [P('<b>STIR</b>\nshort-TI IR'),P('Long TR + long TE\nTI about 120-170 ms\n(1.5 T)'),P('Fat suppressed dark\nFluid/edema bright'),P('MSK marrow edema, soft-tissue edema, tumor screening'),P('Marrow edema, occult fracture, inflammatory change'),P('<b>“STIR suppresses fat”</b>')], [P('<b>GRE / SWI</b>\nsusceptibility-sensitive'),P('Gradient echo\nTE tailored to susceptibility\nSWI commonly long TE'),P('Blood products/iron\nare dark with blooming'),P('Hemorrhage, microbleeds, calcification/venous structures'),P('Microhemorrhages, cavernoma, hemosiderin'),P('<b>“Blooming”</b>')], ] t=Table(data, colWidths=cols, repeatRows=1) t.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,0),teal),('GRID',(0,0),(-1,-1),0.32,grid),('VALIGN',(0,0),(-1,-1),'TOP'), ('BACKGROUND',(0,1),(-1,1),light_teal),('BACKGROUND',(0,3),(-1,3),light_teal),('BACKGROUND',(0,5),(-1,5),light_teal),('BACKGROUND',(0,7),(-1,7),light_teal), ('LEFTPADDING',(0,0),(-1,-1),1.8*mm),('RIGHTPADDING',(0,0),(-1,-1),1.8*mm),('TOPPADDING',(0,0),(-1,-1),1.35*mm),('BOTTOMPADDING',(0,0),(-1,-1),1.35*mm), ])) story.append(t) story += [Spacer(1,3.2*mm), section_bar('AT-A-GLANCE: BRAIN SIGNAL & INTERPRETATION'), Spacer(1,1.4*mm)] brain_data=[ [P('Structure / finding',header),P('T1W',header),P('T2W',header),P('FLAIR',header),P('Interpretation pearl',header)], [P('CSF / free water'),P('Dark',cell_center),P('Bright',cell_center),P('Dark',cell_center),P('FLAIR removes bright CSF so nearby lesions stand out.')], [P('White matter vs gray matter'),P('WM brighter',cell_center),P('GM brighter',cell_center),P('GM slightly brighter',cell_center),P('Use T1W for anatomic detail; T2W/FLAIR for water-rich pathology.')], [P('Edema, gliosis, demyelination'),P('Often low/gray signal',cell_center),P('Bright',cell_center),P('Bright',cell_center),P('FLAIR is especially useful for periventricular and juxtacortical lesions.')], [P('Fat'),P('Bright',cell_center),P('Variable, often bright on FSE',cell_center),P('Variable',cell_center),P('Use fat-saturation or STIR when fat signal obscures pathology.')], [P('Air, cortical bone, dense calcification'),P('Dark',cell_center),P('Dark',cell_center),P('Dark',cell_center),P('Signal voids. CT is often better for calcification and bone detail.')], ] t2=Table(brain_data, colWidths=[44*mm,25*mm,25*mm,25*mm,87*mm]) t2.setStyle(TableStyle([ ('BACKGROUND',(0,0),(-1,0),teal),('GRID',(0,0),(-1,-1),0.32,grid),('VALIGN',(0,0),(-1,-1),'MIDDLE'), ('BACKGROUND',(0,1),(-1,1),blue),('BACKGROUND',(0,3),(-1,3),blue),('BACKGROUND',(0,5),(-1,5),blue), ('LEFTPADDING',(0,0),(-1,-1),1.8*mm),('RIGHTPADDING',(0,0),(-1,-1),1.8*mm),('TOPPADDING',(0,0),(-1,-1),1.25*mm),('BOTTOMPADDING',(0,0),(-1,-1),1.25*mm), ])) story.append(t2) story += [Spacer(1,3.0*mm)] notes=[ P('<b>Parameter basics:</b> TR controls T1 weighting; TE controls T2 weighting; TI selects the tissue signal to null in inversion-recovery sequences.', key), P('<b>Safety note:</b> STIR is not used to assess post-gadolinium enhancement because it suppresses tissues by T1 rather than selectively suppressing fat.', key), P('<b>Abbreviations:</b> TR = repetition time; TE = echo time; TI = inversion time; FSE = fast spin echo; IR = inversion recovery; WM = white matter; GM = gray matter.', key), ] nt=Table([[n] for n in notes], colWidths=[W-2*margin]) nt.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),light_gray),('BOX',(0,0),(-1,-1),0.35,grid),('INNERGRID',(0,0),(-1,-1),0.25,grid),('LEFTPADDING',(0,0),(-1,-1),2.5*mm),('RIGHTPADDING',(0,0),(-1,-1),2.5*mm),('TOPPADDING',(0,0),(-1,-1),1.25*mm),('BOTTOMPADDING',(0,0),(-1,-1),1.25*mm)])) story.append(nt) story += [Spacer(1,2.3*mm), Paragraph('Educational quick reference only. Sequence parameters are approximate and must be adapted to the local protocol. Sources: Neuroanatomy through Clinical Cases, Table 4.3; Grainger & Allison’s Diagnostic Radiology; Radiopaedia MRI sequence parameters.', footer)] doc.build(story) print(OUT) ''') PY python "/tmp/workspace/mri-sequence-reference/src/create_mri_reference.py" python - <<'PY' from pypdf import PdfReader p='/tmp/workspace/mri-sequence-reference/output/MRI_Sequence_Quick_Reference.pdf' r=PdfReader(p) print('pages',len(r.pages),'bytes',__import__('os').path.getsize(p)) print(r.pages[0].extract_text()[:1000]) PY

python - <<'PY' from pypdf import PdfReader from pathlib import Path p=Path('/tmp/workspace/mri-sequence-reference/output/MRI_Sequence_Quick_Reference.pdf') r=PdfReader(str(p)) assert len(r.pages)==1 text=r.pages[0].extract_text() for term in ['T1W','T2W','T2-FLAIR','DWI','STIR','GRE / SWI','AT-A-GLANCE']: assert term in text, term print(f'PASS: {p.name} | pages={len(r.pages)} | size={p.stat().st_size} bytes | key content present') PY